The document discusses several topics:
1. A new version of the Garp3 modeling software was released, including improvements to model sharing, sketching, simulation control, and help pages.
2. Issues that modelers encounter when developing qualitative models are described, along with work to automatically detect and solve such issues.
3. An example is given of a modeling issue where a recruitment rate quantity generated inconsistencies during simulation due to being associated with a river rather than a population stage.
4. Work on a qualitative model exploring sustainability in the Brazilian Riacho Fundo water basin is mentioned, which will be presented to stakeholders for evaluation to support understanding and decision-making about water resources management.
GeoAI: A Model-Agnostic Meta-Ensemble Zero-Shot Learning Method for Hyperspec...Konstantinos Demertzis
Deep learning architectures are the most e
ective methods for analyzing and classifying Ultra-Spectral Images (USI). However, e ective training of a Deep Learning (DL) gradient classifier aiming to achieve high classification accuracy, is extremely costly and time-consuming. It requires huge datasets with hundreds or thousands of labeled specimens from expert scientists. This research exploits the MAML++ algorithm in order to introduce the Model-Agnostic Meta-Ensemble Zero-shot Learning (MAME-ZsL) approach. The MAME-ZsL overcomes the above diculties, and it can be used as a powerful model to perform Hyperspectral Image Analysis (HIA). It is a novel optimization-based Meta-Ensemble Learning architecture, following a Zero-shot Learning (ZsL) prototype. To the best of our knowledge it is introduced to the literature for the first time. It facilitates learning of specialized techniques for the extraction of user-mediated representations, in complex Deep Learning architectures. Moreover, it leverages the use of first and second-order derivatives as pre-training methods. It enhances learning of features which do not cause issues of exploding or diminishing gradients; thus, it avoids potential overfitting. Moreover, it significantly reduces computational cost and training time, and it oers an improved training stability, high generalization performance and remarkable classification accuracy.
A consistent and efficient graphical User Interface Design and Querying Organ...CSCJournals
We propose a software layer called GUEDOS-DB upon Object-Relational Database Management System ORDMS. In this work we apply it in Molecular Biology, more precisely Organelle complete genome. We aim to offer biologists the possibility to access in a unified way information spread among heterogeneous genome databanks. In this paper, the goal is firstly, to provide a visual schema graph through a number of illustrative examples. The adopted, human-computer interaction technique in this visual designing and querying makes very easy for biologists to formulate database queries compared with linear textual query representation.
Feature selection in high-dimensional datasets is
considered to be a complex and time-consuming problem. To
enhance the accuracy of classification and reduce the execution
time, Parallel Evolutionary Algorithms (PEAs) can be used. In
this paper, we make a review for the most recent works which
handle the use of PEAs for feature selection in large datasets.
We have classified the algorithms in these papers into four main
classes (Genetic Algorithms (GA), Particle Swarm Optimization
(PSO), Scattered Search (SS), and Ant Colony Optimization
(ACO)). The accuracy is adopted as a measure to compare the
efficiency of these PEAs. It is noticeable that the Parallel Genetic
Algorithms (PGAs) are the most suitable algorithms for feature
selection in large datasets; since they achieve the highest accuracy.
On the other hand, we found that the Parallel ACO is timeconsuming
and less accurate comparing with other PEA.
Survey on evolutionary computation tech techniques and its application in dif...ijitjournal
In computer science, 'evolutionary computation' is an algorithmic tool based on evolution. It implements
random variation, reproduction and selection by altering and moving data within a computer. It helps in
building, applying and studying algorithms based on the Darwinian principles of natural selection. In this
paper, studies about different evolutionary computation techniques used in some applications specifically
image processing, cloud computing and grid computing is carried out briefly. This work is an effort to help
researchers from different fields to have knowledge on the techniques of evolutionary computation
applicable in the above mentioned areas.
GeoAI: A Model-Agnostic Meta-Ensemble Zero-Shot Learning Method for Hyperspec...Konstantinos Demertzis
Deep learning architectures are the most e
ective methods for analyzing and classifying Ultra-Spectral Images (USI). However, e ective training of a Deep Learning (DL) gradient classifier aiming to achieve high classification accuracy, is extremely costly and time-consuming. It requires huge datasets with hundreds or thousands of labeled specimens from expert scientists. This research exploits the MAML++ algorithm in order to introduce the Model-Agnostic Meta-Ensemble Zero-shot Learning (MAME-ZsL) approach. The MAME-ZsL overcomes the above diculties, and it can be used as a powerful model to perform Hyperspectral Image Analysis (HIA). It is a novel optimization-based Meta-Ensemble Learning architecture, following a Zero-shot Learning (ZsL) prototype. To the best of our knowledge it is introduced to the literature for the first time. It facilitates learning of specialized techniques for the extraction of user-mediated representations, in complex Deep Learning architectures. Moreover, it leverages the use of first and second-order derivatives as pre-training methods. It enhances learning of features which do not cause issues of exploding or diminishing gradients; thus, it avoids potential overfitting. Moreover, it significantly reduces computational cost and training time, and it oers an improved training stability, high generalization performance and remarkable classification accuracy.
A consistent and efficient graphical User Interface Design and Querying Organ...CSCJournals
We propose a software layer called GUEDOS-DB upon Object-Relational Database Management System ORDMS. In this work we apply it in Molecular Biology, more precisely Organelle complete genome. We aim to offer biologists the possibility to access in a unified way information spread among heterogeneous genome databanks. In this paper, the goal is firstly, to provide a visual schema graph through a number of illustrative examples. The adopted, human-computer interaction technique in this visual designing and querying makes very easy for biologists to formulate database queries compared with linear textual query representation.
Feature selection in high-dimensional datasets is
considered to be a complex and time-consuming problem. To
enhance the accuracy of classification and reduce the execution
time, Parallel Evolutionary Algorithms (PEAs) can be used. In
this paper, we make a review for the most recent works which
handle the use of PEAs for feature selection in large datasets.
We have classified the algorithms in these papers into four main
classes (Genetic Algorithms (GA), Particle Swarm Optimization
(PSO), Scattered Search (SS), and Ant Colony Optimization
(ACO)). The accuracy is adopted as a measure to compare the
efficiency of these PEAs. It is noticeable that the Parallel Genetic
Algorithms (PGAs) are the most suitable algorithms for feature
selection in large datasets; since they achieve the highest accuracy.
On the other hand, we found that the Parallel ACO is timeconsuming
and less accurate comparing with other PEA.
Survey on evolutionary computation tech techniques and its application in dif...ijitjournal
In computer science, 'evolutionary computation' is an algorithmic tool based on evolution. It implements
random variation, reproduction and selection by altering and moving data within a computer. It helps in
building, applying and studying algorithms based on the Darwinian principles of natural selection. In this
paper, studies about different evolutionary computation techniques used in some applications specifically
image processing, cloud computing and grid computing is carried out briefly. This work is an effort to help
researchers from different fields to have knowledge on the techniques of evolutionary computation
applicable in the above mentioned areas.
This is a presentation of the JGrass-newAGE system held in Potenza on February 24 20117. It contains an overview of concepts, ideas, behing JGrass-NewAGE ans shows some achievements in a critical manner.
Almost the same as the talk given to Ph.D. students one year ago. It covers the problem of research reproducibility and the tools for doing it. First comes some "theoretical" arguments, then the enumeration of some tools.
Novel Class Detection Using RBF SVM Kernel from Feature Evolving Data Streamsirjes
In the data mining field the classification of data stream creates many problems. The challenges
faces in the data stream are infinite length, concept drift, concept evaluation and feature evolution. Most of the
existing system focuses on the only first two challenges. We propose a framework in which each classifier is
prepared with the novel class detector for addressing the two challenges concept drift and concept evaluation
and for addressing the feature evolution feature set homogeneous technique is proposed. We improved the
novel class detection module by building it more adaptive to evolving the stream. SVM based feature extraction
for RBF kernel method is also proposed for detecting the novel class from the steaming data. By using the
concept of permutation and combination RBF kernel extracts the features and find out the relation between
them. This improves the novel class detect technique and provide more accuracy for classifying the data
Design , Analysis And Manufacturing of Garbage compactor - a Reviewijiert bestjournal
The collection of waste is vital work that ensures our communities remain pleasant environments in which to live. But major problem we are facing today is transportation of the waste which can be reduced by compacting or reducing size of particular waste. A compactor can be used to reduce the volume of waste streams. The waste weight will remain the same so there will be no savings from the total amount of waste produced. However,savings will occur because waste volume will be reduced by approximately 80% which will decr ease the number of times the dumpster will need to be emptied,therefore resulting in lower pick up fees..
WITH SEMANTICS AND HIDDEN MARKOV MODELS TO AN ADAPTIVE LOG FILE PARSERijnlc
We aim to model an adaptive log file parser. As the content of log files often evolves over time, we established a dynamic statistical model which learns and adapts processing and parsing rules. First, we limit the amount of unstructured text by clustering based on semantics of log file lines. Next, we only take the most relevant cluster into account and focus only on those frequent patterns which lead to the desired output table similar to Vaarandi [10]. Furthermore, we transform the found frequent patterns and the output stating the parsed table into a Hidden Markov Model (HMM). We use this HMM as a specific, however, flexible representation of a pattern for log file parsing to maintain high quality output. After training our model on one system type and applying it to a different system with slightly different log file patterns, we achieve an accuracy over 99.99%
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
WITH SEMANTICS AND HIDDEN MARKOV MODELS TO AN ADAPTIVE LOG FILE PARSERkevig
We aim to model an adaptive log file parser. As the content of log files often evolves over time, we
established a dynamic statistical model which learns and adapts processing and parsing rules. First, we
limit the amount of unstructured text by clustering based on semantics of log file lines. Next, we only take
the most relevant cluster into account and focus only on those frequent patterns which lead to the desired
output table similar to Vaarandi [10]. Furthermore, we transform the found frequent patterns and the
output stating the parsed table into a Hidden Markov Model (HMM). We use this HMM as a specific,
however, flexible representation of a pattern for log file parsing to maintain high quality output. After
training our model on one system type and applying it to a different system with slightly different log file
patterns, we achieve an accuracy over 99.99%.
Ijricit 01-002 enhanced replica detection in short time for large data setsIjripublishers Ijri
Similarity check of real world entities is a necessary factor in these days which is named as Data Replica Detection.
Time is an critical factor today in tracking Data Replica Detection for large data sets, without having impact over quality
of Dataset. In this we primarily introduce two Data Replica Detection algorithms , where in these contribute enhanced
procedural standards in finding Data Replication at limited execution periods.This contribute better improvised state
of time than conventional techniques . We propose two Data Replica Detection algorithms namely progressive sorted
neighborhood method (PSNM), which performs best on small and almost clean datasets, and progressive blocking (PB),
which performs best on large and very grimy datasets. Both enhance the efficiency of duplicate detection even on very
large datasets.
Performance Comparision of Machine Learning AlgorithmsDinusha Dilanka
In this paper Compare the performance of two
classification algorithm. I t is useful to differentiate
algorithms based on computational performance rather
than classification accuracy alone. As although
classification accuracy between the algorithms is similar,
computational performance can differ significantly and it
can affect to the final results. So the objective of this paper
is to perform a comparative analysis of two machine
learning algorithms namely, K Nearest neighbor,
classification and Logistic Regression. In this paper it
was considered a large dataset of 7981 data points and 112
features. Then the performance of the above mentioned
machine learning algorithms are examined. In this paper
the processing time and accuracy of the different machine
learning techniques are being estimated by considering the
collected data set, over a 60% for train and remaining
40% for testing. The paper is organized as follows. In
Section I, introduction and background analysis of the
research is included and in section II, problem statement.
In Section III, our application and data analyze Process,
the testing environment, and the Methodology of our
analysis are being described briefly. Section IV comprises
the results of two algorithms. Finally, the paper concludes
with a discussion of future directions for research by
eliminating the problems existing with the current
research methodology.
A HYBRID LEARNING ALGORITHM IN AUTOMATED TEXT CATEGORIZATION OF LEGACY DATAijaia
The goal of this research is to develop an algorithm to automatically classify measurement types from NASA’s airborne measurement data archive. The product has to meet specific metrics in term of accuracy, robustness and usability, as the initial decision-tree based development has shown limited applicability due to its resource intensive characteristics. We have developed an innovative solution that is much more efficient while offering comparable performance. Similar to many industrial applications, the data available are noisy and correlated; and there is a wide range of features that are associated with the type of measurement to be identified. The proposed algorithm uses a decision tree to select features and determine their weights.A weighted Naive Bayes is used due to the presence of highly correlated inputs. The development has been successfully deployed in an industrial scale, and the results show that the development is well-balanced in term of performance and resource requirements.
A HYBRID LEARNING ALGORITHM IN AUTOMATED TEXT CATEGORIZATION OF LEGACY DATAgerogepatton
The goal of this research is to develop an algorithm to automatically classify measurement types from NASA’s airborne measurement data archive. The product has to meet specific metrics in term of accuracy, robustness and usability, as the initial decision-tree based development has shown limited applicability due to its resource intensive characteristics. We have developed an innovative solution that is much more efficient while offering comparable performance. Similar to many industrial applications, the data available are noisy and correlated; and there is a wide range of features that are associated with the type of measurement to be identified. The proposed algorithm uses a decision tree to select features and determine their weights.A weighted Naive Bayes is used due to the presence of highly correlated inputs. The development has been successfully deployed in an industrial scale, and the results show that the development is well-balanced in term of performance and resource requirements.
Multilinear Kernel Mapping for Feature Dimension Reduction in Content Based M...ijma
In the process of content-based multimedia retrieval, multimedia information is processed in order to
obtain descriptive features. Descriptive representation of features, results in a huge feature count, which
results in processing overhead. To reduce this descriptive feature overhead, various approaches have been
used to dimensional reduction, among which PCA and LDA are the most used methods. However, these
methods do not reflect the significance of feature content in terms of inter-relation among all dataset
features. To achieve a dimension reduction based on histogram transformation, features with low
significance can be eliminated. In this paper, we propose a feature dimensional reduction approaches to
achieve the dimension reduction approach based on a multi-linear kernel (MLK) modeling. A benchmark
dataset for the experimental work is taken and the proposed work is observed to be improved in analysis in
comparison to the conventional system.
Software aging prediction – a new approach IJECEIAES
To meet the users’ requirements which are very diverse in recent days, computing infrastructure has become complex. An example of one such infrastructure is a cloud-based system. These systems suffer from resource exhaustion in the long run which leads to performance degradation. This phenomenon is called software aging. There is a need to predict software aging to carry out pre-emptive rejuvenation that enhances service availability. Software rejuvenation is the technique that refreshes the system and brings it back to a healthy state. Hence, software aging should be predicted in advance to trigger the rejuvenation process to improve service availability. In this work, the k-nearest neighbor (k-NN) algorithm-based new approach has been used to identify the virtual machine's status, and a prediction of resource exhaustion time has been made. The proposed prediction model uses static thresholding and adaptive thresholding methods. The performance of the algorithms is compared, and it is found that for classification, the k-NN performs comparatively better, i.e., k-NN showed an accuracy of 97.6. In contrast, its counterparts performed with an accuracy of 96.0 (naïve Bayes) and 92.8 (decision tree). The comparison of the proposed work with previous similar works has also been discussed.
This is a presentation of the JGrass-newAGE system held in Potenza on February 24 20117. It contains an overview of concepts, ideas, behing JGrass-NewAGE ans shows some achievements in a critical manner.
Almost the same as the talk given to Ph.D. students one year ago. It covers the problem of research reproducibility and the tools for doing it. First comes some "theoretical" arguments, then the enumeration of some tools.
Novel Class Detection Using RBF SVM Kernel from Feature Evolving Data Streamsirjes
In the data mining field the classification of data stream creates many problems. The challenges
faces in the data stream are infinite length, concept drift, concept evaluation and feature evolution. Most of the
existing system focuses on the only first two challenges. We propose a framework in which each classifier is
prepared with the novel class detector for addressing the two challenges concept drift and concept evaluation
and for addressing the feature evolution feature set homogeneous technique is proposed. We improved the
novel class detection module by building it more adaptive to evolving the stream. SVM based feature extraction
for RBF kernel method is also proposed for detecting the novel class from the steaming data. By using the
concept of permutation and combination RBF kernel extracts the features and find out the relation between
them. This improves the novel class detect technique and provide more accuracy for classifying the data
Design , Analysis And Manufacturing of Garbage compactor - a Reviewijiert bestjournal
The collection of waste is vital work that ensures our communities remain pleasant environments in which to live. But major problem we are facing today is transportation of the waste which can be reduced by compacting or reducing size of particular waste. A compactor can be used to reduce the volume of waste streams. The waste weight will remain the same so there will be no savings from the total amount of waste produced. However,savings will occur because waste volume will be reduced by approximately 80% which will decr ease the number of times the dumpster will need to be emptied,therefore resulting in lower pick up fees..
WITH SEMANTICS AND HIDDEN MARKOV MODELS TO AN ADAPTIVE LOG FILE PARSERijnlc
We aim to model an adaptive log file parser. As the content of log files often evolves over time, we established a dynamic statistical model which learns and adapts processing and parsing rules. First, we limit the amount of unstructured text by clustering based on semantics of log file lines. Next, we only take the most relevant cluster into account and focus only on those frequent patterns which lead to the desired output table similar to Vaarandi [10]. Furthermore, we transform the found frequent patterns and the output stating the parsed table into a Hidden Markov Model (HMM). We use this HMM as a specific, however, flexible representation of a pattern for log file parsing to maintain high quality output. After training our model on one system type and applying it to a different system with slightly different log file patterns, we achieve an accuracy over 99.99%
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
WITH SEMANTICS AND HIDDEN MARKOV MODELS TO AN ADAPTIVE LOG FILE PARSERkevig
We aim to model an adaptive log file parser. As the content of log files often evolves over time, we
established a dynamic statistical model which learns and adapts processing and parsing rules. First, we
limit the amount of unstructured text by clustering based on semantics of log file lines. Next, we only take
the most relevant cluster into account and focus only on those frequent patterns which lead to the desired
output table similar to Vaarandi [10]. Furthermore, we transform the found frequent patterns and the
output stating the parsed table into a Hidden Markov Model (HMM). We use this HMM as a specific,
however, flexible representation of a pattern for log file parsing to maintain high quality output. After
training our model on one system type and applying it to a different system with slightly different log file
patterns, we achieve an accuracy over 99.99%.
Ijricit 01-002 enhanced replica detection in short time for large data setsIjripublishers Ijri
Similarity check of real world entities is a necessary factor in these days which is named as Data Replica Detection.
Time is an critical factor today in tracking Data Replica Detection for large data sets, without having impact over quality
of Dataset. In this we primarily introduce two Data Replica Detection algorithms , where in these contribute enhanced
procedural standards in finding Data Replication at limited execution periods.This contribute better improvised state
of time than conventional techniques . We propose two Data Replica Detection algorithms namely progressive sorted
neighborhood method (PSNM), which performs best on small and almost clean datasets, and progressive blocking (PB),
which performs best on large and very grimy datasets. Both enhance the efficiency of duplicate detection even on very
large datasets.
Performance Comparision of Machine Learning AlgorithmsDinusha Dilanka
In this paper Compare the performance of two
classification algorithm. I t is useful to differentiate
algorithms based on computational performance rather
than classification accuracy alone. As although
classification accuracy between the algorithms is similar,
computational performance can differ significantly and it
can affect to the final results. So the objective of this paper
is to perform a comparative analysis of two machine
learning algorithms namely, K Nearest neighbor,
classification and Logistic Regression. In this paper it
was considered a large dataset of 7981 data points and 112
features. Then the performance of the above mentioned
machine learning algorithms are examined. In this paper
the processing time and accuracy of the different machine
learning techniques are being estimated by considering the
collected data set, over a 60% for train and remaining
40% for testing. The paper is organized as follows. In
Section I, introduction and background analysis of the
research is included and in section II, problem statement.
In Section III, our application and data analyze Process,
the testing environment, and the Methodology of our
analysis are being described briefly. Section IV comprises
the results of two algorithms. Finally, the paper concludes
with a discussion of future directions for research by
eliminating the problems existing with the current
research methodology.
A HYBRID LEARNING ALGORITHM IN AUTOMATED TEXT CATEGORIZATION OF LEGACY DATAijaia
The goal of this research is to develop an algorithm to automatically classify measurement types from NASA’s airborne measurement data archive. The product has to meet specific metrics in term of accuracy, robustness and usability, as the initial decision-tree based development has shown limited applicability due to its resource intensive characteristics. We have developed an innovative solution that is much more efficient while offering comparable performance. Similar to many industrial applications, the data available are noisy and correlated; and there is a wide range of features that are associated with the type of measurement to be identified. The proposed algorithm uses a decision tree to select features and determine their weights.A weighted Naive Bayes is used due to the presence of highly correlated inputs. The development has been successfully deployed in an industrial scale, and the results show that the development is well-balanced in term of performance and resource requirements.
A HYBRID LEARNING ALGORITHM IN AUTOMATED TEXT CATEGORIZATION OF LEGACY DATAgerogepatton
The goal of this research is to develop an algorithm to automatically classify measurement types from NASA’s airborne measurement data archive. The product has to meet specific metrics in term of accuracy, robustness and usability, as the initial decision-tree based development has shown limited applicability due to its resource intensive characteristics. We have developed an innovative solution that is much more efficient while offering comparable performance. Similar to many industrial applications, the data available are noisy and correlated; and there is a wide range of features that are associated with the type of measurement to be identified. The proposed algorithm uses a decision tree to select features and determine their weights.A weighted Naive Bayes is used due to the presence of highly correlated inputs. The development has been successfully deployed in an industrial scale, and the results show that the development is well-balanced in term of performance and resource requirements.
Multilinear Kernel Mapping for Feature Dimension Reduction in Content Based M...ijma
In the process of content-based multimedia retrieval, multimedia information is processed in order to
obtain descriptive features. Descriptive representation of features, results in a huge feature count, which
results in processing overhead. To reduce this descriptive feature overhead, various approaches have been
used to dimensional reduction, among which PCA and LDA are the most used methods. However, these
methods do not reflect the significance of feature content in terms of inter-relation among all dataset
features. To achieve a dimension reduction based on histogram transformation, features with low
significance can be eliminated. In this paper, we propose a feature dimensional reduction approaches to
achieve the dimension reduction approach based on a multi-linear kernel (MLK) modeling. A benchmark
dataset for the experimental work is taken and the proposed work is observed to be improved in analysis in
comparison to the conventional system.
Software aging prediction – a new approach IJECEIAES
To meet the users’ requirements which are very diverse in recent days, computing infrastructure has become complex. An example of one such infrastructure is a cloud-based system. These systems suffer from resource exhaustion in the long run which leads to performance degradation. This phenomenon is called software aging. There is a need to predict software aging to carry out pre-emptive rejuvenation that enhances service availability. Software rejuvenation is the technique that refreshes the system and brings it back to a healthy state. Hence, software aging should be predicted in advance to trigger the rejuvenation process to improve service availability. In this work, the k-nearest neighbor (k-NN) algorithm-based new approach has been used to identify the virtual machine's status, and a prediction of resource exhaustion time has been made. The proposed prediction model uses static thresholding and adaptive thresholding methods. The performance of the algorithms is compared, and it is found that for classification, the k-NN performs comparatively better, i.e., k-NN showed an accuracy of 97.6. In contrast, its counterparts performed with an accuracy of 96.0 (naïve Bayes) and 92.8 (decision tree). The comparison of the proposed work with previous similar works has also been discussed.
1. Naturnet - Redime
NEWSLETTER
No. 5 April 2007
Content
New Release of the Garp3 Collaborative Danube Delta Biosphere Reserve (DDBR) is
Workbench one of the five case studies considered in the FP6
NaturNet-Redime project to develop Qualitative
Anders Bouwer, Jochem Liem, Floris Linnebank
Reasoning (QR) models…
and Bert Bredeweg (University of Amsterodam)
Page 7
A new version (1.3.8) of the Garp3 Collaborative
Workbench has been released for general use, as of Danube Delta Biosphere Reserve Case Study
April 18th, 2007. Garp3 is a tool for Qualitative Qualitative Reasoning Model - an Environmental
Reasoning and Modelling that supports domain experts System Sustainable Development Decision Making
and novices in building conceptual models of system Support
behaviour … Eugenia Cioaca (Danube Delta National Institute
Page 2 Tulcea, Romania), Bert Bredeweg (University of
Amsterdam), Paulo Salles (University of Brasilia)
Troubleshooting Qualitative Models
Qualitative Reasoning (QR) models may serve
Jochem Liem and Bert Bredeweg (University of
as mediators in decision making process on
Amsterodam)
Sustainable Development. For that purpose, a QR
This paper describes the issues modellers model can be said to capture (to document) an
encounter when developing qualitative models, and explanation of how a system behaves...
initial work started in the NaturNet-Redime (NNR) Page 9
project to automatically detect and solve these issues.
Ecologists working on different case studies in the Curriculum for Learning about Sustainable
NNR project are developing qualitative models in which Development Using Qualitative Reasoning
they capture their knowledge about how river ecology Tim Nuttle (University of Jena, Germany), Paulo
relates to sustainable development... Salles (University of Brasilia, Brazil), Bert
Page 4 Bredeweg (University of Amsterdam, The
Netherlands), Eugenia Cioaca (Danube Delta
A qualitative model about sustainability in the
National Institute, Romania), Elena Nakova (Central
Brazilian Riacho Fundo water basin
Laboratory of General Ecology, Bulgaria), Richard
Paulo Salles (University of Brasilia) Noble (University of Hull, England), Andreas Zitek
(Vienna University of Applied Life Sciences,
A qualitative model exploring sustainability
Austria).
issues in the Riacho Fundo water basin (Brasilia,
Brazil) was developed by the Naturnet – Redime Work Package 6 partners have been hard at
project and in the next months this model will be work producing Qualitative Reasoning (QR) models
presented to stakeholders for evaluation. The model and simulations of sustainability issues pertaining to
goal is to support stakeholders in understanding and case studies in different river catchment areas...
decision-making about water resources management...
Page 6 Page 13
Qualitative Reasoning Model – the modelled Events of interest
system functioning study: Danube Delta Biosphere
Reserve case study Page 15
Eugenia CIOACA (Danube Delta National Institute Contact
Tulcea, Romania), Bert BREDEWEG (University of Page 16
Amsterdam), Paulo SALLES (University of Brasilia),
Tim Nuttle (University of Jena, Germany)
České centrum pro vědu a společnost, Radlická 28/263, 150 00 Praha 5, Czech Republic
www.naturnet.org www.ccss.cz ISSN 1801-6480
<META NAME="DC.Identifier" CONTENT="(SCHEME=ISSN) 18016480">
2. New Release of the Garp3 Collaborative Workbench
Anders Bouwer, Jochem Liem, Floris Linnebank and Bert Bredeweg
(University of Amsterodam)
A new version (1.3.8) of the Garp3 model fragments in the target model wherever
Collaborative Workbench has been released for possible, instead of creating extra copies of each
general use, as of April 18th, 2007. Garp3 is a tool for conditional model fragment. It is also possible to copy
Qualitative Reasoning and Modelling that supports more complex modeling concepts (i.e., refinements)
domain experts and novices in building conceptual than was previously possible.
models of system behaviour [1]. A model in Garp3
consists of model fragments (specifying situations or
processes) and scenarios, which can be simulated to The Sketch environment has been improved in
generate predictions. The result of a simulation is a several respects, based on requests from Garp3
graph consisting of qualitative states and transitions users in the NNR project. The new version allows
involving changes in quantity values and/or saving and loading sketches, so that users can edit
(in)equality statements, representing behavioural and store multiple versions of each type of sketch
change. Garp3 consists of several screens and (and export them as postscript figures). The interface
editors that are organized into three environments: for editing inequalities, quantities and values in the
(1) the Build environment, which provides editors to sketch state-transition graph editor has been
build the basic model ingredients (e.g., entities, improved to better handle the different kinds of
quantities, values), model fragments, and scenarios; (in)equality statements. Other improvements relate
(2) the Sketch environment, which provides editors to to the import functionality, which allows importing
create preliminary sketches (concept map, structural parts of one sketch to another sketch, following the
model, causal model, and expected state-transition modeling framework described in [2].
graph); and (3) the Simulate environment, which
provides control over the simulation and views on the
simulation results (e.g., state-transition graph, values The Simulate environment (see figure 1) has
and dependencies per state, value and inequality been improved in several ways to address two
histories over multiple states). Compared to the issues: (1) in complex simulations, users had trouble
previous release version, a number of substantial finding the possible behaviours; and (2) users
improvements have been made to each of the three sometimes need more control over the simulation
environments, as well as to the simulation tracer, and engine to manage the complexity of the simulation.
the qualitative reasoning portal, as described in the To address the first issue, the functionality for
paragraphs below. searching behaviour paths has been improved by
using a more efficient search algorithm, and adding
options for searching for end states, cyclic paths or
In the Build environment, the copy functionality complete paths (from a begin state to an end state, or
has been improved to facilitate sharing parts of an end loop) in a state-transition graph. To address
models more efficiently, by reusing existing the second issue, the inequality reasoning has been
knowledge definitions. For example, when pasting a improved, and the set of preferences has been
model fragment that includes other model fragments revised, to provide better, and more intuitive control
into another model, Garp3 will now reuse existing over the reasoning of the simulation engine.
2
3. Figure 1. A screenshot of Garp3, illustrating the new functionality
related to simulation control and search in the state-transition graph.
The tracer functionality provides insight into that whenever the user clicks on the Help-icon (the
the reasoning of the Garp3 simulation engine. The owl) in Garp3, the relevant help page for the currently
tracer presents, among other things, information active screen will appear. To support novice users in
about model fragments that are being considered, acquiring the vocabulary of qualitative reasoning and
inequality reasoning being performed, and modeling, the terms used in the Garp3 interface are
terminations, which possibly lead to state transitions. described in a glossary.
The tracer has been completely revised to provide
more information, in a better readable format, and
also includes a new general/critical category to Visit the QRM portal website at
highlight the most important feedback from the http://www.garp3.org for further details and to
simulation engine, which makes it easier for users to download the most recent version of the Garp3
diagnose problems with their model. Furthermore, the software.
tracer works significantly faster than before.
The QRM (Qualitative Reasoning and References
Modelling) portal website provides an overview of the [1] J. Liem, A. Bouwer, and B. Bredeweg, 2006.
Garp3 software, documentation, and related research Collaborative QR model building and simulation
publications, as well as example models and results workbench, Naturnet-Redime, STREP project co-
from the NNR case studies described elsewhere in funded by the European Commission within the Sixth
this newsletter. The major new addition is an Framework Programme (2002-2006), Project no.
extensive collection of help pages about the Garp3 004074, Project Deliverable Report D4.3.
collaborative workbench, explaining all of its
functionality. Each help page contains, among other [2] B. Bredeweg, Salles, P., Bouwer, A., and Liem L,
things, a description of the task at hand, the context, 2005. Framework for conceptual QR description of
related tasks, and, in most cases, an example with a case studies, Naturnet-Redime, STREP project co-
screenshot. The help pages can be accessed directly funded by the European Commission within the Sixth
from Garp3 (provided an internet connection and Framework Programme (2002-2006), Project no.
browser) in a context-sensitive manner. This means 004074, Project Deliverable Report D6.1.
3
4. Troubleshooting Qualitative Models
Jochem Liem and Bert Bredeweg
(University of Amsterdam)
This paper describes the issues modellers One of the main issues encountered by
encounter when developing qualitative models, and modellers developing qualitative models is dealing
initial work started in the NaturNet-Redime (NNR) with undesired simulation results. For example,
project to automatically detect and solve these simulating a scenario may result in having no states,
issues. Ecologists working on different case studies an end state could contain a changing quantity that
in the NNR project are developing qualitative models has not yet reached its final value, and magnitudes
in which they capture their knowledge about how and derivatives could be missing. Furthermore,
river ecology relates to sustainable development. The simulations may result in too many states, some
captured knowledge is organized in model fragments behaviour could be missing, and other behaviour
that describe small parts of the structure and might be undesired. It is often difficult to determine
behaviour of a system. The main purpose of model which model ingredients are responsible for an issue,
fragments is the introduction of causal relations and equally difficult to figure out how and which
between quantities given a certain system structure. model ingredients should be changed to solve the
issue. Currently, modellers ask qualitative reasoning
Qualitative models allow these ecologists not
experts to help them troubleshoot their models.
just to formalise their knowledge, but also to discover
However, at the end of this project, the software
the implications of that knowledge through simulation.
should support modellers well enough for them to
An initial situation of the system (called a scenario)
solve issues without the help of qualitative reasoning
created by the modeller serves as input for the
experts anymore.
simulation engine that generates the behaviour of the
modelled system. The behaviour has the form of a Consider the following example from a case
state graph, and shows how the quantities in the study describing how river planning affects the
system change in time. The simulator works by recruitment of salmon through different life states.
determining which model fragments apply to the Figure 1 shows a scenario that represents a river
system described in the scenario and augments the containing a population of salmon divided into
scenario with the causal relations introduced by these different life stages. Each part of the population lives
model fragments. These causal relations make it in the same river (according to this model), and each
possible to calculate the value of the derivatives of life stage migrates into the next life stage. The part of
the quantities, and thus the successor states of the the population in the first life stage has a high number
initial state can be determined. For each of the of individuals, while the other life stages have a
successor states the same procedure is performed to normal amount of individuals.
create a complete state graph.
Figure 1: A scenario describing the number of individuals of a population in different life stages living in
a river.
4
5. An expert created a model fragment capturing stage is bigger than the number of individuals in the
his knowledge about how different parts of the second stage, the recruitment rate is positive. If they
population recruit from one life stage into the next are equal, the rate is zero. And if the second stage is
(see Figure 2). The model fragment requires two bigger, the rate is negative. The influence (I+) sets
parts of a population to inhabit a river. The first life the derivative of the second number of individuals to
stage should migrate into a second life stage. When positive if the recruitment rate is Plus, negative if it is
these conditional model ingredients are present, new Min, and Zero if it is zero. The proportionality (P+)
knowledge is added to the scenario. A recruitment sets the derivative of the recruitment rate to
rate quantity is introduced, and its value is calculated increasing if the number of individuals of the first
by subtracting the number of individuals in the first stage increases, to decreasing if it decreases, and to
stage from the number of individuals in the second stable if it is stable.
stage. When the number of individuals in the first
Figure 2: The recruitment model fragment representing the causal relations involved in the recruitment
process.
When simulating the scenario, the model Associating the recruitment rate to one of the
fragment fits on the scenario 3 times (on the eggs population stages instead of the river can solve the
and the juveniles, on the juveniles and the smolts,
issue, as it would result in a recruitment rate quantity
and on the smolts and the adults). However, the
to be introduced for each interaction.
simulator generates no states. This is a typical
example of a modelling issue that is difficult to solve In the last half-year of the NNR project, we aim
for the modeller. When a scenario is simulated and to develop model-troubleshooting support within
1
no model fragments are active, there is only a single Garp3 that will automatically detect these kind of
state in the state graph, i.e. the state describing the issues within models and suggest possible solutions.
scenario itself. When there are no states, the We will analyse the intermediate modelling results
introduced knowledge causes a contradiction, e.g. created by the modellers working on case studies to
two incompatible inequalities are true, or a value has determine what the most frequently occurring issues
multiple values. are, and how they could be solved. Furthermore, we
are looking at existing model-based diagnosis
In this case, the issue with the model fragment
literature to see how existing methods can contribute
is that the recruitment rate is associated to the river.
to a solution. Model troubleshooting support should
Since there is only a single river, only a single
make it easier for domain experts to capture their
recruitment quantity is introduced, even though there
knowledge about sustainable development.
are three interactions. Calculating the recruitment
rate for the juveniles to the smolts and the smolts to
the adults yields a value zero. However, for the eggs
to the juveniles the recruitment rate yields the value
Plus.
Therefore, the single recruitment rate quantity is set
to both the value Plus and the value Zero, which is
impossible. Therefore no states are generated.
1
http:www.garp3.org
5
6. A qualitative model about sustainability in the Brazilian Riacho
Fundo water basin
Paulo Salles (University of Brasilia)
Qualitative Reasoning models may be useful in this
A qualitative model exploring sustainability
case. An evaluation study with Latvian students,
issues in the Riacho Fundo water basin (Brasilia,
reported in the Naturnet-Redime Newsletter last
Brazil) was developed by the Naturnet – Redime
November (Bredeweg et al. 2006) confirms the
project and in the next months this model will be
potential of such models to communicate
presented to stakeholders for evaluation. The model
sustainability concepts. The qualitative model
goal is to support stakeholders in understanding and
developed by the Naturnet – Redime team for the
decision-making about water resources
Riacho Fundo focuses on the following aspects: (a)
management. Relevant concepts were selected
the influence of urban drainage systems on soil
according to stakeholders opinions, and are
conditions, economic activities and human well being;
expressed in the model with everyday vocabulary
(b) the dynamics of erosion and water infiltration in
and diagrams, in order to make them accessible to
the soil, and their influences on springs, streams, and
the general public. This article explains some
economic activities in semi urbanized areas; and (c)
features of the Riacho Fundo basin addressed by the
the effects of soil protection on springs and river
model and how the evaluation process will be done.
conditions, biodiversity and agricultural production.
The Riacho Fundo is a small water basin in
The Riacho Fundo model will be evaluated in a
Brasília, Brazil. Since the new capital was built in the
series of meetings with members and people
late 50’s, it has been impacted by changes of natural
involved in the WBC – Paranoá river. A general
landscapes into rural and urban areas. The most
workshop is being organized to present the results of
important economic activities in the basin are those
the Naturnet-Redime project. Next to that, the
related to services, including business offices,
Naturnet – Redime team will run a number of
commerce and car repair garages. Industrial activities
evaluation sessions with volunteers, in which different
include food, dyed fabric and clothes production.
aspects of the model will be actually evaluated. Given
Vegetables and corn production are the main
its potential for educational purposes, some of these
activities for most of the farmers, and cattle, pork and
meetings will include teachers and students. As in
sheep are the most important herds in the Riacho
similar activities previously done, qualitative model
Fundo region. Agriculture represents a small part of
evaluation starts with discussions about
the regional GDP, but it is important as it contributes
representations of concepts and simulation results.
to keep green areas and parts of natural vegetation.
Using diagrammatic representations of cause-effect
As a consequence of these economic activities, soil
relations encoded in the model as the basis for
and water contamination with pesticides, industrial
representing knowledge, evaluators are asked to
effluents and organic matter have been reported.
discuss how to solve conflicting situations, and to
Besides that, urbanization has caused deforestation
draw (using paper and pencil) alternative diagrams to
and loss of biodiversity and erosion resulted in the
express their own views. Finally, the evaluators give
disappearance of springs and small streams.
their opinion in questionnaires and interviews about
According to Brazilian legislation, decisions on different aspects of the model, such as the clarity of
water management are made in Water Basin concepts represented in the model, significance for
Committees (WBC), institutions that gather improving understanding and usability of the model
representatives from the government, productive as a tool to support decision-making. The results of
sectors and civil society. Riacho Fundo water the evaluation will be described in the next issue of
resources are now managed by the Paranoá river the Naturnet-Redime Newsletter!
WBC, recently created after five years of
Reference:
stakeholders and community mobilization. Since the
preparatory meetings, the WBC has been the locus Bredeweg, B.; Salles, P.; Bouwer, A.; Bakker,
for discussions about what is required for the Riacho E. and Liem, J. (2006) Successful use of Garp3 by
Fundo basin to be sustainable. From these activities, stakeholders. Naturnet-Redime Newsletter, number
it became clear that there is a need for tools to 4, November 2006, pp.: 10 – 12. ISSN 1801-6480.
improve understanding of the complex problems the
WBC has to decide upon, and to express people’s
ideas and proposals using a formal language.
6
7. Qualitative Reasoning Model – the modelled system functioning
study: Danube Delta Biosphere Reserve case study
Eugenia CIOACA (Danube Delta National Institute Tulcea, Romania), Bert BREDEWEG (University of
Amsterdam), Paulo SALLES (University of Brasilia), Tim Nuttle (University of Jena, Germany)
Danube Delta Biosphere Reserve (DDBR) graphics. They are focused on describing the
is one of the five case studies considered in the system’s features related to those causes and their
FP6 NaturNet-Redime project to develop effects which delimit the sustainable development
Qualitative Reasoning (QR) models. actions. The three conceptual structures (Concept
Model System: DDBR, located at the mouth of the map, Causal model, and Structural model) focus on
Danube River before it reaches the Black Sea, has water pollution negative effect propagation from
an area of 5,800 sq. km, making it one of the water to aquatic biota (plants, zooplankton,
greates t wetlands in the world. macroinvertebrates, fish, birds) and ultimately to
human health.
In order to construct a model of a real system
using the Qualitative Reasoning concept [1], it is Concept map is presented in Figure 1. It
necessary to understand this system functions. It contains the modelled system components and the
requires a good knowledge of the system functional relationships among them, in the
components, their structural and cause-effect framework of basic physical, physical-chemical and
relationships, which leads to a better understanding biological processes. The DDBR main components
of the overall system’s behaviour. This is achieved are aquatic ecosystems. Their behaviour depends
as a preliminary study necessary in preparing the on water quality. The concept map stresses the
Qualitative Reasoning (QR) model ingredients. It Water pollution process. This process “negatively
follows the Framework for conceptual QR affects” both aquatic biological components status
description of case studies [2], through Concept and human health. The influences can be direct or
map, Global causal model, and Structural model indirect.
Figure 1. Concept map for Danube Delta Biosphere Reserve water pollution.
Full Structural model, for the two types of
ecosystems terrestrial and aquatic, is shown in
Figure 2. It contains the system entities and their Structural relationships hierarchical organised [3, 4,
structural relationships hierarchical organised [3, 5], as they are necessary to be later implemented in
Garp3 – QR specialised software.
4,5], as they are necessary to be later implemented
in Garp3 – QR specialised software.
7
8. Figure 2. Structural model of the Danube Delta Biosphere Reserve aquatic ecosystems.
Global Causal model contains the modelled them are presented in Figure 3: DDBR global
system quantities and the causal relationships causal model. Influence (I+, positive rate or I-,
among them, governed by active processes. In negative rate) indicates a direct effect of a process
total, 12 processes are active in the DDBR aquatic rate on other quantity, causing this quantity value
environment, which influence the quantities that change: increase or decrease, respectively.
represent the amount of each organism. Changes Proportionality indicates an indirect effect of a
in these amounts propagate to other quantities, as quantity change (P+, quantity increase, or P-,
effect of one organism group behaviour to another quantity decrease) that causes other quantity value
one. The quantities and causal dependencies change: increase or decrease, respectively.
(Influence : I+/I- or Proportionality: P+/P-) among
Figure 3. Global Causal model for Danube Delta Biosphere Reserve aquatic ecosystems water pollution.
References 2. B., Bredeweg, P., Salles, A., Bouwer, and J.,
Liem 2005. Framework for conceptual QR
1. B., Bredeweg, 2005 – Qualitative Reasoning.
description of case studies, NaturNet-Redime,
NaturNet-Redime NEWSLETTER No. 1, pp. 3-5.
Project Deliverable report D6.1.
8
9. 3. E., Cioaca, B., Bredeweg, T., Nuttle, 2006 - The basic biological, physical, and chemical
Danube Delta Biosphere Reserve: a Case Study processes related to the environment
for Qualitative Modelling of Sustainable http://www.naturnet.org/Internal
th
Development. QR-06 20 International section/Deliverables list
Workshop on Qualitative Reasoning
5. T., Nuttle, et al, 2006 - Progress in developing
PROCEEDINGS, pp.151-156. Dartmouth
cognitive models of sustainability: case studies
College, Hanover, New Hampshire, USA.
from European and Brazilian river basins.
4. E., Cioaca, S., Covaliov, L., Torok, Ibram, O., NaturNet-Redime NEWSLETTER 2, pp.12-15.
2006 - D6.2.1 Textual description of the Danube
Delta Biosphere Reserve case study focusing on
Danube Delta Biosphere Reserve Case Study Qualitative
Reasoning Model - an Environmental System Sustainable
Development Decision Making Support
Eugenia Cioaca (Danube Delta National Institute Tulcea, Romania), Bert Bredeweg (University of
Amsterdam), Paulo Salles (University of Brasilia)
Qualitative Reasoning (QR) models may DDBR QR model consists of the following
serve as mediators in decision making process on ingredients: 18 entities, hierarchical structured as
Sustainable Development. For that purpose, a QR shown in Figure 1; 17 Scenarios, as shown in
model can be said to capture (to document) an Figure 2, and 57 Model fragments. The description
explanation of how a system behaves. The of the Scenarios and Fragments, and Simulation
documentation has two distinguished parts: QR results, presented in this paper, refers to the DDBR
model components description as they are aquatic ecosystems water pollution process.
represented in Garp3 software [1] and the model
scenarios simulation results. This paper presents
a summary of the Danube Delta Biosphere Reserve Scenarios
(DDBR) QR model documentation [2]. Within the
To model the DDBR water pollution process,
DDBR area, aquatic ecosystems are the most
extensive and important natural resource of this there 3 scenarios have been constructed. Here is
presented the DD Water pollution and DD aquatic
area, both from the biodiversity and the human
being perspective. Due to its impacts, water biodiversity Scenario (Figure 3.). It concerns the
quality is a central focus of concern from the modelled system’s Entities, their associated
Strategy of Sustainable Development point of view. quantities, the Configurations among Entities
Nevertheless, water pollution is a major problem in participating in this process. It configures the
process negative effects and the ways it
the DDBR and has caused loss of biodiversity over
the last several decades. Additionally, human propagates to aquatic biotic component (Aquatic
population relies on DDBR resources for fresh biological entities). This Scenario contains:
water and food, so they are implicitly negatively 1. The modelled system's external influences
influenced by water pollution. To progress and (Agents): Agriculture: Nutrient run-off (which
reduce these negative effects, it is necessary to participates in the water pollution process only
understand and communicate how water pollution if in high content. For values equal or smaller
affects biodiversity and human health in DDBR. The than Medium it participates in Plant growth
DDBR QR model is therefore constructed to model process as main food resource for any Plant
the behaviour of DDBR aquatic ecosystem flora species) and Industry: Heavy metals (which
and fauna populations as well as human health, have the property of bioaccumulation in any
indicator of people living in or around this area. The aquatic biological organism, leading to that
behaviour of these components is modelled as entity pollution, even Mortality if in
related to the basic aquatic ecosystem physical, Medium/High concentration in water).
chemical and biological processes. The main
9
10. Figure 1. Entity hierarchy of the DDBR system. Figure 2. The DDBR system QR model Scenarios.
2. The third water pollution component is given, 3. To reduce the simulation complexity,
mainly by Cyanotoxins. They are produced in Assumptions are introduced in the Scenario
water if there is a content of some poisoning construction: “Assume nutrient consumption is
species of Blue-green algae (Cyanobacteria), zero and steady”, “Assume Migration is zero
which contain Cyanotoxins in their cells. and steady”, and “Assume Production is
medium and steady”.
Figure 3. DD Water pollution and DD aquatic biodiversity Scenario
Model fragments
In the Qr software that we use there are
A process is presented in this paper in Figure 4. DD
three types of Model fragments: Static, Process,
Water pollution and DD aquatic population
and Agent.
biodiversity.
10
11. Figure 4. DD Water pollution and DD aquatic population biodiversity - Process MF.
It figures the structural and behavioural
relationships between River delta and the Aquatic
2. A part of Danube Delta: Nutrient inflow stays
population, related to River delta: Water pollution
in the system and contributes to Nutrient
influence on Aquatic population components
available for Plant species growth while
behaviour, as follows: positive influence (I+) on
another part is lost (Nutrient net loss), either
Aquatic population: Mortality and negative
through Nutrient outflow or Nutrient
proportionality (P-) on Aquatic population:
consumption (by aquatic Plant species only).
Biodiversity. It emphasizes the causal conditions
which have been generating the loss of DDBR 3. So happens with Danube Delta: Heavy
biodiversity, in order to delimit those objectives for a metals inflow.
sustainable use of natural resources and a
4. A part of Nutrient net loss and Heavy metals
Sustainable Development Strategy addressing the
aquatic ecosystems. net loss is recycled from dead organic matter
as result of Particulate Organic matter
DDBR QR Model output: Scenarios bacterial decomposition (Pom bact decomp)
simulation results. process.
One of the scenario simulation results is 5. Danube delta: Water pollution rate is the
presented for the DD Water pollution and DD result of three main water pollutants: Danube
aquatic population biodiversity Scenario. This is the delta: Nutrient available, Heavy metals
Dependency diagram (Figure 5), which provides available and Cyanotoxins;
information on structure and causality (Influence I,
6. Danube delta: Water pollution rate has a
or Proportionality P), correspondence (Q, dQ) and
direct positive influence (I+) on any Aquatic
in/equality (=, >, <) dependency relationships
among the system’s water pollutants (Nutrients, biological entity: Mortality.
Heavy metals and Cyanotoxins), any Aquatic 7. Aquatic biological entity: Mortality has an
biological entity, involved in Water pollution process indirect negative influence (P-) on any
and its effect on Aquatic populations, as follows: Aquatic biological entity: Biomass.
1. Danube River: Nutrient inflow and Heavy
Metals inflow main resources are the two
system’s external influences (Agents):
Agriculture and Industry, localised “In
catchment area of” the River. There is a
close relationship (P+ and Q) between
Nutrient run-off from Agriculture lands and
Nutrient that enters the Danube River. The
same relationship occurs between Heavy
metals run-off from Industry and Heavy
metals entering the Danube River. From the
River, these two main water pollutants reach
the Danube Delta aquatic ecosystems.
11
12. Figure 4: Dependency diagram of the Water pollution process.
Knowledge about the aquatic ecosystems behaviour References
within the DDBR system, as it is presented in the
B., Bredeweg, A., Bouwer, J., LIEM, 2006 – Garp3-
DDBR QR Model, may supportdecision-making
Workbench for capturing conceptual knowledge.
activities on natural resources protection, as well as
NaturNet-Redime NEWSLETTER No. 2, pp. 2-4.
achieving the environmental Sustainable
Development within this system. E., Cioaca, B., Bredeweg, P., Salles, 2006 -
D6.2.2. Qualitative Reasoning Model and
documentation for learning about sustainable
development focusing on basic biological, physical,
and chemical processes related to the environment in
the Danube Delta Biosphere Reserve,
http://www.naturnet.org/Internalsection/Deliverables
list/
.
12
13. Curriculum for Learning about Sustainable Development Using
Qualitative Reasoning.
Tim Nuttle (University of Jena, Germany), Paulo Salles (University of Brasilia, Brazil), Bert Bredeweg
(University of Amsterdam, The Netherlands), Eugenia Cioaca (Danube Delta National Institute, Romania),
Elena Nakova (Central Laboratory of General Ecology, Bulgaria), Richard Noble (University of Hull,
England), Andreas Zitek (Vienna University of Applied Life Sciences, Austria).
Work Package 6 partners have been hard at 1. Goal: the expected learning outcome of
work producing Qualitative Reasoning (QR) models completing this assignment.
and simulations of sustainability issues pertaining to
2. Introduction: a textual description of the
case studies in different river catchment areas.
system and its key features and issues.
Models from the case studies for the River Mesta
(Bulgaria), Danube Delta Biosphere Reserve 3. Overview: a list of the main concepts that will
(Romania), and Riacho Fundo (Brazil) are completed be covered in the exercises.
and models for the River Kamp (Austria) and
Yorkshire River Ouse and River Trent (England) are 4. Exercises: a series of questions about the
progressing well according to plan. Papers from each model system and instructions on how to
interact with the model to discover the
of these cases studies are being presented in a
answers.
special session dedicated to the NaturNet-Redime
st
project at the 21 International Workshop on Here, we provide an example of some of the
Qualitative Reasoning, to be held 27-29 June 2007 in questions that learners would seek to answer while
Aberystwyth, Wales (http://monet.aber.ac.uk/qr07/). exploring the Riacho Fundo case study:
These case studies focus on different parts of Sce ur01a: What happens when there is no
the sustainability domain and will be integrated into a drainage in a city?
common curriculum where learners can interact with
models to learn about cause and effect in sustainable Open scenario Sce ur01a drainage zero
development scenarios. The learning endeavor will constant and run the simulation (for the initial
be supported by a curriculum and assignments that state/s only).
guide learners through the learning process. Q1. (structure) Explain in your own words the
Assignments for exploring QR models from system being modeled by this scenario.
each case study will follow a newly developed Hint: Select state [1] and open the entity
standard assignment template, which can be relations view. Rearrange the elements as
reapplied to future models as they are developed. necessary.
This template follows basic principles in pedagogy,
where learning progresses from basic levels focusing
on understanding definitions of terms (i.e.,
“knowledge” in Bloom’s Taxonomy of cognitive
understanding), through application of knowledge, to
synthesis and evaluation of sustainable versus
unsustainable scenarios. This assignment
organization also progresses through the hierarchical
levels of the model, from structure (What are the
components of the system and how are they
related?) to causality (How do the relations between
components affect each other?) to dynamics (How do
the causal relationships cause the system to change
through time?).
As with the other NaturNet-Redime learning
materials, assignments for learning about
sustainability in each of the case studies will be
presented using Moodle software, an open source
course management system that applies sound
pedagogical principles to help educators create Figure 1. The Garp3 workbench and state graph
effective online learning communities for the Scenario (top) and the Entities Relations
(www.moodle.org). Each assignment is structured as view for state [1] (bottom).
follows:
13
14. After following the instructions (and the hint),
the learner would see that the system being modeled
is one where rainfall falls on the urban Riacho Fundo
and where there is no drainage (Figure 1). The
learner would progress through several more
questions (not shown) to explore structure and
causal relationships that result from that structure.
Then learners are asked to make predictions based
on that causality:
Q6. (causality) Predict what will happen
throughout the system and why. Will the
system evolve towards a final end point? What
will that end point be?
Hint: Display the causal dependencies for
state [1]. Reason through the causal
dependencies to see how they cause and
propagate change through the system. Figure 3. Value History for the path (Figure 1 left)
showing the change in the modeled scenario.
Learners would see that with a medium
amount of water runoff and zero drained water, there
is uncontrolled flow (value plus) in the urban Riacho
Fundo (three value histories on the left of Figure 3).
When there is uncontrolled flow, this causes the
amount of uncontrolled water to increase. As
uncontrolled water increases in the catchment,
flooded area increases as does the amount of
economic damage.
Figure 2. Causal dependencies from state [1]. This simple scenario demonstrates how the
model of the Riacho Fundo case study transmits the
expert knowledge contained in the model to the
Learners would follow the instructions, learner. Subsequent questions in the assignment
producing Figure 2, and write a paragraph describing lead the learner to evaluate whether this system is
their predictions. They would then compare their sustainable or unsustainable, and how sustainability
predictions to those produced by the Garp3 of the system might be better achieved. Further
reasoning engine: assignments address other scenarios in the Riacho
Q7. (dynamics) Describe what the model Fundo, including more possibly more complex
predicts to happen in the system over time. scenarios that involve more processes, as well as
point to other models and case studies where related
Hint: Run the full simulation. Note the general concepts can be explored.
nature of the state graph (cycles, branching
paths, etc.). Once the assignments for each case study
model are completed, they will be evaluated by
Select a path. View the value history, stakeholder and student groups to assess the degree
focusing on what happens to the state to which they help meet the learning goals
variables. Repeat for several paths (if there established in the assignment.
are more than one).
Look for assignments for each of the case
studies to appear on the NaturNet-Redime web portal
(www.naturnet.org) in the coming months.
14
15. Events of interest
9th – 11th May, Maputo 12th June – 15th June 2007, Brussels
IST-Africa 2007 Conference and Exhibition is Green Week will provide a unique opportunity
the second in an Annual Conference Series which for debate, exchange of experience and best practice
brings together delegates from leading commercial, among non-governmental organisations, businesses,
government & research organisations across Africa various levels of government and the public,
and from Europe, takes place in Maputo, Brussels, Belgium
Mosambique, South Africa
http://ec.europa.eu/environment/greenweek/home.ht
http://www.ist-africa.org/Conference2007/ ml
10th – 11th May 2007, Dresden 27th June – 29th June 2007, Aberystwyth
th
The 6 Saxonian GI Strategy – Forum GI QR07: 21st Annual Workshop on Qualitative
2007. Dresden, Germany. European week Reasoning, Aberystwyth, U.K
http://gdi-sn.blogspot.com/ http://monet.aber.ac.uk:8080/monet/qr07/QR07/Hom
e.html
15th – 16th May 2007, Prague
2nd – 5th July 2007, Glasgow
Information systems in agriculture and forestry
2007, XIII. European conference, themed Living EFITA/WCCA 2007 , Environmental and rurals
Labs Prague, Czech republic. sustainability through ICT, Glasgow Caledonian
University, Glasgow, Scotland
http://www.isaf.cz/index_en.php?iMenu=10
http://www.efitaglasgow.org/
21st May 2007, Prague
4th – 6th July 2007, Porto
FP7 Information Day - will be specifically
th
dedicated to FP7 opportunities in 2007 in the fields of 13 GI & GIS Workshop. GISDIs for
ICT for the environment and ICT for energy environmental analysis, monitoring, and modelling;
efficiency, Prague, Czech Republic environmental protection, natural hazards prediction
and mitigation, and other applications areas , Porto,
http://www.isess.org/conferences.asp?Conf=5
Portugal
http://www.ec-gis.org/Workshops/13ec-gis
22nd May – 24th May 2007, Prague
The International Symposium on
4th – 9th November 2007, Papeete
Environmental Software Systems. ISESS 2007 is part
of a series organized by IFIP (International MUTL 2007, International Workshop on Mobile
Federation for Information Processing) Working and Ubiquitous Technologies for Learning, Papeete,
Group 5.11 "Computers and Environment". Crowne French Polynesia (Tahiti)
Plaza Hotel Prague, Czech
http://www.iaria.org/conferences2007/MUTL.html
http://www.isess.org/
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16. SIXTH FRAMEWORK PROGRAMME
Educational programmes on social, economic, and environmental
tools for the implementation of the EU Strategy on Sustainable Development
at both EU and international levels
NATURNET-REDIME
New Education and Decision Support Model for Active Behaviour in Sustainable Development
Based on Innovative Web Services and Qualitative Reasoning
Project no. 004074
Instrument: SPECIFIC TARGETED RESEARCH PROJECT
Thematic Priority: SUSTDEV-2004-3.VIII.2.e
Start date of project: 1st March 2005 Duration: 30 months
Web: http://www.naturnet.org
Project officer Patrizia Poggi Patrizia.POGGI@ec.europa.eu
Project co-funded by the European Commission within the Sixth Framework Programme (2002-2006)
Co-odinator Contact person Country E-mail
Czech Centre for Science and Society Karel Charvat CZ ccss@ccss.cz
Partner Contact person Country E-mail
Environmental Network Ltd Peter Barz UK pkab@env-net.com
http://www.env-net.com/
Albert-Ludwigs University of Freiburg Barbara Koch DE barbara.koch@felis.uni-freiburg.de
https://portal.uni-freiburg.de/felis/
Municipality of Francavilla di Sicilia Nino Paterno IT npat@stepim.it
http://www.stepim.it/
Gymnazium Bozeny Nemcove Jan Sterba CZ sterba@gybon.cz
http://www.gybon.cz/
INNOVATION. Grenzüberschreitendes Netzwerk e.V Frank Hoffmann DE 320052219146@t-online.de
http://www.IGN-SN.de
Institute of Mathematics and Comp. Science, Univ. of Latvia Maris Alberts LV alberts@acad.latnet.lv
http://www.lumii.lv/
Joanneum Research Alexander Almer AT alexander.almer@joanneum.at
http://dib.joanneum.at/
KRIMULDA - Municipality Juris Salmins LV krimulda@rrp.lv
APIF MOVIQUITY SA Wendy Moreno ES wmp@moviquity.com
http://www.moviquity.com/
Mission TIC de la Collectivité Territoriale de Corse Jerome Granados FR jerome.granados@mitic.corse.fr
http://www.mitic.corse.fr
Region Vysocina Jiri Hiess CZ Hiess.J@kr-vysocina.cz
University of Jena, Institute of Ecology Tim Nuttle DE Tim.Nuttle@uni-jena.de
http://www.uni-jena.de/ecology.html
Michael Neumann mn@mneum.de
Human Computer Studies Laboratory, Informatics Institute, Bert Bredeweg NL http://hcs.science.uva.nl/~bredeweg
Faculty of Science, University of Amsterdam
http://hcs.science.uva.nl/projects/NNR/
Bulgarian Academy of Sciences, Central Laboratory of Yordan Uzunov BG uzunov@ecolab.bas.bg
General Ecology (CLGE)
Danube Delta National Instit. for Research and Development Eugenia Cioaca RO eugenia958@yahoo.co.uk
http://www.indd.tim.ro/
University of Brasilia, Institute of Biological Sciences Paulo Salles BR psalles@unb.br
University of Hull, International Fisheries Institute Ian Cowx UK i.g.cowx@hull.ac.uk
University of Natural Resources and Applied Life Sciences, Stefan Schmutz AT stefan.schmutz@boku.ac.at
Department of Hydrobiology, Fisheries and Aquaculture
16