A new methodology is developed to analyse existing water quality monitoring networks. This methodology
incorporates different aspects of monitoring, including vulnerability/probability assessment, environmental
health risk, the value of information, and redundancy reduction. The work starts with a formulation of a
conceptual framework for groundwater quality monitoring to represent the methodology’s context. This
work presents the development of Bayesian techniques for the assessment of groundwater quality. The
primary aim is to develop a predictive model and a computer system to assess and predict the impact of
pollutants on the water column. The process of the analysis begins by postulating a model in light of all
available knowledge taken from relevant phenomenon. The previous knowledge as represented by the prior
distribution of the model parameters is then combined with the new data through Bayes’ theorem to yield
the current knowledge represented by the posterior distribution of model parameters. This process of
updating information about the unknown model parameters is then repeated in a sequential manner as
more and more new information becomes available.
The main challenges to achieving a reliable model which can predict well the process are the
nonlinearities associated with many biological and biochemical processes in the system. Artificial intelligent
approaches revolved as better alternative in predicting the system. Typical measured variables for effluent
quality of wastewater treatment plant are pH, and mixed liquor suspended solids (MLSS). This paper
presents an adaptive neuro-fuzzy inference system (ANFIS) and feed-forward neural network (FFNN)
modeling applied to the domestic plant of the Bunus regional sewage treatment plant. ANFIS and feedforward
neural network techniques as nonlinear function approximators have demonstrated the capability
of predicting nonlinear behaviour of the system. The data for the period of two years and nine months
sampled weekly (140 week samples) were collected and used for this study. Simulation studies showed
that the prediction capability of the ANFIS model is somehow better than that of the FFNN model. The
ANFIS model may serves as a valuable prediction tool for the plant.
DEVELOPING THE OPTIMIZED OCEAN CURRENT STRENGTHENING DESALINATION SEMI-PERMEA...ijbesjournal
Alongside improvements in desalination operation and development of new technologies, problems of weakened counter current and global warming have emerged. Therefore, our study suggests a new desalination model, based on the experimental Support Vector Machine (SVM) algorithm, for semipermeable membrane separation. First, the reverse osmosis (RO) process used semi-permeable membrane and osmotic pressure to remove the solutes dissolved in seawater and obtain pure freshwater. The desalination process also applied MSF and MED, which are the best technologies developed through elimination of various problems that were previously experienced. This research is directed towards suggesting a model that can effectively create the semi-permeable membrane used in the desalination process. To efficiently prevent a counter current and safely obtain the water resources, an innovative technology is suggested by applying Genetic Algorithm (GA) to the SVM model for the semi-p
The main challenges to achieving a reliable model which can predict well the process are the
nonlinearities associated with many biological and biochemical processes in the system. Artificial intelligent
approaches revolved as better alternative in predicting the system. Typical measured variables for effluent
quality of wastewater treatment plant are pH, and mixed liquor suspended solids (MLSS). This paper
presents an adaptive neuro-fuzzy inference system (ANFIS) and feed-forward neural network (FFNN)
modeling applied to the domestic plant of the Bunus regional sewage treatment plant. ANFIS and feedforward
neural network techniques as nonlinear function approximators have demonstrated the capability
of predicting nonlinear behaviour of the system. The data for the period of two years and nine months
sampled weekly (140 week samples) were collected and used for this study. Simulation studies showed
that the prediction capability of the ANFIS model is somehow better than that of the FFNN model. The
ANFIS model may serves as a valuable prediction tool for the plant.
DEVELOPING THE OPTIMIZED OCEAN CURRENT STRENGTHENING DESALINATION SEMI-PERMEA...ijbesjournal
Alongside improvements in desalination operation and development of new technologies, problems of weakened counter current and global warming have emerged. Therefore, our study suggests a new desalination model, based on the experimental Support Vector Machine (SVM) algorithm, for semipermeable membrane separation. First, the reverse osmosis (RO) process used semi-permeable membrane and osmotic pressure to remove the solutes dissolved in seawater and obtain pure freshwater. The desalination process also applied MSF and MED, which are the best technologies developed through elimination of various problems that were previously experienced. This research is directed towards suggesting a model that can effectively create the semi-permeable membrane used in the desalination process. To efficiently prevent a counter current and safely obtain the water resources, an innovative technology is suggested by applying Genetic Algorithm (GA) to the SVM model for the semi-p
Letter Sent by 25 Anti-Fracking Organizations to Gov. Tom Corbett on DEP Wate...Marcellus Drilling News
A letter sent by 25 known anti-drilling groups to PA Gov. Tom Corbett rehasing unsubstantiated allegations that the state Dept. of Environmental Protection withholds testing for certain chemicals that may be tied to shale gas drilling.
Performance comparison of SVM and ANN for aerobic granular sludgejournalBEEI
To comply with growing demand for high effluent quality of Domestic Wastewater Treatment Plant (WWTP), a simple and reliable prediction model is thus needed. The wastewater treatment technology considered in this paper is an Aerobic Granular Sludge (AGS). The AGS systems are fundamentally complex due to uncertainty and non-linearity of the system makes it hard to predict. This paper presents model predictions and optimization as a tool in predicting the performance of the AGS. The input-output data used in model prediction are (COD, TN, TP, AN, and MLSS). After feature analysis, the prediction of the models using Support Vector Machine (SVM) and Feed-Forward Neural Network (FFNN) are developed and compared. The simulation of the model uses the experimental data obtained from Sequencing Batch Reactor under hot temperature of 50˚C. The simulation results indicated that the SVM is preferable to FFNN and it can provide a useful tool in predicting the effluent quality of WWTP.
Design and Construction of a Simple and Reliable Temperature Control Viscomet...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.
Environmental Monitoring Model of Health, Parasitological, And Colorimetric C...theijes
The sanitary quality of water was evaluated in two micro basins, Bacaxá and Capivari belonging to the Lakes Basin St. John in the state of Rio de Janeiro, Brazil, for colimetric and parasitological analysis. Analyses were performed seasonally over a year and the levels of Escherichia coli were within the recommended only in the summer of 2012 and fall, and inappropriate with levels above recommended in winter, spring and summer of 2013 in both the micro basins. Through our observations, we compare the average values of the levels of total coliforms and Escherichia coli between both rivers. Initially, the samples indicate a similarity between the distributions of coliforms and Escherichia coli. However, Mann-Whitney-Wilcoxon test samples indicate that the distributions are different. In parasitological analysis it was observed that in Capivari was detected a greater presence of filarial larvae. Anthropogenic influences mainly by the presence of sewage is being able to compromise the health quality of the micro basins studied carrying a significant pollutant load to the Juturnaíba reservoir. The monitoring of the sanitary quality of the watersheds that supply the population may indicate when it is necessary to adopt more effective measures in the treatment of water supply of cities.
Assessment of mortality and morbidity risks due to the consumption of some sa...theijes
Natural radioactivity of sixty sachet waters produced by fifteen different enterprises was measured by gamma spectrometry technique. The concentrations of the main natural radionuclides, 40K, 226Ra, and 232Th in the samples varied respectively from 0.87 to 5.70 Bq/L, 0.16 to 0.47 Bq/L and 0.17 to 0.60 Bq/L in the samples with mean values respective of 2.66± 0.60 Bq/L, 0.22± 0.65 Bq/L and 0.34± 0.07 Bq/L. The annual effective doses due to the ingestion of these radionuclides varied from 45.48 to113.07 μSv/y with a mean of 78.41± 15.51 μSv/y. The mortality and morbidity risks assessed in samples, varied respectively from 4.94 10-5 to 1.17 10-4 and 7.20 10-5 to 1.24 10-4 with average values of 6.75 10-5 and 9.84 10-5 . This study showed a morbidity risk relatively high, thus harmful for the population.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Statistical Analysis of Ground Water Quality in Rural Areas of Uttar Pradesh ...IJERA Editor
The importance of groundwater for the existence of human society cannot be exaggerated. Groundwater is the
major source of water in both rural and urban India.Duringlast decade, it was observed that ground water get
polluted drastically and hence, resulted into many water borne diseases which is a cause of many health hazards.
In this paper an attempt has been made to test groundwater quality of different villages of Uttar Pradesh, India
on the basis of thirteen parameters like pH, total dissolved solids, conductivity, total hardness, biological oxygen
demand etc. The results obtained were compared with the BIS (IS 10500:1991) Permissible Standards for
drinking water. Normal Distribution analysis was applied to describe various characteristics of the samples
collected and Correlation Analysiswas done on the samples which measured the strength of association between
twowaterparameters.On the basis of results obtained from analytical and statistical analysis, it was revealed that
all the water sources chosen for study are not suitable for the utilization of water.
Data mining is the knowledge discovery in databases and the gaol is to extract patterns and knowledge from
large amounts of data. The important term in data mining is text mining. Text mining extracts the quality
information highly from text. Statistical pattern learning is used to high quality information. High –quality in
text mining defines the combinations of relevance, novelty and interestingness. Tasks in text mining are text
categorization, text clustering, entity extraction and sentiment analysis. Applications of natural language
processing and analytical methods are highly preferred to turn
DISSECT AND ENRICH DIVIDE AND RULE SCHEME FOR WIRELESS SENSOR NETWORK TO SOLV...ijsc
In remote sensor system, sensor hubs have the restricted battery power, so to use the vitality in a more
productive manner a few creator's created a few strategies, yet at the same time there is have to decrease
the vitality utilization of hubs. In this paper, we presented another method called as 'Partition and Rule
strategy' to unravel the scope dividing so as to open the system field into subfield and next maintain a
strategic distance from the vitality gap issue with the assistance of static bunching. Essentially, in gap and
run plan system range is isolated into three locales to be specific internal, center and external to conquer
the issue of vitality utilization. We execute this work in NS-2 and our recreation results demonstrate that
our system is far superior than old procedures.
Letter Sent by 25 Anti-Fracking Organizations to Gov. Tom Corbett on DEP Wate...Marcellus Drilling News
A letter sent by 25 known anti-drilling groups to PA Gov. Tom Corbett rehasing unsubstantiated allegations that the state Dept. of Environmental Protection withholds testing for certain chemicals that may be tied to shale gas drilling.
Performance comparison of SVM and ANN for aerobic granular sludgejournalBEEI
To comply with growing demand for high effluent quality of Domestic Wastewater Treatment Plant (WWTP), a simple and reliable prediction model is thus needed. The wastewater treatment technology considered in this paper is an Aerobic Granular Sludge (AGS). The AGS systems are fundamentally complex due to uncertainty and non-linearity of the system makes it hard to predict. This paper presents model predictions and optimization as a tool in predicting the performance of the AGS. The input-output data used in model prediction are (COD, TN, TP, AN, and MLSS). After feature analysis, the prediction of the models using Support Vector Machine (SVM) and Feed-Forward Neural Network (FFNN) are developed and compared. The simulation of the model uses the experimental data obtained from Sequencing Batch Reactor under hot temperature of 50˚C. The simulation results indicated that the SVM is preferable to FFNN and it can provide a useful tool in predicting the effluent quality of WWTP.
Design and Construction of a Simple and Reliable Temperature Control Viscomet...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.
Environmental Monitoring Model of Health, Parasitological, And Colorimetric C...theijes
The sanitary quality of water was evaluated in two micro basins, Bacaxá and Capivari belonging to the Lakes Basin St. John in the state of Rio de Janeiro, Brazil, for colimetric and parasitological analysis. Analyses were performed seasonally over a year and the levels of Escherichia coli were within the recommended only in the summer of 2012 and fall, and inappropriate with levels above recommended in winter, spring and summer of 2013 in both the micro basins. Through our observations, we compare the average values of the levels of total coliforms and Escherichia coli between both rivers. Initially, the samples indicate a similarity between the distributions of coliforms and Escherichia coli. However, Mann-Whitney-Wilcoxon test samples indicate that the distributions are different. In parasitological analysis it was observed that in Capivari was detected a greater presence of filarial larvae. Anthropogenic influences mainly by the presence of sewage is being able to compromise the health quality of the micro basins studied carrying a significant pollutant load to the Juturnaíba reservoir. The monitoring of the sanitary quality of the watersheds that supply the population may indicate when it is necessary to adopt more effective measures in the treatment of water supply of cities.
Assessment of mortality and morbidity risks due to the consumption of some sa...theijes
Natural radioactivity of sixty sachet waters produced by fifteen different enterprises was measured by gamma spectrometry technique. The concentrations of the main natural radionuclides, 40K, 226Ra, and 232Th in the samples varied respectively from 0.87 to 5.70 Bq/L, 0.16 to 0.47 Bq/L and 0.17 to 0.60 Bq/L in the samples with mean values respective of 2.66± 0.60 Bq/L, 0.22± 0.65 Bq/L and 0.34± 0.07 Bq/L. The annual effective doses due to the ingestion of these radionuclides varied from 45.48 to113.07 μSv/y with a mean of 78.41± 15.51 μSv/y. The mortality and morbidity risks assessed in samples, varied respectively from 4.94 10-5 to 1.17 10-4 and 7.20 10-5 to 1.24 10-4 with average values of 6.75 10-5 and 9.84 10-5 . This study showed a morbidity risk relatively high, thus harmful for the population.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Statistical Analysis of Ground Water Quality in Rural Areas of Uttar Pradesh ...IJERA Editor
The importance of groundwater for the existence of human society cannot be exaggerated. Groundwater is the
major source of water in both rural and urban India.Duringlast decade, it was observed that ground water get
polluted drastically and hence, resulted into many water borne diseases which is a cause of many health hazards.
In this paper an attempt has been made to test groundwater quality of different villages of Uttar Pradesh, India
on the basis of thirteen parameters like pH, total dissolved solids, conductivity, total hardness, biological oxygen
demand etc. The results obtained were compared with the BIS (IS 10500:1991) Permissible Standards for
drinking water. Normal Distribution analysis was applied to describe various characteristics of the samples
collected and Correlation Analysiswas done on the samples which measured the strength of association between
twowaterparameters.On the basis of results obtained from analytical and statistical analysis, it was revealed that
all the water sources chosen for study are not suitable for the utilization of water.
Data mining is the knowledge discovery in databases and the gaol is to extract patterns and knowledge from
large amounts of data. The important term in data mining is text mining. Text mining extracts the quality
information highly from text. Statistical pattern learning is used to high quality information. High –quality in
text mining defines the combinations of relevance, novelty and interestingness. Tasks in text mining are text
categorization, text clustering, entity extraction and sentiment analysis. Applications of natural language
processing and analytical methods are highly preferred to turn
DISSECT AND ENRICH DIVIDE AND RULE SCHEME FOR WIRELESS SENSOR NETWORK TO SOLV...ijsc
In remote sensor system, sensor hubs have the restricted battery power, so to use the vitality in a more
productive manner a few creator's created a few strategies, yet at the same time there is have to decrease
the vitality utilization of hubs. In this paper, we presented another method called as 'Partition and Rule
strategy' to unravel the scope dividing so as to open the system field into subfield and next maintain a
strategic distance from the vitality gap issue with the assistance of static bunching. Essentially, in gap and
run plan system range is isolated into three locales to be specific internal, center and external to conquer
the issue of vitality utilization. We execute this work in NS-2 and our recreation results demonstrate that
our system is far superior than old procedures.
FUZZY-CLUSTERING BASED DATA GATHERING IN WIRELESS SENSOR NETWORK ijsc
Wireless Sensor Networks (WSN) is spatially distributed, collection of sensor nodes for the purpose of
monitoring physical or environmental conditions, such as temperature, sound, pressure, etc. and to
cooperatively pass their data through the network to a base station. The critical challenge is to minimize
the energy consumption in data gathering and forwarding from sensor nodes to the sink. Cluster based
data aggregation is one of the most popular communication protocols in this field. Clustering is an
important procedure for extending the network lifetime in wireless sensor networks. Cluster Heads (CH)
aggregate data from relevant cluster nodes and send it to the base station. A main challenge in WSNs is to
select suitable CHs. Another communication protocol is based on a tree construction. In this protocol,
energy consumption is low because there are short paths between the sensors. In this paper, Dynamic
Fuzzy Clustering data aggregation is introduced. This approach is based on clustering and minimum
spanning tree. The proposed method initially uses fuzzy decision making approach for the selection of CHs.
Afterward a minimum spanning tree is constructed based on CHs. CHs are selected efficiently and
accurately. The combining clustering and tree structure is reclaiming the advantages of the previous
structures. Our method is compared to the well-known data aggregation methods, in terms of energy
consumption and the amount of energy residuary in each sensor network lifetime. Our method decreases
energy consumption of each node. When the best CHs selected and the minimum spanning tree is formed by
the best CHs, the remaining energy of the nodes will be preserved. Node lifetime has an important role in
WSN. Using our proposed data aggregation algorithm, survival of the network is improved
Kurumsal müşterilerimize ihtiyaçları doğrultusunda tasarımcı ağımızla özgün, trendlere uygun, kullanışlı hediyelik tasarım ürünler sunuyoruz. Bu çözümler ile kurumsal müşterilerimizin müşterileri, iş birlikçileri, tedarikçileri ve çalışanları ile ilişkilerini güçlendirmesini sağlıyoruz.
EDGE PRESERVATION OF ENHANCED FUZZY MEDIAN MEAN FILTER USING DECISION BASED M...ijsc
Image noise refers to random variations in the basic characteristics of image like brightness, intensity or
color difference. These variations are not present in the image which is captured but may occur due to
environmental conditions like sensor temperature or due to circuit of the scanner or other similar issues.
Basically noise means unwanted signals in the image. Various filters have been designed for removal of
almost all types of noise. It has been seen in most of the cases that as a result of high amount of filtering or
repetitive filtering of image for the removal of noise, edges of images mostly get distorted or smeared out. It
means that most of the filtering techniques lead to loss of fine edges of the images which needs to be
preserved in order to enhance the quality of image. This paper has focused on to improve the enhanced
fuzzy median mean filter so that fine edges get preserved in a better way. Experiments have been performed
in MATLAB. Comparative analysis have been done on the basis of PSNR, MSE, BER and RMSE and it has
shown that border correction applied on images improves the results of enhanced fuzzy median mean filter.
Power generation today is an increasingly demanding task, worldwide, because of emphasis on
efficient ways of generation. A power station is a complicated multivariable controlled plant, which
consists of boiler, turbine, generator, power network and loads. The power sector sustainability depends
on innovative technology and practices in maintaining unit performance, operation, flexibility and
availability . The demands being placed on Control & Instrumentation engineers include economic
optimization, practical methods for adaptive and learning control, software tools that place state-of-art
methods . As a result, Fuzzy techniques are explored which aim to exploit tolerance for imprecision,
uncertainty, and partial truth to achieve robustness, tractability, and low cost. This paper proposes use of
fuzzy techniques in two critical areas of Soot Blowing optimization and Drum Level Control.
Presently, in most of the Power stations the soot blowing is done based on a fixed time schedule. In many
instances, certain boiler stages are blown unnecessarily, resulting in efficiency loss. Therefore an fuzzy
based system is proposed which shall indicate individual section cleanliness to determine correct soot
blowing scheme. Practical soot blowing optimization improves boiler performance, reduces NOx emissions
and minimizes disturbances caused by soot blower activation. Due to the dynamic behaviour of power
plant, controlling the Drum Level is critical. If the level becomes too low, the boiler can run dry resulting
in mechanical damage of the drum and boiler tubes. If the level becomes too high, water can be carried
over into the Steam Turbine which shall result in catastrophic damage. Therefore an fuzzy based system is
proposed to replace the existing conventional controllers
Safety through design is a critical consideration in oven and furnace manufacturing. This slideshow takes a look at industry standards governing their design and identifies common safety oversights.
This work considers the multi-objective optimization problem constrained by a system of bipolar fuzzy relational equations with max-product composition. An integer optimization based technique for order of preference by similarity to the ideal solution is proposed for solving such a problem. Some critical features associated with the feasible domain and optimal solutions of the bipolar max-Tp equation constrained optimization problem are studied. An illustrative example verifying the idea of this paper is included. This
is the first attempt to study the bipolar max-T equation constrained multi-objective optimization problems
from an integer programming viewpoint.
FACE SKETCH GENERATION USING EVOLUTIONARY COMPUTING ijsc
In this paper, an evolutionary genetic algorithm is used to generate face sketch from the face description. Face sketch generation without face image is extremely important for the law enforcement agencies. The genetic algorithm is used for generating face sketch through several iterations of the lgorithm. The face image description is captured through graphical user interface just by clicking options for each face
features. Face features are used to extract face images and generate initial population for the genetic algorithm. Genetic operators such as selection, crossover and mutation are used for next generation of the population. The Genetic algorithm cycle is repeated until the user is satisfied with face sketch generated. The novelty of the paper includes face sketch generation from face image description. The result shows that
evolutionary based technique for sketch generation produces the desired face sketch.
Some slip, trip and fall hazards are obvious, but many are
difficult to spot. Each of the following pictures have at least
one slipping or tripping hazard. Can you spot them all?
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Hydrological Calibration in the Mount Lofty Ranges using Source Paramenter Es...eWater
The catchments of the Mount Lofty Ranges (MLR) provide a crucial water resource for the rural and urban community of Adelaide. The Source Catchments model is one planning tool used to assess current and future water, sediment and nutrient yields from the catchment. The modelling is critical for future planning to ensure a safe and reliable water supply is maintained.
Linking PEST and Source Catchments for hydrology calibration was an efficient and repeatable method for calibration. Future work to improve the process could include calibrating with other rainfall runoff models such as Sacramento which better account for groundwater losses or explicit representation of groundwater in the model.
This project has focused solely on rainfall and runoff. It has not considered how external impacts (e.g. climate change) may change catchment hydrological characteristics or constituent characteristics such as EMC/DWC values. Future projects through the Goyder Water Research Institute aim to address some of these
issues. This project may produce data which will inform further work in this respect.
Statistical analysis to identify the main parameters to effecting wwqi of sew...eSAT Journals
Abstract The present study was conducted to determine the wastewater quality index and to study statistical interrelationships amongst different parameters. The equation was developed to predict BOD and WWQI. A number of water quality physicochemical parameters were estimated quantitatively in wastewater samples following methods and procedures as per governing authority guidelines. Wastewater Quality Index (WWQI) is regarded as one of the most effective way to communicate wastewater quality in a collective way regarding wastewater quality parameters. The WWQI of wastewater samples was calculated with fuzzy MCDM methodology. The wastewater quality index for treated wastewater was evaluated considering eight parameters subscribed by Gujarat Pollution control Board (GPCB), a governing authority for environmental monitoring in Gujarat State, India. Considerable uncertainties are involved in the process of defining the treated wastewater quality for specific usage, like irrigation, reuse, etc.
The paper presents modeling of cognitive uncertainty in the field data, while dealing with these systems recourse to fuzzy logic. Also a statistical study is done to identify the main affecting variables to the WWQI. The Statistical Regression Analysis has been found to be highly useful tool for correlating different parameters. Correlation Analysis of the data suggests that TDS, SS, BOD, COD, O&G and Cl are significantly correlated with WWQI and DO of wastewater. The estimated BOD from independent variance DO for maximum, minimum and average is 25.35 mg/L, 2.65 mg/L and 13.56 mg/L respectively. While estimated WWQI from independent variance DO for maximum, minimum and average is 0.6212, 0.3074 and 0.4581 respectively. Out of eight parameters, TDS-BOD, TDS-COD, TDS-Cl, SS-BOD, SS-COD, and BOD-COD are significantly correlated. Present study shows that WWQI is influenced by BOD, COD, SS and TDS.
Variance of total dissolved solids and electrical conductivity for water qual...IJECEIAES
Water pollution is one of the most serious environmental problems in Malaysia. The most notable occurrence of pollution happened in Selangor. Currently, there are various water quality monitoring (WQM) methods to observe the quality of water. One of the methods used is the internet of things (IoT) for wireless sensor network technology to obtain real-time data measurement. In this study, the developed WQM system is equipped with a sensor that can measure total dissolved solid (TDS) and electrical conductivity (EC). Arduino UNO was used in this system as a microcontroller to interact with the sensor. The Wi-Fi module, ESP8266, was used to transfer the collected data to ThingSpeak, which acts as a cloud to store all the data. The results showed that both sample populations can be discriminated since the p-value is greater than 0.05 in the normality test, while in the paired sample t-test, the p-value is less than 0.05. In conclusion, this research provides an easier way to monitor water quality by taking up less time at less cost, as well as being reliable in giving real-time data reading.
Performance assessment of water filtration plants in pakistan - JBESInnspub Net
A study was carried out to evaluate the water quality of filtration plants installed at six different places of Cantonment Board Sialkot, Pakistan to suggest and recommend guidelines for their improvement. Water samples from six Treatment plants and their respective twelve connections (two from each treatment plants) were collected before and after treatment. In this way, total samples were collected and tested. Values of these samples before and after treatment were used for comparison with World Health Organization (WHO) guidelines for drinking water standards. Thirty three parameters including physical, chemical and bacteriological were determined for each sample. The results were satisfactory both chemically and bacteriologically according to WHO guidelines for water quality of treatment plants. The results showed that the samples of water were fit, both before and after treatment plant except for water sample of treatment plant No. IV & V (Before treatment). Total and faecal coliform were found in these samples. Various causes of faecal contamination before treatment may be due to leakage of pipelines, operation at tubewells, layout of freshwater pipes parallel or beneath the sewerage pipes or channels. Disinfection of water at source is recommended to deal with the faecal contamination; otherwise there is no need of filtration plant.
Remote sensing data driven bathing water quality assessment using sentinel-3nooriasukmaningtyas
In this paper we are investigating the possibility of usage of remote sensing satellite data, more precisely sentinel-3 OLCI and SLSTR data, for assessment of bathing water quality. In this research we used data driven approach and analysis of data in order to pinpoint aspects of remote sensing data that can be useful for bathing water quality assessment. For this purpose we collected satellite images for period from start of June till end of September of 2019 and results of in-situ measurement for the same period . Results of in-situ measurement were correlated with satellite images bands and analyzed. We propose a simple method for rapid assessment of possible deterioration of bathing water quality to be used by public health authorities for better planning of in situ measurements. Results of implementation of predictive models based on k-nearest neighbour (KNN) and decision tree (DT) are described.
A novel fuzzy rule based system for assessment of ground water potability: A ...IOSR Journals
Abstract: Groundwater is an important water resource for domestic, irrigation, and industrial needs. The most
widely exploited use of this resource is for consumption. Assessment of potability of any ground water samples
is a non-trivial task. A new fuzzy rule based system has been proposed to assess the quality of ground-water
samples collected from the bore-wells across 24 districts of Karnataka (South India). Eight groundwater quality
salts parameters are selected for water quality analysis. A membership function for the fuzzy rule based system
for each salt is developed and the weights for each parameter was calculated using Analytic Hierarchy Process
(AHP) that relies on pair wise comparison. The system showed that out of 24 districts of Karnataka state,
ground water from 51.78% bore-wells was not feasible for consumption.
Keywords: Groundwater quality, Fuzzy rule based system
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
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An efficient method for assessing water
1. International Journal on Soft Computing (IJSC) Vol. 5, No. 2, May 2014
DOI: 10.5121/ijsc.2014.5203 21
AN EFFICIENT METHOD FOR ASSESSING WATER
QUALITY BASED ON BAYESIAN BELIEF NETWORKS
Khalil Shihab1
and Nida Al-Chalabi2
1
College of Engineering & Science, Victoria University, Australia
2
Department of Computer Science, SQU, Oman
ABSTRACT
A new methodology is developed to analyse existing water quality monitoring networks. This methodology
incorporates different aspects of monitoring, including vulnerability/probability assessment, environmental
health risk, the value of information, and redundancy reduction. The work starts with a formulation of a
conceptual framework for groundwater quality monitoring to represent the methodology’s context. This
work presents the development of Bayesian techniques for the assessment of groundwater quality. The
primary aim is to develop a predictive model and a computer system to assess and predict the impact of
pollutants on the water column. The process of the analysis begins by postulating a model in light of all
available knowledge taken from relevant phenomenon. The previous knowledge as represented by the prior
distribution of the model parameters is then combined with the new data through Bayes’ theorem to yield
the current knowledge represented by the posterior distribution of model parameters. This process of
updating information about the unknown model parameters is then repeated in a sequential manner as
more and more new information becomes available.
KEYWORDS
Bayesian Belief Networks, Water Quality Assessment, Data Mining
1. INTRODUCTION
Water is an essential requirement for irrigated agriculture, domestic uses, including drinking,
cooking and sanitation. Declining surface and groundwater quality is regarded as the most serious
and persistent issue and has become as a global issue effecting the people and the ecosystem.
Anthropogenic sources of pollution such as agriculture, industry, and municipal waste, contribute
to the degradation of groundwater quality, which may limit the use of these resources and lead to
health-risk consequences. There are many observable factors contributing to the deterioration of
water quality. These factors need to be monitored and their maximum allowable limits need to be
determined. Decline in water quality is manifested in a number of ways, for example, elevated
nutrient levels, acid from mines, domestic and oil spill, wastes from distilleries and factories, salt
water intrusion and temperature. These factors and others will provide the input data for our
computer system. Efficient water management relies upon efficient monitoring systems that have
the capability to provide information that are decision relevant. Unfortunately, existing
monitoring systems do not always fulfil this objective, where many monitoring systems are
designed to gather data that are redundant and do not add decision-relevant information of value.
Therefore, the needs to acquire data that are decision-relevant, and efficient, establish a need for
the development of cost-effective and flexible analytical methodology for water quality
monitoring networks. Recent attempts based on Artificial Intelligence (AI) were first applied to
the interpretation of biomonitoring data. Other works were based on pattern recognition using
artificial neural networks (NNs). In particular, the supervised learning machines have also been
used in water resources management applications, which have been drawn more attention in the
2. International Journal on Soft Computing (IJSC) Vol. 5, No. 2, May 2014
22
research literature. For instance, the relevance vector machine RVM model has been used in
hydrological applications and groundwater quality modelling showed good results. However,
these methods ignore the dependencies between water quality variables.
Therefore, a Bayesian reasoning approach is employed here. This approach incorporates prior
knowledge about the possible state of a system, and adds new data in a pre-posterior analysis to
produce posterior knowledge of full information about possible system states [1-3]. This approach
enables a comprehensive evaluation of water quality variables and allows establishing public
health concepts. Bayesian methods of statistical inference offer the greatest potential for
groundwater monitoring. This is because these methods can be used to recognize the variability
arising from three different sources of errors, namely, analytical test errors, sampling errors and
time errors, in addition to the variability in the true concentration [4, 5]. The Bayesian methods
can also be used to significantly increase the precision and the accuracy of the test methods used
in a given environmental laboratory [6, 7]. The mobility of salt and other pollutants in steady state
and transient environmental conditions can be predicted by applying Bayesian models to a range
of spatial and temporal scales under varying environmental conditions. Bayesian networks use
statistical techniques that tolerate subjectivity and small data sets. Furthermore, these methods are
simple to apply and have sufficient flexibility to allow reaction to scientific complexity free from
impediment from purely technical limitations [8].
The process of Bayesian analysis begins by postulating a model in light of all available
knowledge taken from relevant phenomenon [9]. The previous knowledge as represented by the
prior distribution of the model parameters is then combined with the new data through Bayes’
theorem to yield the current knowledge (represented by the posterior distribution of model
parameters). This process of updating information about the unknown model parameters is then
repeated in a sequential manner as more and more new information becomes available. In this
work, we studied the Salalah area of Oman because the groundwater has been an important
natural resource and the only available water source other than the seasonal rainfall.
Groundwater quality and pollution are determined and measured by comparing physical,
chemical, biological, microbiological, and radiological quantities and parameters to a set of
standards and criteria. A criterion is basically a scientific quantity upon which a judgment can be
based [10, 11]. In this work, however, we considered only the chemical parameters: total
dissolved solids (TDS), electrical conductivity (EC) and water pH.
2. UNCERTAINTY ANALYSIS]
The Ministry of Water Resources (MWR) maintains data on the concentration of the harmful
substances in the groundwater at Taqah monitoring sites, which are located to the south of the
Sultanate of Oman, in the Salalah plain [12, 13]. We observed that good quality data were
obtained from several monitoring wells in this region. Because of the lack of monitoring wells in
certain areas in that region, we filled in the missing measurements with data obtained from Oman
Mining Company (OMCO) and Ministry of Environmental and Regional Municipalities (MRME)
[14].
Data for water quality assessment are normally collected from various monitoring wells and then
analyzed in environmental laboratories in order to measure the concentration of a number of
water quality constituents. We realized that the methods used by these laboratories do not
emphasize accuracy. There is a lack of awareness among both laboratory and validation personnel
regarding the possibility of false positives in environmental data. In order to overcome this
problem and to have representative data, we, therefore, used the following modified Bayesian
model to that developed by Banerjee, Planting and Ramirez [10], to preprocessing the datasets
used for the development of the Bayesian Networks.
3. International Journal on Soft Computing (IJSC) Vol. 5, No. 2, May 2014
23
2.1. Bayesian Models
The formulation of the model is as follows:
Let S denote a particular hazardous constituent of interest. Since the concentration of the
substance may vary from well to another, it is necessary to consider each well separately. Let xt=
(xt1, xt2, xt3, xtm) be the vector of m measurements of the concentration of S in m distinct water
samples from a given well at a given sampling occasion where (m>=1) and (t=1, 2, . . .). Each
measurement consists of the true concentration of S plus an error.
Let Xt be the true concentration of S in the groundwater at sampling occasion t. If we assume that
the true concentration Xt is unknown and is a random variable, the model evaluates the posterior
distribution of Xt given the sample measurements xt at sampling occasion t.
Using the normality assumption and given Xt = xt and δ2
, the concentration measurements in xt
represent a random sample of size m for random distribution with mean xt and variance δ2
.
We assume that the parameters xt and δ2
of the normal distribution are random variables with
certain prior probability distribution. Therefore, the model for prior distribution of Xt and δ2
can
be presented as follows:
For t =1, 2… and given δ2
the conditional distribution of Xt at sampling occasion t is a normal
distribution with mean μt-1 and variance δ2
t-1 δ2
. The marginal distribution of δ2
is an inverted
gamma distribution with parameter βt-1 and νt-1.
This model uses the following prior distribution, which represents the concentration
measurements before the first sampling.
The pdf of the prior distribution of X0 is:
2)12(2
0
0
0
00
0
00
0
2
1
1)(
+−
−
+=
v
v
x
v
xf
(2.1)
which is the pdf of the student’s t-distribution with 2v0 degrees of freedom, location parameters
μ0 and variance δ0
2
β0/ν0.
Now suppose that the observations are available on the concentration of S, given the sample Xt
the posterior marginal distribution of Xt is a student’s t-distribution with 2vt degree of freedom,
location parameters μt and variance δt
βt/νt where the pdf has the form:
2)12(2
2
1
1)/(
+−
−
+=
tv
ttt
tt
t
tt
x
v
xxf
(2.2)
where:
[ ])1(2/)(2/)( 2
11
1
1 −−
=
− +−+−+= ∑ ttt
m
j
tjtt mxmxx
2/1 mvv tt += −
4. International Journal on Soft Computing (IJSC) Vol. 5, No. 2, May 2014
24
2
1
2
11 1/()( −−− ++= ttttt mxm ) (2.3)
)1/( 2
1
2
1
2
−− += ttt m
mxx
m
j
tjt /
1
∑=
=
It is obvious from the equation of μt the sequential nature of this posterior distribution. Therefore,
in order to present the true unknown concentration of the substance S in the well under
consideration, it is frequently more convenient to put a range (or interval) which contains most of
the posterior probability. Such intervals are called highest posterior density (HPD) intervals. Thus
for a given probability content of (1-α), 0< α<1, a 100(1- α) percent HPD interval for Xt, is given
by:
tttvt t
t )2/(2±
(2.4)
when t2vt(α/2) is the 100(1- α/2) percentile of the student’s t-distribution with 2vt degree of
freedom.
2.2. Bayesian Algorithm
In brief, the monitoring algorithm, which is based on the Bayesian model, is as follows:
(1) Fix a value of α (0< α <1) based on the desired confidence level. In this case, we chose α
to be 0.01.
(2) Since we do not have enough data to work with, we used the same parameters of the prior
distribution used in the model of Banerjee, Plantinga and Ramirez. These parameters are :
β0= 0.0073 , ν0=2.336 , μ0= 9.53 , δ0
2
=3056.34
(3) At each sampling occasion t , ( t= 1,2,...), compute the parameters βt , νt , μt and δt of the
posterior distribution Xt given the set of observations in xt on the concentration of S
available from a given well in a given site using (2.3). Compute LHPD and UHPD using
these parameter estimates and (2.4).
(4) Plot μt, LHPD, and UHPD that are obtained in step 3 above against sampling occasion t.
(5) For the next sampling occasion, update the values of the parameters βt, νt, μt and δt using
(2.3) and the datasets just obtained. Recomputed LHPD, and UHPD using the updated
parameter values in (2.4) and repeat step 4 above.
Some of these datasets needed to be scaled down using the following normalization technique:
−
=
x
x
, where
nxx
n
i
i∑=
, and 1
22
−
−
=
∑
n
xnx
n
i
i
2.3. Implementation
The pre-processing system is implemented on PC platform using Visual Basic programming
language.
Table 1 presents the concentration data for TDS (Total Dissolved Solids) for Well 001/577 in the
Taqah area. In particular, the table shows the true concentration data for TDS produced by our
pre-processing system.
5. International Journal on Soft Computing (IJSC) Vol. 5, No. 2, May 2014
25
Table 1. Concentration Data of TDS for Well001/577 in the Salalah plain, where OC stands for Observed
Concentration and ETC stands for Expected True Concentration.
Te OC LHPD ETC UHPD
84 1.147 0.85 1.15 1.45
85 1.106 1 1.13 1.26
86 1.938 1.12 1.4 1.68
87 2.237 1.33 1.61 1.88
88 3.857 1.6 2.06 2.52
89 3.834 1.91 2.35 2.79
90 3.957 2.18 2.58 2.98
91 3.761 2.38 2.73 3.08
92 4.3 2.58 2.9 3.23
93 3.958 2.72 3.01 3.3
94 1 2.54 2.83 3.11
95 3.714 2.64 2.9 3.16
96 3.65 2.73 2.96 3.19
97 3.381 2.78 2.99 3.2
98 3.396 2.83 3.02 3.2
99 3.477 2.87 3.04 3.22
00 3.498 2.91 3.07 3.23
01 3.23 2.93 3.08 3.23
02 3.243 2.95 3.09 3.22
03 3.267 2.97 3.1 3.22
04 3.297 2.99 3.11 3.22
3. BAYESIAN NETWORKS
After the pre-processing stage, we constructed a Bayesian Network (BN) by using the Hugin
system. We then used this BN as an initial building network for the construction of two Dynamic
Bayesian Networks in order to predict the impact of pollution on groundwater quality [15, 16].
3.1. Dynamic Bayesian Networks (DBNs)
DBNs extend Bayesian Networks from static domains to dynamic domains [17, 18]. This is
achieved by introducing relevant temporal dependencies between the representations of the static
network at different times.
The main characteristic of DBNs is as follows:
Let Xt be the state of the system at time t, and assume that
(1) The process is Markovian, i.e.,
P(Xt/X0, X1, . . ., Xt-1)= P(Xt/Xt-1)
(2) The process is stationary or time-invariant, i.e.,
P(Xt/Xt-1) is the same for every t.
6. International Journal on Soft Computing (IJSC) Vol. 5, No. 2, May 2014
26
Therefore, we just need P(X0), which is a static Bayesian network (BN), and P(Xt/Xt-1), which is a
network fragment, where the variables in Xt-1 have no parents, in order to have a Dynamic
Bayesian Network (DBN).
3.2. Bayesian Networks Development
Among more than twenty wells in the Taqah area, we selected only four wells for this study.
Those four wells have had, to the greatest extent, complete data measurements and provide
sufficient information for the assessment of the groundwater quality for this area.
The electrical conductivity (EC) of the water has been used as a measure for the salinity hazard of
the groundwater used for irrigation in the Salalah plain. The total dissolved solid (TDS) limit is
600 mg/L, which is the objective of the current plan of the MWR. TDS contains several dissolved
solids but 90% of its concentration is made up of six constituents. These are: sodium Na,
magnesium Mg, calcium Ca, chloride Cl, bicarbonate HCO3 and sulfate SO4. We, therefore,
considered only these elements in the calculation of TDS.
We also used the following relationship between TDS and EC.
TDS = A * EC; where A is a constant with value between 0.65 and 0.77.
Both TDS and EC can affect water acidity or water pH. Solute chemical constituents are variable
in high concentration at lower pH (higher acidity). On the other hand, acidity allows migration of
hydrogen ions (H+), which is an indication of conductivity. Therefore, our work concentrated on
the following relations.
TDS EC, EC pH, TDS pH
Reaching to these relations we used two learning approaches to construct and parameterize a
simple static BN that have three nodes, each node represents a groundwater quality constituent
(TDS, EC or pH). Learning basically consists of two different components: 1) learning the
network structure, 2) learning the conditional probability distributions.
For the first component, we used the Hugin system that supports structure and parameter learning
in Bayesian networks. We also developed a program written in C++ to generate the conditional
probabilities for TDS, EC and pH using Table 2 as input.
Once the static BN model (static model) for each monitoring well was built, parameterized and
tested, we used these models as initial building networks in the construction of OOBNs. Figure 1
models the time slices for each well characterizing the temporal nature of identical model
structures, where the initial building network, see Figure 2, describes a generic time-sliced
network.
7. International Journal on Soft Computing (IJSC) Vol. 5, No. 2, May 2014
27
Table 2. TDS, EC, and pH data for the well Well 001/577.
Yr TDS
mg/L
EC
µS/cm
pH
84 542.7 548 7.85
85 525.5 548 7.8
86 565.4 579 7.75
87 604.2 588 7.57
88 541.8 601 7.43
89 565.9 625 7.34
90 558.6 638 7.32
91 640.4 798 7.27
92 754.5 739 7.24
93 798.7 758 7.28
94 746.4 799 7.29
95 615.8 514 7.3
96 737.5 619 7.28
97 753.6 869 7.19
98 935.6 558 7.15
99 1174 855 7.15
0 1021 796 7.06
1 1067 855 6.98
2 1223 844 6.94
3 1055 881 6.9
Figure 1. The OOBN representing three time-sliced networks
8. International Journal on Soft Computing (IJSC) Vol. 5, No. 2, May 2014
28
Figure 2. The initial building block representing one time-sliced network
Since our Bayesian Networks are tractable models, we also implemented the exact inference for
the network described in Figure 8 and compared the results with that produced by OOBN. Figures
3 and 4 show the KL-divergence between the true and the approximate distribution [10]. Since
the KL-distance converges to zero, this is an indication of the accuracy and reliability of OOBN
Figure 3. True and approximate probability distributions.
Figure 4. The KL-divergence between the true and the
estimate distributions over all variables.
4. USING CLASSICAL TIME SERIES FOR THE ASSESSMENT OF
GROUNDWATER QUALITY
The purpose of this section is to apply the classical time series analysis to groundwater quality
data and to compare the results with that obtained by the application of Dynamic Bayesian
Networks (DBNs). The continuous and regular monitoring data of electrical conductivity (EC),
total dissolved solid (TDS), pH measured by the Ministry of Water Resources (MWR) were also
used here for the time series analysis.
Time series analyses of water supply wells with respect to the concentration of chemical
constituents are presented in Figures 5-10.
9. International Journal on Soft Computing (IJSC) Vol. 5, No. 2, May 2014
29
Total dissolved solids (TDS) are a measure of the dissolved minerals in water and also a measure
of drinking water quality. There is a secondary drinking water standard of 500 milligrams per liter
(mg/L) TDS; water exceeding this level tastes salty. Groundwater with TDS levels greater than
1500 mg/L is considered too saline to be a good source of drinking water. Figure 5 shows the
concentration of TDS for the well Well001/577 for a period of twenty one years.
The fluctuation of the concentration of the chloride (Cl), sodium (Na), and calcium (Ca) with
respect to time is shown in Figure 7. The values were averaged during the initial analysis as there
were no significant differences among the monthly data. Chloride values above 250 mg/l give a
slight salty taste to water which is objectionable by many people.
Relationships between TDS, EC and pH are examined using multiple regression analysis, see
Figure 7. Multiple regression analysis is used to explain as much variation observed in the
response variable as possible, while minimizing unexplained variation from “noise”. The results
of this analysis are used to produce the moving average chart, Figure 9, and the linear regression
chart, Figure 10. We used Excel Business Tools [19], Microsoft Excel, and Matlab for producing
these and other charts.
y = 3 3.9 2 8x + 4 1 2.5 3
R 2
= 0 .8 2 2 3
0
2 0 0
4 0 0
6 0 0
8 0 0
1 0 0 0
1 2 0 0
1 4 0 0
1 9 8 4
1 9 8 6
1 9 8 8
1 9 9 0
1 9 9 2
1 9 9 4
1 9 9 6
1 9 9 8
2 0 0 0
2 0 0 2
2 0 0 4
mg/l
T D S
L in e a r
( T D S )
Figure 5. Fluctuation of TDS concentration for the well Well001/577
y = 0 .0 1 2x - 2 1.1 0 5
R 2
= 0 .5 1 8 3
2 .5 5
2 .6 0
2 .6 5
2 .7 0
2 .7 5
2 .8 0
2 .8 5
2 .9 0
2 .9 5
1 9 8 4 1 9 8 9 1 9 9 4 1 9 9 9 2 0 0 4 2 0 0 9
EC
Figure 6. EC concentration is poorly represented for the well Well001/577
10. International Journal on Soft Computing (IJSC) Vol. 5, No. 2, May 2014
30
0
50
100
150
200
250
300
350
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
mg/l
M g
So4
N a
C a
K
C l
Figure 7. Fluctuation of the concentration of the major chemical constituents for Well001/577 for a period
of 21 years
Figure 8. Excel templates for financial analysis and business productivity
from Excel Business Tools
As is shown in Figure 7 that the trend is as follows:
TrendWQ=19.01*TDS - 5.42*EC -270.16*pH + 205.14
5 . 0 0
0 . 0 0
5 . 0 0
1 0 . 0 0
1 5 . 0 0
2 0 . 0 0
2 5 . 0 0
1 9 8 0 1 9 8 5 1 9 9 0 1 9 9 5 2 0 0 0 2 0 0 5
Y e a r
Trend
Figure 9. Moving average chart of 2-year period for groundwater quality trend
Figure 10. A curve fitting chart showing groundwater quality trend over time
11. International Journal on Soft Computing (IJSC) Vol. 5, No. 2, May 2014
31
Figure 9 shows the groundwater quality trend over time (linear regression). The trend has the
following properties:
Linear model Poly1:
f(x) = p1*x + p2
Coefficients (with 95% confidence bounds):
p1 = 0.8954 (0.7962, 0.9947)
p2 = 1.332 (0.08589, 2.579)
Goodness of fit:
SSE: 32.91
R-square: 0.9494
Adjusted R-square: 0.9467
RMSE: 1.316
Although the classical time series models are used here to assess the presence and strength of
temporal patterns of groundwater quality. These models are based on the assumption of stationary
(i.e. time invariant). They have been widely used in many domains such as financial data and
weather forecasting. Yet these models do not readily adapt to domains with dynamically changing
model characteristics, as is the case with groundwater quality assessment. In addition to the above
mentioned assumption, the classical models are restricted in their ability to represent the general
probabilistic dependency among the domain variables and they fail to incorporate prior
knowledge.
The observed groundwater quality data are irregularly spaced and not predetermined as in the
case with ordinary time series. This may cause the traditional time series techniques to be
ineffective (Prediction: what is the predicted value for one period a head). It is evident that the
time series casts doubts on the positive or negative effects of any chemical constituent on the
groundwater quality for the long run, and is thus not as clear and reliable as in the case of using
Dynamic Bayesian Techniques. While some groundwater quality constituents, such as chloride
and TDS, show an increasing trend, the other constituents, such as pH, Mg, and SO4 do not
demonstrate obvious trends. Therefore, we can draw a reliable conclusion on the cause of the
increasing trend of the groundwater quality and we cannot investigate the effect of the increasing
or decreasing other constituents, such as pH and EC. In addition to this ignorance of the cause-
effect relationships, classical time series models assume the linearity in the relationships among
variables and normality of their probability distributions.
5. CONCLUSION AND FURTHER WORK
This work presents the assessment of groundwater quality. Bayesian methods have been
investigated and shown to offer considerable potential for use in groundwater quality prediction.
These methods are based on reasoning under conditions of uncertainty. This work is the first step
towards having a comprehensive network that contains the other variables that are considered by
the researchers significant for the assessment of groundwater quality in the Salalah plain in
particular.
Also we showed that the classical time series models do not readily adapt to domains with
dynamically changing model characteristics, as is the case with groundwater quality assessment.
This is mainly because these models are restricted in their ability to represent the general
12. International Journal on Soft Computing (IJSC) Vol. 5, No. 2, May 2014
32
probabilistic dependency among the domain variables and they fail to incorporate prior
knowledge.
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