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.
Farsi character recognition using new hybrid feature extraction methodsijcseit
Identification of visual words and writings has long been one of the most essential and the most attractive
operations in the field of image processing which has been studied since the last few decades and includes
security, traffic control, fields of psychology, medicine, and engineering, etc. Previous techniques in the
field of identification of visual writings are very similar to each other for the most parts of their analysis,
and depending on the needs of the operational field have presented different feature extraction. Changes in
style of writing and font and turns of words and other issues are challenges of characters identifying
activity. In this study, a system of Persian character identification using independent orthogonal moment
that is Zernike Moment and Fourier-Mellin Moment has been used as feature extraction technique. The
values of Zernike Moments as characteristics independent of rotation have been used for classification
issues in the past and each of their real and imaginary components have been neglected individually and
with the phase coefficients, each of them will be changed by rotation. In this study, Zernike and Fourier-
Mellin Moments have been investigated to detect Persian characters in noisy and noise-free images. Also,
an improvement on the k-Nearest Neighbor (k-NN) classifier is proposed for character recognition. Using
the results comparison of the proposed method with current salient methods such as Back Propagation
(BP) and Radial Basis Function (RBF) neural networks in terms of feature extraction in words, it has been
shown that on the Hoda database, the proposed method reaches an acceptable detection rate (96/5%).
Although fuzzy systems demonstrate their ability to
solve different kinds of problems in various applications, there is an increasing interest on developing solid mathematical implementations suitable for control applications such as that used in fuzzy logic controllers (FLC). It is well known that, wide range of parameters is needed to be specified before the construction of a fuzzy system. To simplify in a systematic way the design and construction of a general fuzzy system, and without loss for generality a full parameterization process for a singleton type FLC is proposed in this paper. The resented methodology is very helpful in developing a universal computing algorithm for a standard fuzzy like PID controllers. An illustrative example shows the simplicity of applying the new paradigm.
Fault diagnosis using genetic algorithms and principal curveseSAT Journals
Abstract Several applications of nonlinear principal component analysis (NPCA) have appeared recently in process monitoring and fault diagnosis. In this paper a new approach is proposed for fault detection based on principal curves and genetic algorithms. The principal curve is a generation of linear principal component (PCA) introduced by Hastie as a parametric curve passes satisfactorily through the middle of data. The existing principal curves algorithms employ the first component of the data as an initial estimation of principal curve. However the dependence on initial line leads to a lack of flexibility and the final curve is only satisfactory for specific problems. In this paper we extend this work in two ways. First, we propose a new method based on genetic algorithms to find the principal curve. Here, lines are fitted and connected to form polygonal lines (PL). Second, potential application of principal curves is discussed. An example is used to illustrate fault diagnosis of nonlinear process using the proposed approach. Index Terms: Principal curve, Genetic Algorithm, Nonlinear principal component analysis, Fault detection.
INTERVAL TYPE-2 INTUITIONISTIC FUZZY LOGIC SYSTEM FOR TIME SERIES AND IDENTIF...ijfls
This paper proposes a sliding mode control-based learning of interval type-2 intuitionistic fuzzy logic system for time series and identification problems. Until now, derivative-based algorithms such as gradient descent back propagation, extended Kalman filter, decoupled extended Kalman filter and hybrid method of decoupled extended Kalman filter and gradient descent methods have been utilized for the optimization of the parameters of interval type-2 intuitionistic fuzzy logic systems. The proposed model is based on a Takagi-Sugeno-Kang inference system. The evaluations of the model are conducted using both real world and artificially generated datasets. Analysis of results reveals that the proposed interval type-2 intuitionistic fuzzy logic system trained with sliding mode control learning algorithm (derivative-free) do outperforms some existing models in terms of the test root mean squared error while competing favourable with other models in the literature. Moreover, the proposed model may stand as a good choice for real time applications where running time is paramount compared to the derivative-based models.
The Positive Effects of Fuzzy C-Means Clustering on Supervised Learning Class...Waqas Tariq
Selection of inputs is one of the most substantial components of classification algorithms for data mining and pattern recognition problems since even the best classifier will perform badly if the inputs are not selected very well. Big data and computational complexity are main cause of bad performance and low accuracy for classical classifiers. In other words, the complexity of classifier method is inversely proportional with its classification efficiency. For this purpose, two hybrid classifiers have been developed by using both type-1 and type-2 fuzzy c-means clustering with cascaded a classifier. In this proposed classifier, a large number of data points are reduced by using fuzzy c-means clustering before applied to a classifier algorithm as inputs. The aim of this study is to investigate the effect of fuzzy clustering on well-known and useful classifiers such as artificial neural networks (ANN) and support vector machines (SVM). Then the role of positive effects of these proposed algorithms were investigated on applied different data sets.
Text Independent Speaker Identification Using Imfcc Integrated With IcaIOSR Journals
Abstract: Over the years, more research work has been reported in literature regarding text independent
speaker identification using MFC coefficients. MFCC is one of the best methods modeled on human auditory
system. Murali et al (2011) [1] has developed a Text independent speaker identification using MFC coefficients
which follows Generalized Gaussian mixer model. MFCC, because of its filter bank structure it captures the
characteristics of information more effectively in lower frequency region than higher region, because of this,
valuable information in high frequency region may be lost. In this paper we rectify the above problem by
retrieving the information in high frequency region by inverting the Mel bank structure. The dimensionality and
dependency of above features were reduced by integrating with ICA. Here Text Independent Speaker
Identification system is developed by using Generalized Gaussian Mixer Model .By the experimentation, it was
observed that this model outperforms the earlier existing models.
Keywords: Independent Component Analysis; Generalized Gaussian Mixer Model; Inverted Mel frequency
cepstral coefficients; Bayesian classifier; EM algorithm.
Farsi character recognition using new hybrid feature extraction methodsijcseit
Identification of visual words and writings has long been one of the most essential and the most attractive
operations in the field of image processing which has been studied since the last few decades and includes
security, traffic control, fields of psychology, medicine, and engineering, etc. Previous techniques in the
field of identification of visual writings are very similar to each other for the most parts of their analysis,
and depending on the needs of the operational field have presented different feature extraction. Changes in
style of writing and font and turns of words and other issues are challenges of characters identifying
activity. In this study, a system of Persian character identification using independent orthogonal moment
that is Zernike Moment and Fourier-Mellin Moment has been used as feature extraction technique. The
values of Zernike Moments as characteristics independent of rotation have been used for classification
issues in the past and each of their real and imaginary components have been neglected individually and
with the phase coefficients, each of them will be changed by rotation. In this study, Zernike and Fourier-
Mellin Moments have been investigated to detect Persian characters in noisy and noise-free images. Also,
an improvement on the k-Nearest Neighbor (k-NN) classifier is proposed for character recognition. Using
the results comparison of the proposed method with current salient methods such as Back Propagation
(BP) and Radial Basis Function (RBF) neural networks in terms of feature extraction in words, it has been
shown that on the Hoda database, the proposed method reaches an acceptable detection rate (96/5%).
Although fuzzy systems demonstrate their ability to
solve different kinds of problems in various applications, there is an increasing interest on developing solid mathematical implementations suitable for control applications such as that used in fuzzy logic controllers (FLC). It is well known that, wide range of parameters is needed to be specified before the construction of a fuzzy system. To simplify in a systematic way the design and construction of a general fuzzy system, and without loss for generality a full parameterization process for a singleton type FLC is proposed in this paper. The resented methodology is very helpful in developing a universal computing algorithm for a standard fuzzy like PID controllers. An illustrative example shows the simplicity of applying the new paradigm.
Fault diagnosis using genetic algorithms and principal curveseSAT Journals
Abstract Several applications of nonlinear principal component analysis (NPCA) have appeared recently in process monitoring and fault diagnosis. In this paper a new approach is proposed for fault detection based on principal curves and genetic algorithms. The principal curve is a generation of linear principal component (PCA) introduced by Hastie as a parametric curve passes satisfactorily through the middle of data. The existing principal curves algorithms employ the first component of the data as an initial estimation of principal curve. However the dependence on initial line leads to a lack of flexibility and the final curve is only satisfactory for specific problems. In this paper we extend this work in two ways. First, we propose a new method based on genetic algorithms to find the principal curve. Here, lines are fitted and connected to form polygonal lines (PL). Second, potential application of principal curves is discussed. An example is used to illustrate fault diagnosis of nonlinear process using the proposed approach. Index Terms: Principal curve, Genetic Algorithm, Nonlinear principal component analysis, Fault detection.
INTERVAL TYPE-2 INTUITIONISTIC FUZZY LOGIC SYSTEM FOR TIME SERIES AND IDENTIF...ijfls
This paper proposes a sliding mode control-based learning of interval type-2 intuitionistic fuzzy logic system for time series and identification problems. Until now, derivative-based algorithms such as gradient descent back propagation, extended Kalman filter, decoupled extended Kalman filter and hybrid method of decoupled extended Kalman filter and gradient descent methods have been utilized for the optimization of the parameters of interval type-2 intuitionistic fuzzy logic systems. The proposed model is based on a Takagi-Sugeno-Kang inference system. The evaluations of the model are conducted using both real world and artificially generated datasets. Analysis of results reveals that the proposed interval type-2 intuitionistic fuzzy logic system trained with sliding mode control learning algorithm (derivative-free) do outperforms some existing models in terms of the test root mean squared error while competing favourable with other models in the literature. Moreover, the proposed model may stand as a good choice for real time applications where running time is paramount compared to the derivative-based models.
The Positive Effects of Fuzzy C-Means Clustering on Supervised Learning Class...Waqas Tariq
Selection of inputs is one of the most substantial components of classification algorithms for data mining and pattern recognition problems since even the best classifier will perform badly if the inputs are not selected very well. Big data and computational complexity are main cause of bad performance and low accuracy for classical classifiers. In other words, the complexity of classifier method is inversely proportional with its classification efficiency. For this purpose, two hybrid classifiers have been developed by using both type-1 and type-2 fuzzy c-means clustering with cascaded a classifier. In this proposed classifier, a large number of data points are reduced by using fuzzy c-means clustering before applied to a classifier algorithm as inputs. The aim of this study is to investigate the effect of fuzzy clustering on well-known and useful classifiers such as artificial neural networks (ANN) and support vector machines (SVM). Then the role of positive effects of these proposed algorithms were investigated on applied different data sets.
Text Independent Speaker Identification Using Imfcc Integrated With IcaIOSR Journals
Abstract: Over the years, more research work has been reported in literature regarding text independent
speaker identification using MFC coefficients. MFCC is one of the best methods modeled on human auditory
system. Murali et al (2011) [1] has developed a Text independent speaker identification using MFC coefficients
which follows Generalized Gaussian mixer model. MFCC, because of its filter bank structure it captures the
characteristics of information more effectively in lower frequency region than higher region, because of this,
valuable information in high frequency region may be lost. In this paper we rectify the above problem by
retrieving the information in high frequency region by inverting the Mel bank structure. The dimensionality and
dependency of above features were reduced by integrating with ICA. Here Text Independent Speaker
Identification system is developed by using Generalized Gaussian Mixer Model .By the experimentation, it was
observed that this model outperforms the earlier existing models.
Keywords: Independent Component Analysis; Generalized Gaussian Mixer Model; Inverted Mel frequency
cepstral coefficients; Bayesian classifier; EM algorithm.
The main goal of cluster analysis is to classify elements into groupsbased on their similarity. Clustering has many applications such as astronomy, bioinformatics, bibliography, and pattern recognition. In this paper, a survey of clustering methods and techniques and identification of advantages and disadvantages of these methods are presented to give a solid background to choose the best method to extract strong association rules.
Fault detection based on novel fuzzy modelling csijjournal
The Fault detection which is based on fuzzy modeling is investigated. Takagi-Sugeno (TS) fuzzy model can
be derived by structure and parameter identification, where only the input-output data of the identified system are available. In the structure identification step, Gustafson-Kessel clustering algorithm (GKCA) is used to detect clusters of different geometrical shapes in the data set and to obtain the point-wise membership function of the premise. In the parameter identification step, Unscented Kalman filter (UKF) is
used to estimate the parameters of the premise’s membership function. In the consequence part, Kalman filter (KF) algorithm is applied as a linear regression to estimate parameters of the TS model using the input-output data set. Then, the obtained fuzzy model is used to detect the fault. Simulations are provided to demonstrate the effectiveness of the theoretical results.
Control chart pattern recognition using k mica clustering and neural networksISA Interchange
Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in manufacturing processes. This paper presents a novel hybrid intelligent method (HIM) for recognition of the common types of control chart pattern (CCP). The proposed method includes two main modules: a clustering module and a classifier module. In the clustering module, the input data is first clustered by a new technique. This technique is a suitable combination of the modified imperialist competitive algorithm (MICA) and the K-means algorithm. Then the Euclidean distance of each pattern is computed from the determined clusters. The classifier module determines the membership of the patterns using the computed distance. In this module, several neural networks, such as the multilayer perceptron, probabilistic neural networks, and the radial basis function neural networks, are investigated. Using the experimental study, we choose the best classifier in order to recognize the CCPs. Simulation results show that a high recognition accuracy, about 99.65%, is achieved.
2-DOF BLOCK POLE PLACEMENT CONTROL APPLICATION TO:HAVE-DASH-IIBTT MISSILEZac Darcy
In a multivariable servomechanism design, it is required that the output vector tracks a certain reference
vector while satisfying some desired transient specifications, for this purpose a 2DOF control law
consisting of state feedback gain and feedforward scaling gain is proposed. The control law is designed
using block pole placement technique by assigning a set of desired Block poles in different canonical forms.
The resulting control is simulated for linearized model of the HAVE DASH II BTT missile; numerical
results are analyzed and compared in terms of transient response, gain magnitude, performance
robustness, stability robustness and tracking. The suitable structure for this case study is then selected.
In this paper, implementations of three Hough Transform based fingerprint alignment algorithms are analyzed with respect to time complexity on Java Card environment. Three algorithms are: Local Match Based Approach (LMBA), Discretized Rotation Based Approach
(DRBA), and All Possible to Match Based Approach (APMBA). The aim of this paper is to present the complexity and implementations of existing work of one of the mostly used method of fingerprint alignment, in order that the complexity can be simplified or find the best algorithm with efficient complexity and implementation that can be easily implemented on Java Card environment for match on card. Efficiency involves the accuracy of the implementation, time taken to perform fingerprint alignment, memory required by the implementation and instruction operations required and used.
Machine Learning Technique PCA Part's description in this research paper. Very good source for a clear understanding of how PCA i.e the Principal Component Analysis technique works while implementing machine learning techniques.
Single Channel Speech De-noising Using Kernel Independent Component Analysis...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.
The main goal of cluster analysis is to classify elements into groupsbased on their similarity. Clustering has many applications such as astronomy, bioinformatics, bibliography, and pattern recognition. In this paper, a survey of clustering methods and techniques and identification of advantages and disadvantages of these methods are presented to give a solid background to choose the best method to extract strong association rules.
Fault detection based on novel fuzzy modelling csijjournal
The Fault detection which is based on fuzzy modeling is investigated. Takagi-Sugeno (TS) fuzzy model can
be derived by structure and parameter identification, where only the input-output data of the identified system are available. In the structure identification step, Gustafson-Kessel clustering algorithm (GKCA) is used to detect clusters of different geometrical shapes in the data set and to obtain the point-wise membership function of the premise. In the parameter identification step, Unscented Kalman filter (UKF) is
used to estimate the parameters of the premise’s membership function. In the consequence part, Kalman filter (KF) algorithm is applied as a linear regression to estimate parameters of the TS model using the input-output data set. Then, the obtained fuzzy model is used to detect the fault. Simulations are provided to demonstrate the effectiveness of the theoretical results.
Control chart pattern recognition using k mica clustering and neural networksISA Interchange
Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in manufacturing processes. This paper presents a novel hybrid intelligent method (HIM) for recognition of the common types of control chart pattern (CCP). The proposed method includes two main modules: a clustering module and a classifier module. In the clustering module, the input data is first clustered by a new technique. This technique is a suitable combination of the modified imperialist competitive algorithm (MICA) and the K-means algorithm. Then the Euclidean distance of each pattern is computed from the determined clusters. The classifier module determines the membership of the patterns using the computed distance. In this module, several neural networks, such as the multilayer perceptron, probabilistic neural networks, and the radial basis function neural networks, are investigated. Using the experimental study, we choose the best classifier in order to recognize the CCPs. Simulation results show that a high recognition accuracy, about 99.65%, is achieved.
2-DOF BLOCK POLE PLACEMENT CONTROL APPLICATION TO:HAVE-DASH-IIBTT MISSILEZac Darcy
In a multivariable servomechanism design, it is required that the output vector tracks a certain reference
vector while satisfying some desired transient specifications, for this purpose a 2DOF control law
consisting of state feedback gain and feedforward scaling gain is proposed. The control law is designed
using block pole placement technique by assigning a set of desired Block poles in different canonical forms.
The resulting control is simulated for linearized model of the HAVE DASH II BTT missile; numerical
results are analyzed and compared in terms of transient response, gain magnitude, performance
robustness, stability robustness and tracking. The suitable structure for this case study is then selected.
In this paper, implementations of three Hough Transform based fingerprint alignment algorithms are analyzed with respect to time complexity on Java Card environment. Three algorithms are: Local Match Based Approach (LMBA), Discretized Rotation Based Approach
(DRBA), and All Possible to Match Based Approach (APMBA). The aim of this paper is to present the complexity and implementations of existing work of one of the mostly used method of fingerprint alignment, in order that the complexity can be simplified or find the best algorithm with efficient complexity and implementation that can be easily implemented on Java Card environment for match on card. Efficiency involves the accuracy of the implementation, time taken to perform fingerprint alignment, memory required by the implementation and instruction operations required and used.
Machine Learning Technique PCA Part's description in this research paper. Very good source for a clear understanding of how PCA i.e the Principal Component Analysis technique works while implementing machine learning techniques.
Single Channel Speech De-noising Using Kernel Independent Component Analysis...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.
presentation for marketeers at a Datlinq event.How location and geography may provide insights and understanding of consumer behavior, how consumers are contributing themselves to better data, and how by analysing location we are able to make better decisions.
An Artificial Intelligence Approach to Ultra High Frequency Path Loss Modelli...ijtsrd
This study proposes Artificial Intelligence AI based path loss prediction models for the suburban areas of Abuja, Nigeria. The AI based models were created on the bases of two deep learning networks, namely the Adaptive Neuro Fuzzy Inference System ANFIS and the Generalized Radial Basis Function Neural network RBF NN . These prediction models were created, trained, validated and tested for path loss prediction using path loss data recorded at 1800MHz from multiple Base Transceiver Stations BTSs distributed across the areas under investigation. Results indicate that the ANFIS and RBF NN based models with Root Mean Squared Error RMSE values of 5.30dB and 5.31dB respectively, offer greater prediction accuracy over the widely used empirical COST 231 Hata, which has an RMSE of 8.18dB. Deme C. Abraham ""An Artificial Intelligence Approach to Ultra-High Frequency Path Loss Modelling of the Suburban Areas of Abuja, Nigeria"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30227.pdf
Paper Url : https://www.ijtsrd.com/computer-science/artificial-intelligence/30227/an-artificial-intelligence-approach-to-ultra-high-frequency-path-loss-modelling-of-the-suburban-areas-of-abuja-nigeria/deme-c-abraham
Investigations on Hybrid Learning in ANFISIJERA Editor
Neural networks have attractiveness to several researchers due to their great closeness to the structure of the brain, their characteristics not shared by many traditional systems. An Artificial Neural Network (ANN) is a network of interconnected artificial processing elements (called neurons) that co-operate with one another in order to solve specific issues. ANNs are inspired by the structure and functional aspects of biological nervous systems. Neural networks, which recognize patterns and adopt themselves to cope with changing environments. Fuzzy inference system incorporates human knowledge and performs inferencing and decision making. The integration of these two complementary approaches together with certain derivative free optimization techniques, results in a novel discipline called Neuro Fuzzy. In Neuro fuzzy development a specific approach is called Adaptive Neuro Fuzzy Inference System (ANFIS), which has shown significant results in modeling nonlinear functions. The basic idea behind the paper is to design a system that uses a fuzzy system to represent knowledge in an interpretable manner and have the learning ability derived from a Runge-Kutta learning method (RKLM) to adjust its membership functions and parameters in order to enhance the system performance. The problem of finding appropriate membership functions and fuzzy rules is often a tiring process of trial and error. It requires users to understand the data before training, which is usually difficult to achieve when the database is relatively large. To overcome these problems, a hybrid of Back Propagation Neural network (BPN) and RKLM can combine the advantages of two systems and avoid their disadvantages.
Development of Adaptive Neuro Fuzzy Inference System for Estimation of Evapot...ijsrd.com
The accuracy of an adaptive neurofuzzy computing technique in estimation of reference evapotranspiration (ETo) is investigated in this paper. The model is based on Adaptive Neurofuzzy Inference System (ANFIS) and uses commonly available weather information such as the daily climatic data, Maximum and Minimum Air Temperature, Relative Humidity, Wind Speed and Sunshine hours from station, Karjan (Latitude - 22°03'10.95"N, Longitude - 73°07'24.65"E), in Vadodara (Gujarat), are used as inputs to the neurofuzzy model to estimate ETo obtained using the FAO-56 Penman.Monteith equation. The daily meteorological data of two years from 2009 and 2010 at Karjan Takuka, Vadodara, are used to train the model, and the data in 2011 is used to predict the ETo in that year and to validate the model. The ETo in training period (Train- ETo) and the predicted results (Test-ETo) are compared with the ETo computed by Penman-Monteith method (PM-ETo) using "gDailyET" Software. The results indicate that the PM-ETo values are closely and linearly correlated with Train- ETo and Test- ETo with Root Mean Squared Error (RMSE) and showed the higher significances of the Train- ETo and Test- ETo. The results indict the feasibility of using the convenient model to resolve the problems of agriculture irrigation with intelligent algorithm, and more accurate weather forecast, appropriate membership function and suitable fuzzy rules.
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.
New artificial neural network design for Chua chaotic system prediction usin...IJECEIAES
This study aims to design a new architecture of the artificial neural networks (ANNs) using the Xilinx system generator (XSG) and its hardware co-simulation equivalent model using field programmable gate array (FPGA) to predict the behavior of Chua’s chaotic system and use it in hiding information. The work proposed consists of two main sections. In the first section, MATLAB R2016a was used to build a 3×4×3 feed forward neural network (FFNN). The training results demonstrate that FFNN training in the Bayesian regulation algorithm is sufficiently accurate to directly implement. The second section demonstrates the hardware implementation of the network with the XSG on the Xilinx artix7 xc7a100t-1csg324 chip. Finally, the message was first encrypted using a dynamic Chua system and then decrypted using ANN’s chaotic dynamics. ANN models were developed to implement hardware in the FPGA system using the IEEE 754 Single precision floating-point format. The ANN design method illustrated can be extended to other chaotic systems in general.
Prediction of Extreme Wind Speed Using Artificial Neural Network ApproachScientific Review SR
Prediction of an accurate wind speed of wind farms is necessary because of the intermittent nature
of wind for any region. Number of methods such as persistence, physical, statistical, spatial correlation, artificial
intelligence network and hybrid are generally available for prediction of wind speed. In this paper, ANN based
methods viz., Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks are used. The
performance of the networks applied for prediction of wind speed is evaluated by model performance indicators
viz., Correlation Coefficient (CC), Model Efficiency (MEF) and Mean Absolute Percentage Error (MAPE).
Meteorological parameters such as maximum and minimum temperature, air pressure, solar radiation and
altitude are considered as input units for MLP and RBF networks to predict the extreme wind speed at Delhi.
The study shows the values of CC, MEF and MAPE between the observed and predicted wind speed (using
MLP) are computed as 0.992, 95.4% and 4.3% respectively while training the network data. For RBF network,
the values of CC, MEF and MAPE are computed as 0.992, 95.9% and 3.0% respectively. The model
performance analysis indicates the RBF is better suited network among two different networks studied for
prediction of extreme wind speed at Delhi.
Design of frequency selective surface comprising of dipoles using artificial ...IJAAS Team
This paper depicts the design of Frequency Selective Surface (FSS) comprising of dipoles using Artificial Neural Network (ANN). It has been observed that with the change of the dimensions and periodicity of FSS, the resonating frequency of the FSS changes. This change in resonating frequency has been studied and investigated using simulation software. The simulated data were used to train the proposed ANN models. The trained ANN models are found to predict the FSS characteristics precisely with negligible error. Compared to traditional EM simulation softwares (like ANSOFT Designer), the proposed technique using ANN models is found to significantly reduce the FSS design complexity and computational time. The FSS simulations were made using ANSOFT Designer v2 software and the neural network was designed using MATLAB software.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
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.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
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Ag04606202206
1. Dr. T. M. V. Suryanarayana et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 6( Version 6), June 2014, pp.202-206
www.ijera.com 202 | P a g e
Adaptive Neuro-Fuzzy Inference System For Rainfall-Runoff
Modeling
Ratansharan Panchal*, Dr. T. M. V. Suryanarayana**, Dr. F. P. Parekh***
*(PG Student, Water Resources Engineering and Management Institute (WREMI), Faculty of Technology and
Engineering, the Maharaja Sayajirao University of Baroda, Samiala-391410)
**(Associate Professor, Water Resources Engineering and Management Institute (WREMI), Faculty of
Technology and Engineering, The Maharaja Sayajirao University of Baroda, Samiala-391410)
***(Offg. Director and Associate Professor, Water Resources Engineering and Management Institute
(WREMI), Faculty of Technology and Engineering, The Maharaja Sayajirao University of Baroda, Samiala-
391410)
ABSTRACT
In this study an Adaptive Neuro-Fuzzy Inference System (ANFIS) was used for rainfall-runoff modeling for the
Dharoi sub-basin, India. Different combinations of rainfall were considered as the inputs to the model, and
runoff was considered as the output. Input space partitioning for model structure identification was done by grid
partitioning. A hybrid learning algorithm consisting of back-propagation and least-squares estimation was used
to train the model for runoff estimation. The optimal learning parameters were determined by trial and error
using Triangular membership function. Root mean square error (RMSE) and correlation coefficient (r) were
used for selecting the best performing model.
Keywords – Adaptive Neuro Fuzzy Inference System (ANFIS) modeling, Dharoi sub-basin, Rainfall-Runoff
I. INTRODUCTION
The hydrologic behavior of rainfall-runoff
process is very complicated phenomenon which is
controlled by large number of climatic and
physiographic factors that vary with both the time
and space. The relationship between rainfall and
resulting runoff is quite complex and is influenced by
factors relating the topography and climate. In recent
years, artificial neural network (ANN), fuzzy logic,
genetic algorithm and chaos theory have been widely
applied in the sphere of hydrology and water
resource. ANN have been recently accepted as an
efficient alternative tool for modeling of complex
hydrologic systems and widely used for prediction.
Some specific applications of ANN to hydrology
include modeling rainfall-runoff process. Fuzzy logic
method was first developed to explain the human
thinking and decision system by [1]. Several studies
have been carried out using fuzzy logic in hydrology
and water resources planning [2]. Adaptive neuro-
fuzzy inference system (ANFIS) which is integration
of neural networks and fuzzy logic has the potential
to capture the benefits of both these fields in a single
framework. ANFIS utilizes linguistic information
from the fuzzy logic as well learning capability of an
ANN. Adaptive neuro fuzzy inference system
(ANFIS) is a fuzzy mapping algorithm that is based
on Tagaki-Sugeno-Kang (TSK) fuzzy inference
system [3] and [4]. ANFIS used for many
applications such as, database management, system
Design and planning/forecasting of the water
resources [5].
II. NEURO-FUZZY MODEL
Neuro-fuzzy modeling refers to the way of
applying various learning techniques developed in
the neural network literature to fuzzy modeling or to
a fuzzy inference system (FIS). The basic structure of
a FIS consists of three conceptual components: a rule
base, which contains a selection of fuzzy rules; a
database which defines the membership functions
(MF) used in the fuzzy rules; and a reasoning
mechanism, which performs the inference procedure
upon the rules to derive an output (see Fig. 1). FIS
implements a nonlinear mapping from its input space
to the output space. This mapping is accomplished by
a number of fuzzy if-then rules, each of which
describes the local behavior of the mapping. The
parameters of the if-then rules (referred to as
antecedents or premises in fuzzy modeling) define a
fuzzy region of the input space, and the output
parameters (also consequents in fuzzy modeling)
specify the corresponding output. Hence, the
efficiency of the FIS depends on the estimated
parameters. However, the selection of the shape of
the fuzzy set (described by the antecedents)
corresponding to an input is not guided by any
procedure [6]. But the ule structure of a FIS makes it
possible to incorporate human expertise about the
system being modeled directly into the modeling
process to decide on the relevant inputs, number of
RESEARCH ARTICLE OPEN ACCESS
2. Dr. T. M. V. Suryanarayana et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 6( Version 6), June 2014, pp.202-206
www.ijera.com 203 | P a g e
MFs for each input, etc. and the corresponding
numerical data for parametestimation.
Fig. 1 Fuzzy Inference System with crisp
In the present study, the concept of the adaptive
Network, which is a generalization of the common
backpropagation neural network, is employed to
tackle the parameter identification problem in a FIS.
An adaptive network is a multi layered feed
forward structure whose overall output behavior is
determined by the value of a collection of modifiable
parameters. More specifically, the configuration of an
adaptive network is composed of a set of nodes
connected through directional links, where each node
is a process unit that performs a static node function
on its incoming signal to generate a single node
output. The node function is a parameterized function
with modifiable parameters. It may be noted that
links in an adaptive network only indicate the flow
direction of signals between nodes and no weights
are associated with these links. Readers are referred
to [7] for more details on adaptive networks. [8]
introduced a novel architecture and learning
procedure for the FIS that uses a neural network
learning algorithm for constructing a set of fuzzy if
then rules with appropriate MFs from the stipulated
input–output pairs. This procedure of developing a
FIS using the framework of adaptive neural networks
is called an adaptive neuro fuzzy inference system
(ANFIS).
1.1. ANFIS architecture
The general structure of the ANFIS is presented
in Fig. 2. Selection of the FIS is the major concern
when designing an ANFIS to model a specific target
system. Various types of FIS are reported in
Fig. 2 (a) Fuzzy Inference System (b) Equivalent
ANFIS
the literature and each are characterized by their
consequent parameters only. The current study uses
the Sugeno fuzzy model since the consequent part of
this FIS is a linear equation and the parameters can
be estimated by a simple least squares error method.
For instance, consider that the FIS has two inputs
x and y and one output z: For the first order Sugeno
fuzzy model, a typical rule set with two fuzzy if-then
rules can be expressed as:
Rule 1: If x is A1 and y is B1; then f1
=p1x + q1y + r (1)
Rule 2: If x is A2 and y is B2; then f2
= p2x + q2y + r (2)
Where A1, A2 and B1, B2 are the MFs for inputs x and
y; respectively; p1; q1; r1 and p2; q2; r2 are the
parameters of the output function. Fig. 2(a) illustrates
the fuzzy reasoning mechanism for this Sugeno
model to derive an output function (f) from a given
input vector [x, y].
The corresponding equivalent ANFIS
architecture is presented in Fig. 2(b), where nodes of
the same layer have similar functions. The
functioning of the ANFIS is as follows:
Layer 1: Each node in this layer generates
membership grades of an input variable. The node
output OPi
1
is defined by:
OPi
1
= 𝜇 𝐴𝑖 (𝑥) for i = 1, 2 or (3)
OPi
1
= 𝜇 𝐵𝑖−2 (𝑦) for i = 3, 4 (4)
where x (or y) is the input to the node; Ai (or Bi-2) is a
fuzzy set associated with this node, characterized by
the shape of the MFs in this node and can be any
3. Dr. T. M. V. Suryanarayana et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 6( Version 6), June 2014, pp.202-206
www.ijera.com 204 | P a g e
appropriate functions that are continuous and
piecewise differentiable such as Gaussian,
generalized bell shaped, trapezoidal shaped and
triangular shaped functions. Assuming a generalized
bell function as the MF, the output OPi
1
can be
computed as,
𝑂𝑃𝑖
1
= 𝜋 𝐴𝑖 =
1
1+(
𝑥− 𝑐 𝑖
𝑎 𝑖
)2𝑏𝑖
(5)
Where {ai; bi; ci} is the parameter set that changes
the shapes of the MF with maximum equal to 1 and
minimum equal to 0.
Layer 2: Every node in this layer multiplies
the incoming signals, denoted as ∏, and the output
OP𝑖
2
that represents the firing strength of a rule is
computes as,
OP𝐼
2
= wi = µAi (x) µBi (y), i = 1, 2. (6)
Layer 3: The ith node of this layer, labeled
as N, computes the normalized firing strengths as,
OP𝑖
3
= 𝑤i =
𝑤𝑖
𝑤1+𝑤2
, i = 1, 2 (7)
Layer 4: Node i in this layer compute the
contribution of the ith rule towards the model output,
with the following node functions:
OP𝑖
4
= 𝑤ifi = 𝑤i(pix + qiy + ri) (8)
Where 𝑤 is the output of layer 3 and {pi, qi, ri} is the
parameter set.
Layer 5: The single node in this layer
computes the overall output of the ANFIS as:
OP𝑖
5
= Overall output = 𝑤𝑖 ifi =
𝑊𝑖𝑓𝑖𝑖
𝑊𝑖𝑖
(9)
III. STUDY AREA AND DATA
Area selected for the present study is the Dharoi
sub basin which is the part of Sabarmati river basin.
Study area is the Dharoi sub basin which is
designated by line in Sabarmati river basin map. The
area covering upper sub-basin and the catchment of
the main river up to Dharoi dam is designated as
Dharoi sub-basin. Constructed in 1978, the Dharoi
dam is located about 165 km upstream Ahmadabad in
village Dharoi of Mehsana district. This covers
drainage area of the main river up to Dharoi dam.
In this study, long term monthly Rainfall and
Runoff data are derived for Dharoi sub basin which is
the part of Sabarmati river basin. Catchment area of
the sub basin is 5,540 sq.km, out of which about
2,640 sq.km lies in Gujarat state.
The area covering upper sub-basin and the
catchment of the main river up to Dharoi Dam is
designated as Dharoi sub-basin. The Dharoi dam is
constructed in 1978 and is located about 165 kms
upstream Ahmedabad in village Dharoi of Mehsana
district.
Fig. 3 Dharoi sub-basin in Sabarmati Basin
In Dharoi sub basin there are six Rain gauge
stations existed but among them Hadad Rain gauge
station’s data is selected for the year 1968 to 2010
(42 years). Rainfall data are considered from June to
October for each year so total 217 monthly data sets
are used.
IV. MODEL DEVELOPMENT AND
TESTING
There are no fixed rules for developing an
ANFIS, even though a general framework can be
followed based on previous successful applications in
engineering. The selection of proper input and output
data posses the prime importance and needs to be
selected carefully. Here the Rainfall-Runoff model
was developed using the Rainfall data as input and
Runoff data as output.
Here, in the current study, Rainfall-Runoff
datasets were firstly divided in the different ratio of
training and testing data i.e. 80-20%, 70-30% and 60-
40% that means the 80% datasets were used for
training the model and remaining 20% dataset were
taken for its validation purpose. The runoff model
was developed for each of the three rain gauge station
namely Hadad, Khedbrhama and Dharoi in, Dharoi
4. Dr. T. M. V. Suryanarayana et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 6( Version 6), June 2014, pp.202-206
www.ijera.com 205 | P a g e
sub-basin. The best model for the each of the three
stations has been selected by means of model
evaluation parameters.
The results obtained for all three stations then
evaluated by means of the model evaluation
parameters selected for the current study given
below:
Root mean square error (RMSE):
𝑅𝑀𝑆𝐸 =
(𝑄 𝑖 − 𝑄𝑛
𝑖=1 (𝑖))2
𝑛
Correlation coefficient:
r =
𝑄 𝑖 − 𝑄 (𝑄 𝑖 − 𝑄)𝑛
𝑖=1
(𝑄 𝑖 − 𝑄𝑛
𝑖=1 )2 (𝑄 𝑖 − 𝑄)𝑛
𝑖=1
2
Where Q (i) is the n estimated runoff value, Q(i) is
the n observes runoff value, 𝑄 is the mean of the
observed runoff values, and 𝑄 is the mean of the
estimated runoff values.
V. RESULTS AND DISCUSSION
The models were developed using 7 numbers of
membership functions of type triangular with 7 If-
then rules for all different sets of training and testing
dataset for each rain gauge station in ANFIS.
After obtaining the results the best model for the
stations was selected and highlighted by means of the
evaluation parameters that are RMSE and r values
given in table-1.
Table-1: ANFIS results for different stations
Hadad
Ratio
%
Training Testing
RMSE r RMSE r
80-20 1.249 0.999 0.853 0.999
70-30 1.319 0.999 0.808 0.999
60-40 1.395 0.999 0.845 0.999
Khedbrhama
Ratio
%
Training Testing
RMSE r RMSE r
80-20 1.365 0.999 0.841 0.999
70-30 1.367 0.999 1.015 0.999
60-40 1.457 0.999 0.988 0.999
Dharoi
Ratio
%
Training Testing
RMSE r RMSE r
80-20 1.259 0.999 1.023 0.999
70-30 1.659 0.999 1.123 0.999
60-40 1.367 0.999 1.110 0.999
Fig. 4 Comparison of observed runoff vs predicted
runoff for the station hadad training
Fig. 5 Comparison of observed runoff vs predicted
runoff for the station hadad testing
Fig. 6 Comparison of observed runoff vs. predicted
runoff for the station khedbrhama training
Fig. 7 Comparison of observed runoff vs. predicted
runoff for the station khedbrhama testing
0
100
200
300
400
500
1
15
29
43
57
71
85
99
113
127
141
155
169
Runoff,mm
Runoff Pre. Runoff
0
100
200
300
400
1 5 9 13 17 21 25 29 33 37 41
Runoff,mm
Runoff Pre. Runoff
0
200
400
600
800
1
16
31
46
61
76
91
106
121
136
151
166
Runoff,mm
Runoff Pre. Runoff
0
100
200
300
400
500
1 5 9 13 17 21 25 29 33 37 41
Runoff,mm
Runoff Pre. Runoff
5. Dr. T. M. V. Suryanarayana et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 6( Version 6), June 2014, pp.202-206
www.ijera.com 206 | P a g e
Fig. 8 Comparison of observed runoff vs. predicted
runoff for the station dharoi training
Fig. 9 Comparison of observed runoff vs. predicted
runoff for the station dharoi testing
Here, the results shows (table-1) that the ratio for
training and testing data of 60-40% and 70-30%
gives the better results for the RMSE and r values but
when looking to the ratio of 80-20%, it gives the best
results for the current study and gives the best model
of Rainfall-Runoff for all the three rain gauge
stations namely Hadad, Khedbrhama and Dharoi.
Also the estimated runoff values shows the very little
variation as compared to the observed runoff values.
Also, the comparison of the observed runoff vs.
predicted runoff was shown for all the stations
namely hadad (fig. 4 & 5), khedbrhama (fig. 6 & 7)
and dharoi (fig. 8 & 9).
VI. SUMMARY AND COCLUSION
Here, one can conclude that the Rainfall-Runoff
model for the Hadad, Khedbrhama and Dharoi rain
gauge stations is 7 triangular type membership
functions with the input and output training and
testing ratio of 80-20% which gives the RMSE and r
values as 1.249, 0.999 training and 0.853, 0.999
testing for Hadad rain gauge station, 1.365, 0.999
training and 0.841, 0.999 testing for Khedbrhama
rain gauge station and 1.259, 0.999 training and
1.023, 0.999 testing for Dharoi rain gauge station.
Also the ratio of 60-40% and 70-30% training
and testing gives the reasonably much accurate
results and one can use these models in absence of
the best model for the prediction of runoff in Dharoi
sub-basin for the future prediction of runoff.
Summary states that the ANFIS tool provides the
betterment of the Rainfall-Runoff modeling in
comparison of the other tools as ANN, Fuzzy logic
etc. And one can used this tool for such hydrological
modeling say rainfall-runoff, rainfall prediction,
evapotranspiration etc. for the future prediction.
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[1] Zadeh, L.A., 1965. Fuzzy sets. Information and
Control 8 (3), 338–353.
[2] Mahabir, C.; Hicks, F.E. and Robinson F.
A.(2003). “Application of fuzzy logic to
forecast seasonal runoff”. Hydrological
Process, 17:3749-3762.
[3] Jang, J.-S.R., Sun, C.-T., 1995. Neuro-fuzzy
modeling and control. Proceedings IEEE 83
(3), 378–406.
[4] Loukas, Y.L .(2001). “Adaptive neuro-fuzzy
inference system: an instant and architecture-
free predictor for improved QSAR studies”. J
Med Chem 44(17):2772 2783.
[5] Nayak, P.C., Sudheer, K.P., Rangan, D.M. and
Ramasastri, K.S. (2004). “A neuro-fuzzy
computing technique for modelling
hydrological time series”. J. Hydrology, 291 :
52-66.
[6] Ojala, T., 1995 Neuro-Fuzzy systems in
control. M Sc. Thesis, Tempere Univeristy of
Technology, Tampere, Finland.
[7] Brown, M., Harris, C., 1994. Neurofuzzy
Adaptive Modeling and Control. Prentice Hall.
[8] Jang, J.-S.R., 1993. ANFIS: adaptive network
based fuzzy inference system. IEEE
Transactions on Systems, Man and
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[9] Takagi, T., and M. Sugeno.(1985). “Fuzzy
identification of systems and its application to
modeling and control”. IEEE Transactions on
Systems, Man, and Cybernetics,15: 116–132.
0
100
200
300
400
500
600
700
1
15
29
43
57
71
85
99
113
127
141
155
169
Runoff,mm
Runoff Pre. Runoff
0
100
200
300
400
500
1 4 7 101316192225283134374043
Runoff,mm
Runoff Pre. Runoff