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Modular neural network approach for short term flood forecasting a comparative study

Rahul P, A. A.
2010 International Journal of Advanced Computer Science and Applications  
This research demonstrates static neural approach by applying Modular feedforward neural network to rainfall-runoff modeling for the upper area of Wardha River in India.  ...  The model is developed by processing online data over time using static modular neural network modeling.  ...  A comparison between four different models of Modular feedforward neural network model is made to investigate the performance of four distinct approaches.  ... 
doi:10.14569/ijacsa.2010.010514 fatcat:e2m7wdfjybdpblxjwqdk5hj4wi

An intuitive view to compare intelligent systems

A.R. Nazemi, M.R.T. Akbarzadeh, S.M. Hosseini
2004 IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.  
., Rainfall-Runoff modeling is introduced and nine soft computing-based modeling approaches are considered to describe the rainfall-runoff process in a particular case study.  ...  systems for modeling rainfall-runoff process in the considered case study.  ...  ACKNOWLEDGMENT The authors would like to greatly thanks Professor Soroosh Sorooshian and Professor K. L. Hsu for providing data of Leaf River basin and some of their publications.  ... 
doi:10.1109/nafips.2004.1337363 fatcat:tegj5466ejgank45wkoetjush4

Short term flood forecasting using General Recurrent neural network modeling a comparative study

Rahul P. Deshmukh, A. A. Ghatol
2010 International Journal of Computer Applications  
This research demonstrates dynamic neural approach by applying general recurrent neural network to rainfall-runoff modeling for the upper area of Wardha River in India.  ...  Methodologies and techniques by applying different learning rule, activation function and input layer structure are presented in this paper and a comparison for the short term runoff prediction results  ...  A comparison between five different learning rules with four activation function for four different input structures is made for optimal performance for general recurrent neural network model.  ... 
doi:10.5120/1259-1777 fatcat:fjwlbgmpuvf75hcetxk2jqgmka

Rainfall Runoff Modelling Using Generalized Neural Network and Radial Basis Network

C Chandre Gowda, Mayya S. G.
2014 International Journal of Intelligent Systems and Applications in Engineering  
Rainfall runoff study has a wide scope in water resource management. To provide a reliable runoff prediction model is of paramount importance.  ...  Runoff prediction is carried out using Generalized Regression neural network and Radial Basis neural network.  ...  Acknowledgements The authors wish to thank the head of the department of applied mechanics and hydraulics (NITK, Surathkal) and all the faculty group and research scholars who helped to improve the quality  ... 
doi:10.18201/ijisae.82758 fatcat:t2d6sg2jijhynetro3przesgfm

Study of Time Series Data Mining for the Real Time Hydrological Forecasting: A Review

Satanand Mishra, C. Saravanan, V. K. Dwivedi
2015 International Journal of Computer Applications  
Researchers are developed models for runoff forecasting using the data mining tools and techniques like regression analysis, clustering, artificial neural network (ANN), and support vector machine (SVM  ...  Now a day's comparison of ANN, fuzzy logic and genetic algorithms for rainfall-runoff modeling has carried out. The study described pros and cons of these algorithms and suggest  ...  The weighted training examples are then used to train a classification model that becomes a new feature for the SVM model [20]. framework of new algorithm for Rainfall runoff modeling.  ... 
doi:10.5120/20692-3581 fatcat:v3vfg7a4wfgwxfdgxdgvlhvrfq

Hydrological modelling using artificial neural networks

C. W. Dawson, R. L. Wilby
2001 Progress in physical geography  
This review considers the application of artificial neural networks (ANNs) to rainfall-runoff modelling and flood forecasting.  ...  Accordingly, a template is proposed in order to assist the construction of future ANN rainfall-runoff models.  ...  RW was supported by ACACIA (A Consortium for the Application of Climate Impact Assessments).  ... 
doi:10.1177/030913330102500104 fatcat:3phirit2mndqtjcojhvjw3kb7m

Hydrological modelling using artificial neural networks

C.W. Dawson, R.L. Wilby
2001 Progress in physical geography  
This review considers the application of artificial neural networks (ANNs) to rainfall-runoff modelling and flood forecasting.  ...  Accordingly, a template is proposed in order to assist the construction of future ANN rainfall-runoff models.  ...  RW was supported by ACACIA (A Consortium for the Application of Climate Impact Assessments).  ... 
doi:10.1191/030913301674775671 fatcat:ubueotqulnfpdazvrnl6uh4uda

RAINFALL RUNOFF MODELING BY MULTILAYER PERCEPTRON NEURAL NETWORK FOR LUI RIVER CATCHMENT

Nadeem Nawaz, Sobri Harun, Rawshan Othman, Arien Heryansyah
2016 Jurnal Teknologi  
This study presents an application of Multilayer Perceptron neural network (MLPNN) for the continuous and event based rainfall-runoff modeling to evaluate its performance for a tropical catchment of Lui  ...  The study has found that MLPNN model can be used as reliable rainfall-runoff modeling tool in tropical catchments.  ...  Acknowledgement The authors would like to express their gratitude towards the Department of Irrigation and Drainage for provision of hydrological data for Lui River catchment and copiously thankful to  ... 
doi:10.11113/jt.v78.9230 fatcat:yvdplwooafhltf3dmyt3ltk5pm

A Comparative Study Between Artificial Neural Networks and Fuzzy Inference System for Estimation and Filling of Missing Runoff Data at Al-Jawadiyah Station

Alaa Ali Slieman, Dmitry Kozlov, D. Bazarov
2021 E3S Web of Conferences  
best network was reached according to the regression criteria and the root mean of the error squares between the measured values and the predicted values.  ...  Many experiments were conducted, and a very large number of artificial neural networks were trained with changing the number of hidden layers, the number of neurons, and the training algorithms until the  ...  Other researchers used artificial neural network models to estimate rainfall-runoff relations, and the results showed the high reliability of the artificial models according to various scenarios [5] [  ... 
doi:10.1051/e3sconf/202126401048 fatcat:ditftm33anhbvm4ifkpjcyqjve

Rainfall-Runoff Prediction based on Artificial Neural Network: A Case Study Priyadarshini Watershed

S.K. Kothe, B.L. Ayare, H.N. Bhange, S.T. Patil
2019 International Journal of Current Microbiology and Applied Sciences  
When input as rainfall was given and output as observed runoff in neural network toolbox in MATLAB 7.9 training of the network automatically stops whenever recommended output reached with least errors.  ...  Results and Discussion Runoff estimation by using ANN model In the present study, artificial neural network was tested by using logistic sigmoid function and trained with a Levenberg-Marquardt back-propagation  ... 
doi:10.20546/ijcmas.2019.805.151 fatcat:gaetr6zlibf7pmp2hks6j6ecky

Annual Runoff Forecasting Based on Multi-Model Information Fusion and Residual Error Correction in the Ganjiang River Basin

Peibing Song, Weifeng Liu, Jiahui Sun, Chao Wang, Lingzhong Kong, Zhenxue Nong, Xiaohui Lei, Hao Wang
2020 Water  
Finally, according to residual error correction, a modified coupling forecasting model is introduced so as to further improve the accuracy of the predicted annual runoff time series in the verification  ...  forecasting model based on multiple linear regression (MLR), back propagation neural network (BPNN), Elman neural network (ENN), and particle swarm optimization-support vector machine for regression (  ...  Back Propagation Neural Network (BPNN) The back propagation neural network (BPNN) is one of the most widely used neural network models, and it is a multi-layer feedforward network trained based on the  ... 
doi:10.3390/w12082086 fatcat:4l67acfsxrbw3bpj4ld55cfqce

Time Series Data Mining in Real Time Surface Runoff Forecasting through Support Vector Machine

Vinayak Choubey, Satanand Mishra, S. K. Pandey
2014 International Journal of Computer Applications  
This study presents support vector machine based model for forecasting the runoff-rainfall events. A SVM based model is either implemented through Radial base or Gaussian based Kernel functions.  ...  Root Mean Square Error (RMSE), Mean Absolute error (MAE), Mean Squared error (MSE) and correlation coefficient (CC).  ...  The authors would like to thank the Central Water Commission, Ministry of Water Resources, India for providing Water Level and Discharge data.  ... 
doi:10.5120/17163-7223 fatcat:hsnour6fgvdjfh4emdp6ekz6wq

A Semivirtual Watershed Model by Neural Networks

James C. Y. Guo
2001 Computer-Aided Civil and Infrastructure Engineering  
The optimization scheme developed in this study can train the model to establish a set of weights under the guidance of the kinematic wave theory.  ...  The weighting procedure used in the semi-virtual watershed model expands the Rational method from peak runoff predictions to complete hydrograph predictions under continuous and non-uniform rainfall events  ...  Architecture of a neural network is similar to a hydrologic system, but the operation is different.  ... 
doi:10.1111/0885-9507.00217 fatcat:3dfy7ltrx5dslkfj2rcc2eihgy

Multi-criteria validation of artificial neural network rainfall-runoff modeling

R. Modarres
2009 Hydrology and Earth System Sciences  
In this study we propose a comprehensive multicriteria validation test for rainfall-runoff modeling by artificial neural networks.  ...  However, the MLP4 network is the best network to reproduce the mean and variance of the observed runoff based on non-parametric tests.  ...  Various types of neural network models are available for rainfall-runoff modeling.  ... 
doi:10.5194/hess-13-411-2009 fatcat:ar5hjvldrvep3d5jn7knx7kmxm

Rainfall Runoff Modeling using Gene Expression Programming and Artificial Neural Network

2020 International Journal of Engineering and Advanced Technology  
In the present study two data driven modeling approaches, Artificial Neural Network (ANN) and Gene Expression Programming (GEP) has been used for modeling of rainfall-runoff process as these methods does  ...  GEP and ANN are used to model rainfall-runoff relationship for Dindori catchment in upper Narmada River Basin.  ...  In FFBPN error in first iteration is calculated and is back propagated to the network so that error can be minimized in next iteration.Fig 4 showsthe network of FFBPN used in the study for rainfall runoff  ... 
doi:10.35940/ijeat.b4264.029320 fatcat:nhcl7lntdnaqzi7jbzyntelugu
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