Application of neuro-genetic algorithm to determine reservoir response in different hydrologic adversaries

Majumder, Mrinmoy; Barman, Rabindra Nath; Roy, Priti Kumar; Jana, Bipal K.; Mazumdar, Asis

doi:10.17221/32/2008-swr

Cited by 3 publications

(2 citation statements)

References 13 publications

(13 reference statements)

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“…The dataset is also used for cross-validation to prevent over-training during the training phase. (MAJUMDER, 2009). Model characteristics of the neuro-genetic models developed for the present study is shown in Table 3.…”

Section: Phase 4: Testing Phasementioning

confidence: 99%

Fluoride contamination - Artificial neural network modeling and inverse distance weighting approach

Dar

Sankar

Dar

et al. 2012

rseau

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The underground waters in the Mamundiyar basin, India, present real chemical quality problems. Their fluoride content always exceeds the recommended levels. The Inverse Distance Weighted (IDW) method has been used for spatial interpolation of various key chemical parameters. Artificial Neural Network (ANN) modeling was applied to understand the correlation and sensitivity of all chemical parameters with respect to fluorides. The correlation of all the considered parameters is found to be poor where the highest correlation observed was only 0.37. This result showed that four of the parameters, namely pH, chlorides, sulphates and calcium, were found to have greater capacity of influencing fluorides than the other eight parameters. Chlorides were found to be the parameter that was the most sensitive and most correlated to fluorides.

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Section: Phase 4: Testing Phasementioning

confidence: 99%

Fluoride contamination - Artificial neural network modeling and inverse distance weighting approach

Dar

Sankar

Dar

et al. 2012

rseau

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“…Genetic algorithms are search algorithms based on the mechanics of natural genetic and natural selection. The basic elements of natural genetics-reproduction, crossover, and mutation-are used in the genetic search procedure (Majumdar et al 2009). …”

Section: Network Building Proceduresmentioning

confidence: 99%

A comparative study for prediction of direct runoff for a river basin using geomorphological approach and artificial neural networks

et al. 2011

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Traditional techniques for estimation of flood using historical rainfall-runoff data are restricted in application for small basins due to poor stream gauging network. To overcome such difficulties, various techniques including those involving the morphologic details of the ungauged basin have been evolved. The geomorphologic instantaneous unit hydrograph method belongs to the latter approach. In this study, a gamma geomorphologic instantaneous unit hydrograph (GGIUH) model (based on geomorphologic characteristics of the basin and the Nash instantaneous unit hydrograph model) was calibrated and validated for prediction of direct runoff (flood) from the catchment of the Dulung-Nala (a tributary of the Subarnarekha River System) at Phekoghat station in the state of West Bengal in the eastern part of India. Sensitivity analysis revealed that a change in the model parameters viz., n, R A and R B by 1-20% resulted in the peak discharge to vary from 1.1 to 27.2%, 3.4 to 21.2% and 3.4 to 21.6%, respectively, and the runoff volume to vary from 0.3 to 12.5%, 2.1 to 2.6% and 2.2 to 2.7%, respectively. The Nash-Sutcliffe model efficiency criterion, percentage error in volume, the percentage error in peak, and net difference of observed and simulated time to peak which were used for performance evaluation, have been found to range from 74.2 to 95.1%, 2.9 to 20.9%, 0.1 to 20.8% and -1 to 3 h, respectively, indicating a good performance of the GGIUH model for prediction of runoff hydrograph. Again, an artificial neural network (ANN) model was prepared to predict ordinates of discharge hydrograph using calibrative approach. Both the ANN and GGIUH models were found to have predicted the hydrograph characteristics in a satisfactory manner. Further, direct surface runoff hydrographs computed using the GGIUH model at two map scales (viz. 1:50,000 and 1:250,000) were found to yield comparable results for the two map scales. For a final clarification, the probability density function of the actual and predicted data from the two models was prepared to compare the pattern identification ability of both the models. The GGIUH model was found to identify the distribution pattern better than the ANN model, although both the models were found to be ably replicating the data patterns of the observed dataset.

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Determination of Evapotranspiration from Stream Flow with the Help of Classified Neurogenetic Model

Majumder

Roy

Mazumdar

2010

Impact of Climate Change on Natural Resource Management

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Application of neuro-genetic algorithm to determine reservoir response in different hydrologic adversaries

Cited by 3 publications

References 13 publications

Fluoride contamination - Artificial neural network modeling and inverse distance weighting approach

Fluoride contamination - Artificial neural network modeling and inverse distance weighting approach

A comparative study for prediction of direct runoff for a river basin using geomorphological approach and artificial neural networks

Determination of Evapotranspiration from Stream Flow with the Help of Classified Neurogenetic Model

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