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Rainfall-Runoff Modeling of Sutlej River Basin (India) Using Soft Computing Techniques

Rainfall-Runoff Modeling of Sutlej River Basin (India) Using Soft Computing Techniques

Athar Hussain, Jatin Kumar Singh, A. R. Senthil Kumar, Harne K R
Copyright: © 2019 |Volume: 10 |Issue: 2 |Pages: 20
ISSN: 1947-3192|EISSN: 1947-3206|EISBN13: 9781522566731|DOI: 10.4018/IJAEIS.2019040101
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MLA

Hussain, Athar, et al. "Rainfall-Runoff Modeling of Sutlej River Basin (India) Using Soft Computing Techniques." IJAEIS vol.10, no.2 2019: pp.1-20. http://doi.org/10.4018/IJAEIS.2019040101

APA

Hussain, A., Singh, J. K., Kumar, A. R., & Harne K R. (2019). Rainfall-Runoff Modeling of Sutlej River Basin (India) Using Soft Computing Techniques. International Journal of Agricultural and Environmental Information Systems (IJAEIS), 10(2), 1-20. http://doi.org/10.4018/IJAEIS.2019040101

Chicago

Hussain, Athar, et al. "Rainfall-Runoff Modeling of Sutlej River Basin (India) Using Soft Computing Techniques," International Journal of Agricultural and Environmental Information Systems (IJAEIS) 10, no.2: 1-20. http://doi.org/10.4018/IJAEIS.2019040101

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Abstract

The prediction of the runoff generated within a watershed is an important input in the design and management of water resources projects. Due to the tremendous spatial and temporal variability in precipitation, rainfall-runoff relationship becomes one of the most complex hydrologic phenomena. Under such circumstances, using soft computing approaches have proven to be an efficient tool in modeling of runoff. These models are capable of predicting river runoff values that can be used for hydrologic and hydraulic engineering design and water management purposes. It has been observed that the artificial neural networks (ANN) model performed well compared to other soft computing techniques such as fuzzy logic and radial basis function investigated in this study. In addition, comparison of scatter plots indicates that the values of runoff predicted by the ANN model are more precise than those found by RBF or Fuzzy Logic model.