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Performance Analysis of Soft-Computing Techniques in Rainfall-Runoff Modeling over a River Basin

Sanjeev Karmaka *, Pradeep Kumar Mishr**, Shreerup Goswami***

*-** Bhilai Institute of Technology, Durg, Chhattisgarh, India.

***Sambalpur University, Burla, Odisha, India.

Periodicity:August - October'2021
DOI : https://doi.org/10.26634/jfet.17.1.17454

Abstract

Rainfall-Runoff (R-R) modelling is a challenging and operational task for hydrological scientists. For the past few years, the scientific community has been suggesting soft computing techniques to solve this problem. In this study, various types of soft computing such as Back Propagation Neural Network (BPN), Radial Basis Function (RBF), Support Vector Machine (SVM), Fuzzy Logic (FL) and Genetic Algorithm (GA) were analyzed systematically. BPN and RBF were found to be more suitable to address R-R modeling, but BPN proved to be more appropriate than RBF. During the test performance, it was observed that the average deviation of R-R from the actual was 23 (% of LPA) while RBF produced 43.6 (% of LPA). All these facts have been described in this paper.

Keywords

Rainfall-Runoff (R-R), Long Period Average (LPA), Back Propagation Neural Network (BPN), Radial Basis Function (RBF), Support Vector Machine (SVM), Genetic Algorithm (GA), Fuzzy Logic (FL), Soft Computing Techniques, River Basin.

How to Cite this Article?

Karmaka, S., Mishr, P. K., and Goswami, S. (2021). Performance Analysis of Soft-Computing Techniques in Rainfall-Runoff Modeling over a River Basin. i-manager’s Journal on Future Engineering & Technology, 17(1), 10-15. https://doi.org/10.26634/jfet.17.1.17454

References

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Pdf & Online	35	35	400	25

Performance Analysis of Soft-Computing Techniques in Rainfall-Runoff Modeling over a River Basin

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