Prediction of S12-MKII rainfall simulator experimental runoff data sets using hybrid PSR-SVM-FFA approaches. Issue 2 (28th October 2021)

• Record Type: Journal Article
• Authors: Samantaray, Sandeep; Ghose, Dillip Kumar
• Is Part Of: Journal of water and climate change. Volume 13:Issue 2(2022)
• Page Start: 707
• Page End: 734
• Publication Date: 2021-10-28
• Subjects: PSR-SVM-FFA -- rainfall simulator -- runoff -- SVM -- SVM-FFA; Water -- Periodicals; Hydrology -- Periodicals; Climatic changes -- Periodicals; Climatic changes; Hydrology; Water; Electronic journals; Periodicals; 333.9116
• DOI: 10.2166/wcc.2021.221
• Languages: English
• ISSNs: 2040-2244

Abstract

Abstract: Effective prediction of runoff is a substantial feature for the successful management of hydrological phenomena in arid regions. The present research findings reveal that a rainfall simulator (RS) can be a valuable instrument to estimate runoff as the intensity of rainfall is modifiable in the course of an experimental process, which turns out to be of great advantage. The rainfall-runoff process is a complex physical phenomenon caused by the effect of various parameters. In this research, a new hybrid technique integrating PSR (phase space reconstruction) with FFA (firefly algorithm) and SVM (support vector machine) has gained recognition in various modelling investigations in contrast to the principle of empirical risk minimization through ANN practices. Outcomes of SVM are contrasted against SVM-FFA and PSR-SVM-FFA models. The improvements in NSE (Nash–Sutcliffe efficiency), RMSE (Root Mean Square Error), and WI (Willmott's Index) by PSR-SVM-FFA over SVM models specify that the prediction accuracy of the hybrid model is better. The established PSR-SVM-FFA model generates preeminent WI values that range from 0.97 to 0.98, while the SVM and SVM-FFA models encompass 0.93–0.95 and 0.96–0.97, respectively. The proposed PSR-SVM-FFA model gives more accurate results and error limiting up to 2–3%. HIGHLIGHTS: The models developed in this study using three algorithms are SVM, SVM-FFA, and hybrid PSR-SVM-FFA models. SVM, SVM-FFA, and PSR-SVM-FFA were used, while the earlier studies used simple machine learning approaches for predicting runoff. Interactions of all techniques along with five different scenarios are presented. Watershed has different slopes; hence, six different types of slope conditions have been considered.