Prediction of S12-MKII rainfall simulator experimental runoff data sets using hybrid PSR-SVM-FFA approaches

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%.

Computing River Discharge Using Water Surface Elevation Based on Deep Learning Networks

“…The four metrics used in this study to evaluate the performance of deep learning networks are relative mean absolute percentage error (RMSE), Nash-Sutcliffe efficiency coefficient (NSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). This research chose these metrics because they are widely used in previous literatures [36][37][38][39]. Smaller values of MAE, RMSE, and MAPE indicate better performance of the network in terms of the difference between measured and estimated data.…”