Modelling runoff in an arid watershed through integrated support vector machine
Sandeep Samantaray, Dillip K. Ghose
Abstract
Abstract Modelling of runoff is a significant practice in water resources engineering. Therefore, discovering consistent and advanced methods for prediction of runoff is crucial for hydrologic processes. Here, a narrative integrated intelligence model attached with PSR (phase space reconstruction) is anticipated to estimate runoff for five watersheds of Balangir, Odisha, India. Monthly monsoon precipitation, temperature, humidity data of five watersheds over 28 years (1990–2017) are employed and validated. Here, the proposed model is an integration of support vector machine (SVM) with firefly algorithm (FFA) and PSR. Various indices such as NSE (Nash–Sutcliffe), RMSE (root mean square error) and WI (Willmott's index) are used to find the performance of the model. The developed PSR-SVM-FFA model demonstrates pre-eminent WI value ranging from 0.97 to 0.98 while the SVM and SVM-FFA models encompass 0.92 to 0.93 and 0.94 to 0.95, respectively. Also, an assessment of data from the suggested model is schemed and validated. The proposed PSR-SVM-FFA model gives better accuracy results and error limiting up to 2–3%.