Abstract
Abstract
Forecasting streamflow (Qflow) is vital in flood and water management, determining potential of river water flow, agricultural practices, hydropower generation, and environmental flow study. This research aims to explore capability of hybrid support vector machines (SVM) with Whale Optimisation Algorithm (WOA) model for forecasting streamflow at Badarpur Ghat gauging station of Barak river basin and evaluate its enactment with the conventional SVM model. Root mean squared error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency (NSE) statistical measures are considered as evaluating standards. Assessment of outcomes indicates that the optimization algorithm could enhance the accurateness of standalone SVM model in monthly streamflow forecasting. Compared to conventional artificial intelligence methods without a data pre-processing system, the comparatively good performance of applied hybrid model gives an effective alternate to achieve better precision in streamflow forecasting. Results confirm that enhanced SVM model can better process a multifaceted hydrogeological data set, have higher prediction accuracy, and possess better generalisation capability.
Reference41 articles.
1. Optimal sizing of utility-scale photovoltaic power generation complementarity operating with hydropower: A case study of the world’s largest hydro-photovoltaic plant Energy Convers;Fang;Manag,2017
2. A flexible-possibilistic stochastic programming method for planning municipal-scale energy system through introducing renewable energies and electric vehicles;Yu;J. Clean. Prod.,2019
3. Enhancing long-term streamflow forecasting and predicting using periodicity data component: application of artificial intelligence Water Resour;Yaseen;Manag.,2016
4. SWAT model calibration and uncertainty analysis for streamflow prediction in the Kunwari River Basin, India, using sequential uncertainty fitting Environ;Narsimlu;Process,2015
5. Nonstationarity of hydrological records and recent trends in trend analysis: a state-of-the-art review Environ;Bayazit;Process,2015