Prediction of Flood Discharge Using Hybrid PSO-SVM Algorithm in Barak River Basin

A crucial necessity in integrated water resource management is flood forecasting. Climate forecasts, specifically flood prediction, comprise multifaceted tasks as they are dependant on several parameters for predicting the dependant variable, which varies from time to time. Calculation of these parameters also changes with geographical location. From the time when Artificial Intelligence was first introduced to the field of hydrological modelling and prediction, it has produced enormous attention in research aspects for additional developments to hydrology. This study investigates the usability of support vector machine (SVM), back propagation neural network (BPNN), and integration of SVM with particle swarm optimization (PSO-SVM) models for flood forecasting. Performance of SVM solely depends on correct assortment of parameters. So, PSO method is employed in selecting SVM parameters. Monthly river flow discharge for a period of 1969 - 2018 of BP ghat and Fulertal gauging sites from Barak River flowing through Barak valley in Assam, India were used. For obtaining optimum results, different input combinations of Precipitation (P_t), temperature (T_t), solar radiation (Sr), humidity (H_t), evapotranspiration loss (E_l) were assessed. The model results were compared utilizing coefficient of determination (R²) root mean squared error (RMSE), and Nash-Sutcliffe coefficient (N_SE). The most important results are highlighted below.•First, the inclusion of five meteorological parameters improved the forecasting accuracy of the hybrid model.•Second, model comparison specifies that hybrid PSO-SVM model executed superior performance with RMSE- 0.04962 and NSE- 0.99334 compared to BPNN and SVM models for monthly flood discharge forecasting.•Third, applied optimization algorithm has easy implementation, simple theory, and high computational efficacy. Results revealed that PSO-SVM could be utilised as an improved alternate method for flood forecasting as it provided a higher degree of reliability and accurateness.