Improving Accuracy of River Flow Forecasting Using LSSVR with Gravitational Search Algorithm

Abstract

River flow prediction is essential in many applications of water resources planning and management. In this paper, the accuracy of multivariate adaptive regression splines (MARS), model 5 regression tree (M5RT), and conventional multiple linear regression (CMLR) is compared with a hybrid least square support vector regression-gravitational search algorithm (HLGSA) in predicting monthly river flows. In the first part of the study, all three regression methods were compared with each other in predicting river flows of each basin. It was found that the HLGSA method performed better than the MARS, M5RT, and CMLR in river flow prediction. The effect of log transformation on prediction accuracy of the regression methods was also examined in the second part of the study. Log transformation of the river flow data significantly increased the prediction accuracy of all regression methods. It was also found that log HLGSA (LHLSGA) performed better than the other regression methods. In the third part of the study, the accuracy of the LHLGSA and HLGSA methods was examined in river flow estimation using nearby river flow data. On the basis of results of all applications, it was found that LHLGSA and HLGSA could be successfully used in prediction and estimation of river flow.