Daily streamflow prediction using optimally pruned extreme learning machine

Abstract

Daily streamflow prediction is important for flood warning, navigation, sediment control, reservoir operations and environmental protection. The current paper examines the prediction and estimation capability of a new heuristic method, optimally pruned extreme learning machine (OP-ELM) model, for daily streamflows of Fujiangqiao and Shehang stations at Fujiang River. Prediction accuracy of OP-ELM method is compared with other soft computing models, i.e. adaptive neuro-fuzzy inference system-particle swarm optimization (ANFIS-PSO), multivariate adaptive regression splines (MARS) and M5 model tree (M5Tree) using cross validation technique. Prediction results of the both stations reported that the OP-ELM and ANFIS-PSO are the best in modeling daily streamflows of upstream and downstream, respectively. For improving prediction accuracy of the OP-ELM method, various kernel types are tried and the linear, linear + sigmoid + Gaussian and linear + sigmoid provide the best results for both stations. The OP-ELM outperforms the other methods during estimation of downstream streamflow using hydro climatic data as input. The OP-ELM reduces the prediction error of ANFIS-PSO by 12% in estimation of daily streamflow. It is also found that including local data considerably improves the prediction accuracy in estimation of downstream streamflows. The overall results indicate that the OP-ELM method could be successfully used in predicting and estimating daily streamflow by using hydro climatic data as inputs.