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Daily streamflow prediction using optimally pruned extreme learning machine
- Adnan, Rana Muhammad, Liang, Zhongmin, Trajkovic, Slavisa, Zounemat-Kermani, Mohammad, Li, Binquan, Kisi, Ozgur
- Journal of hydrology 2019 pp. 123981
- algorithms, environmental protection, fuzzy logic, prediction, rivers, sediments, stream flow
- 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.