Assessment of Machine Learning Techniques for Monthly Flow Prediction

作     者：Alizadeh, Zahra Yazdi, Jafar Kim, Joong Hoon Al-Shamiri, Abobakr Khalil 

作者机构：Shahid Beheshti Univ Fac Civil Water & Environm Engn Tehran *** Iran Korea Univ Sch Civil Environm & Architectural Engn Seoul 136713 South Korea 

出 版 物：《WATER》 (Water)

年 卷 期：2018年第10卷第11期

页      面：1676页

核心收录：

基　　金：National Research Foundation of Korea (NRF) - Korean government (MSIP) [2016R1A2A1A05005306] 

主　　题：Gaussian process regression grasshopper optimization algorithm K-nearest neighbor regression neural network support vector machine 

摘      要：Monthly flow predictions provide an essential basis for efficient decision-making regarding water resource allocation. In this paper, the performance of different popular data-driven models for monthly flow prediction is assessed to detect the appropriate model. The considered methods include feedforward neural networks (FFNNs), time delay neural networks (TDNNs), radial basis neural networks (RBFNNs), recurrent neural network (RNN), a grasshopper optimization algorithm (GOA)-based support vector machine (SVM) and K-nearest neighbors (KNN) model. For this purpose, the performance of each model is evaluated in terms of several residual metrics using a monthly flow time series for two real case studies with different flow regimes. The results show that the KNN outperforms the different neural network configurations for the first case study, whereas RBFNN model has better performance for the second case study in terms of the correlation coefficient. According to the accuracy of the results, in the first case study with more input features, the KNN model is recommended for short-term predictions and for the second case with a smaller number of input features, but more training observations, the RBFNN model is suitable.