A Hybrid Model for River Water Level Forecasting: Cases of Xiangjiang River and Yuanjiang River, China

Abstract The accurate monitoring and early warning of river water level is an important measure to ensure the safety of life and property of river basin residents, and the high precision forecasting of river water level is a vital prerequisite to realize this requirement. Therefore, a hybrid model based on Singular Spectrum Analysis (SSA) method, Group Method of Data Handling (GMDH) neural network, Weighted Integration based on Accuracy and Diversity (WIAD) and Kernel Extreme Learning Machine (KELM) algorithm, namely SSA-WIAD-GMDH-KELM model, is proposed to achieve the forecasting of river water level. The original data collected continuously and real-timely from four monitoring stations of two rivers in China are chosen to prove the high-quality of the proposed hybrid model. In order to investigate the forecasting performance of the proposed hybrid model, several comparison models are selected, which consist of the single GMDH model, SSA-GMDH model, SSA-WIAD-GMDH model and SSA-GMDH-KELM model. The experimental results show that: (1) the prediction effect of the SSA-WIAD-GMDH-KELM model on river water level is satisfactory, which has been verified in four groups of original water level series, and the proposed model has good generalization ability; (2) in the proposed hybrid model, SSA can efficiently extract the principal component of the original series, GMDH has good prediction stability, and both WIAD and KELM can effectively improve the prediction accuracy of the model.