The superior coupled model with innovative strategy for accurate imputation of missing hydrological monitoring data in water research-A case of groundwater level data - Xian-Qi Zhang; Zi-Yu Li; Xian-Liang Liu; Xiao-Yan Wu; Xin-Yong Xu; En-Kuan Li; Bo-Wen Wang; Su-Bo Han; Shao-Bo Liu (Water Research X)
The missing hydrological monitoring data pose challenges to regional water resource analysis and research. This paper proposes a strategy of “Decomposition-Classification-Feature Extraction-Imputation” and establishes a coupled model integrating Extreme-point Symmetric Mode Decomposition (ESMD), Permutation Entropy (PE), Singular Value Decomposition (SVD), and Whale Optimization Algorithm-Bidirectional Long Short-Term Memory (WOA-BiLSTM) for the hydrological monitoring data imputation. The practicality of this model in imputing discrete and continuous missing data is verified using daily groundwater level data from monitoring stations around Qinghai Lake, spanning from April 1, 2019, to June 30, 2020. The results show that ESMD can decompose the groundwater level sequences into IMFs and R; the PE-SVD method for complexity-based reclassification and feature extraction can reasonably reduce the number of components while guaranteeing imputing accuracy. Compared with other models, the ESMD-PE-SVD-WOA-BiLSTM coupled model has the best imputing accuracy (MAE=17.43, RMSE=21.08, MAPE=0.32 %, IoA=90 %), providing an effective and reliable tool for precise hydrological monitoring data imputation and regional water resource research.