Multiple Types of Missing Precipitation Data Filling Based on Ensemble Artificial Intelligence Models

The completeness of precipitation observation data is a crucial foundation for hydrological simulation, water resource analysis, and environmental assessment. Traditional data imputation methods suffer from poor adaptability, lack of precision, and limited model diversity. Rapid and accurate imputation using available data is a key challenge in precipitation monitoring. This study selected precipitation data from the Jiaojiang River basin in the southeastern Zhejiang Province of China from 1991 to 2020. The data were categorized based on various missing rates and scenarios, namely MCR (Missing Completely Random), MR (Missing Random), and MNR (Missing Not Random). Imputation of precipitation data was conducted using three types of Artificial Intelligence (AI) methods (Backpropagation Neural Network (BPNN), Random Forest (RF), and Support Vector Regression (SVR)), along with a novel Multiple Linear Regression (MLR) imputation method built upon these algorithms. The results indicate that the constructed MLR imputation method achieves an average Pearson's correlation coefficient (PCC) of 0.9455, an average Nash-Sutcliffe Efficiency (NSE) of 0.8329, and an average Percent Bias (Pbias) of 10.5043% across different missing rates. MLR simulation results in higher NSE and lower Pbias than the other three single AI models, thus effectively improving the estimation performance. The proposed methods in this study can be applied to other river basins to improve the quality of precipitation data and support water resource management.