Abstract
Reliable precipitation forecasting is essential for effective water management and timely warning of natural disasters such as floods and droughts. However, precipitation is a nonlinear water vapor cycle with certain spatial and temporal dependence, and stable prediction accuracy cannot be obtained by using a single model. Therefore, this paper proposes a novelty prediction model based on original feature extraction and an improved multi-objective swarm intelligence optimization algorithm, and it carries out multi-step prediction tests for two sites in the arid/semi-arid region (Qilian Mountain-Hexi Corridor). Finally, through the 19 comparison models, 5 evaluation indexes and 3 model performance tests, it is confirmed that the precipitation combined forecasting model constructed in this study is a reliable prediction system with optimal parameters. And it can provide favorable technical support for weather forecasting.
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