Precipitation is the most basic part of the water cycle process. Aiming at the problem of low prediction accuracy caused by the nonlinear and unstable characteristics of the precipitation series, a new precipitation prediction method based on the CEEMDAN-IWOA-BP coupling model is proposed. This method first uses the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose the original precipitation sequence, and obtains a series of intrinsic mode function (IMF)

more »

... nd residual terms (Res) as inherent potential influencing factors, innovatively introduce TENT chaotic mapping and roulette algorithm to improve the Whale Optimization Algorithm (WOA), use IMFs and Res as the input of the Improve Whale Optimization Algorithm (IWOA) to optimize Back Propagation (BP) neural network prediction model, and finally superimpose the predicted values ​​as ultima result.The present method was applied to predict the annual precipitation from 1958 to 2017 in Sichuan Province.Compared with the prediction results of other models, the CEEMDAN-IWOA-BP coupled model has significantly improved prediction accuracy than the single model, and the prediction error index of the Back Propagation(BP) neural network optimized by the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms is smaller, and the optimization accuracy and solving ability are significantly enhanced compared with the unimproved WOA.