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Rainfall prediction is a very important guideline for water resources management as well as ecological protection, and its changes are the result of multiple factors with obvious uncertainties and nonlinearities. Based on the advantages of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) non-smooth signal decomposition, the Particle Swarm Optimization (PSO) can be used to optimize the input weights and thresholds of the Extreme Learning Machine (ELM), which can effectively improve the prediction effect and accuracy of ELM, and a rainfall prediction model based on CEEMDAN-PSO-ELM is constructed. The model is applied to the monthly rainfall prediction of Zhongwei city, and the results show that the CEEMDAN-PSO-ELM coupled model has a high prediction accuracy, the mean absolute error (MAE) is 1.29, relative percentage error (RPE) is 0.45, root mean square error (RMSE) is 1.43 and the nash efficiency coefficient (NSE) is 0.93. It has obvious advantages in hydrological simulation prediction when compared and analyzed the deep Long-Short Term Memory (LSTM), PSO-ELM coupled model and ELM model.

The simulation of precipitation changes can provide references for the prediction and prevention of flood disasters, and has guiding significance for the comprehensive utilization of regional water resources. Precipitation forecasting is difficult due to the randomness and uncertainty of precipitation events. CEEMD can effectively overcome modal aliasing and white noise interference. The WTD process has obvious denoising effects on the original signal. GRU can effectively solve long-term memory and reflection. Based on the advantages of problems such as gradients in propagation, a CEEMD-WTD-GRU precipitation prediction coupling model is constructed. The second decomposition of CEEMD-WTD-GRU can more effectively extract complex time series information. The time series forecasting provided a new method, which effectively improved the accuracy of the forecast and applied it to the forecast of monthly precipitation in Shanghai. The research results show that the average absolute error of the CEEMD-WTD-GRU model is 3.86, the average relative error is 3.30%, and the Nash efficiency coefficient is 0.99. The prediction accuracy is better than the CEEMD-WTD-GRU model without noise reduction, the CEEMD-LSTM model and GRU model, which shows that it has strong nonlinear and complex process learning ability in hydrological factor simulation, and can be used for regional precipitation prediction.