A novel rainfall prediction model based on CEEMDAN-PSO-ELM coupled model

Abstract

Abstract 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 with the deep Long-Short Term Memory (LSTM), PSO-ELM coupled model and ELM model.