Research on the Application of CEEMD-LSTM-LSSVM Coupled Model in Regional Precipitation Prediction

Precipitation is a vital component of the regional water resource circulation system. Accurate and efficient precipitation prediction especially important in context global warming, as it can help explore pattern promote comprehensive utilization. However, due to influence many factors, process exhibits significant stochasticity, uncertainty, nonlinearity despite having some regularity. In this article, monthly Zhoukou City predicted using complementary ensemble empirical modal decomposition (CEEMD) method combined with long short-term memory neural network (LSTM) model least squares support vector machine (LSSVM) model. The results demonstrate that CEEMD-LSTM-LSSVM root mean square error 15.01 absolute 11.31 predicting City. effectively overcomes problems confounding present (EMD), existence reconstruction errors (EEMD), lack accuracy single LSTM components different frequencies obtained by EEMD decomposition. provides an effective approach for future area predicts study from 2023 2025. reference relevant departments take measures against natural disasters rationally plan urban resources.