A Monthly Precipitation Ensemble Prediction Model Based on LSTM and Shapley Values

 In the current short-term climate prediction of monthly precipitation, there is a lack of nonlinear data mining techniques and objective ensemble forecasting methods of machine learning. A new nonlinear deep learning ensemble objective forecasting model has been established by generating multiple long short-term memory neural networks (LSTMs) with the same expected output as the individual forecasters, and using the cooperative game Shapley value method to determine the weight coefficients of each forecaster in the ensemble forecasting. The forecasting modeling of the monthly precipitation forecasting model has been studied based on the July precipitation samples of 81 meteorological stations in Guangxi from 1960 to 2023, and using height fields, temperature fields, and sea surface temperature field as the basic forecasting factors for monthly precipitation. The experimental results show that under the same forecast modeling samples and forecast factor conditions, the newly established prediction model has higher predictive ability than linear stepwise regression prediction methods and a single LSTM model, demonstrating its applicability to nonlinear monthly precipitation prediction problems. Further analysis reveals that the introduction of storage unit states and gate structures in the hidden layer of the LSTM model enables the network to retain long-term states, making it more suitable for handling and predicting important problems with relatively long intervals and delays in time series. And the Shapley value method can improve the predictive ability of ensemble individuals and enhance the population diversity of ensemble individuals, thereby improving the predictive accuracy of ensemble forecasting models. Therefore, the generalization ability of this deep learning ensemble forecasting model is significantly improved, and the improvement of its forecasting ability has a reasonable analytical basis. There is no overfitting phenomenon in the practical short-term climate prediction business application of general neural network methods, and it has good practical application value.