A novel approach to precipitation prediction using a coupled CEEMDAN-GRU-Transformer model with permutation entropy algorithm.

The accurate forecasting of precipitation in the upper reaches of the Yellow River is imperative for enhancing water resources in both the local and broader Yellow River basin in the present and future. While many models exist for predicting precipitation by analyzing historical data, few consider the impact of different frequency sequences on model accuracy. In this study, we propose a coupled monthly precipitation prediction model that leverages the adaptive noise complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), gated recurrent unit neural network (GRU), and attention mechanism-based transformer model. The permutation entropy (PE) algorithm is employed to partition the data processed by CEEMDAN into different frequencies, with different models utilized to predict different frequencies. The predicted results are subsequently combined to obtain the monthly precipitation prediction value. The model is applied to precipitation prediction in four regions in the upper reaches of the Yellow River and compared with other models. Evaluation results demonstrate that the CEEMDAN-GRU-Transformer model outperforms other models in predicting precipitation for these regions, with a coefficient of determination R2 greater than 0.8. These findings suggest that the proposed model provides a novel and effective method for improving the accuracy of regional medium and long-term precipitation prediction.

Monthly precipitation prediction in Luoyang city based on EEMD-LSTM-ARIMA model.

At present, the method of using coupled models to model different frequency subseries of precipitation series separately for prediction is still lacking in the research of precipitation prediction, thus in this paper, a coupled model based on Ensemble Empirical Mode Decomposition (EEMD), Long Short-Term Memory neural network (LSTM) and Autoregressive Integrated Moving Average (ARIMA) is proposed for month-by-month precipitation prediction. The monthly historical precipitation data of Luoyang City from 1973 to 2021 were used to build the model, and the modal components of different frequencies obtained by EEMD decomposition were divided into high-frequency series part and low-frequency series part using the Permutation Entropy (PE) algorithm, the LSTM model is used to predict the high-frequency sequence part, while the ARIMA model is used to predict the low-frequency sequence part. Monthly precipitation forecasts are obtained by superimposing the results of the two models. Finally, the predictive performance is evaluated using several assessment metrics. The indicators show that the model predictive performance outperforms the EMD-LSTM (Empirical Mode Decomposition), EEMD-LSTM, EEMD-ARIMA combined models and the single models, and the model has high confidence in the prediction results of future precipitation.