ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Research on Objective Prediction Method for Summer Precipitation in Eastern Northwest China Based on Multi-Deep-Learning Algorithm Fusion
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Abstract:
Based on the precipitation data of 155 meteorological stations in the eastern part of Northwest China from 1961 to 2022, as well as historical datasets including global atmospheric data, sea surface temperature (SST), and sea ice data, this study integrates the Temporal Convolutional Network(TCN) module and the Convolutional Block Attention Module(CBAM) into the Long Short-Term Memory (LSTM) deep learning algorithm. A climate-smart prediction model for summer precipitation in the eastern part of Northwest China(named CBAM-TCN-LSTM) based on the fusion of deep learning algorithms was thereby established. The predictive performance of the model was verified, and its predictive capability was compared with that of multiple other deep learning algorithms.The results show that the intelligent prediction model based on the fusion of multiple deep learning algorithms outperforms single-algorithm deep learning models. During the independent sample validation period(2018–2022), the PS score for summer precipitation prediction ranged from 60% to 80%, with an average value of 73.8%.The Anomaly Correlation Coefficient (ACC) was positive for all years except 2020, with a mean value of 0.14, representing a significant improvement over other models. When compared with 7 current mainstream models (including machine learning algorithms, deep learning networks, and time-series networks), the CBAM-TCN-LSTM model exhibited superior performance across all five evaluation metrics: PS, PC, ACC, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). Furthermore, the CBAM-TCN-LSTM model was successfully applied in the 2023 flood season forecasting operation, accurately predicting the characteristic of below-normal summer precipitation in most areas of the eastern part of Northwest China, with a PS score of 90%.By building a precipitation prediction model that combines the TCN module and the CBAM module on the basis of the LSTM model (which has strong time-series predictive capability), this study provides a scientific basis and technical support for regional precipitation prediction, and the model holds good prospects for popularization and application.
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