LSTM-CM: a hybrid approach for natural drought prediction based on deep learning and climate models

摘要

Droughts cause severe damage to the economy, society, and environment. Drought forecasting plays an important role in establishing mitigation drought damage plans. In this study, a hybrid model involving long short-term memory and a climate model (LSTM-CM) is constructed for drought prediction. LSTM-CM was compared to the long short-term model stand-alone (LSTM-SA) and climate prediction model GloSea5 (GS5). The performance of models was evaluated based on the Pearson correlation coefficient (CC), mean absolute error (MAE), root mean squared error (RMSE), and skill score (SS). GS5 displayed physical robustness in predictions and did not reduce the amplitude or shift results. However, GS5 prediction tends to have a large bias caused by the inputs, model structure, and parameters. The MAEs of GS5 at 1, 2 and 3 months (0.41, 0.68, and 0.89) were higher than those of LSTM-SA (0.38, 0.61, and 0.89). The LSTM-SA reduced bias, but predictions were characterized by shifts, small variance, and failure to capture drought occurrences in long-lead-time cases. LSTM-CM yielded enhanced drought predictions by encompassing the low bias of LSTM-SA and the physical process simulation ability of GS5; thus, it inherited the good features of these models and limited the poor features. The SS values based on the CC, MAE, and RMSE of LSTM-CM compared to those of GS5 for 1 -, 2-, and 3-month lead time predictions were improved from 29.17 to 54.29, 22.47 to 34.15, and 1.75 to 35.09, respectively. LSTM-CM can accurately detect drought events and displayed less uncertainty in prediction than LSTM-SA and GS5.