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- W4385864122 abstract "This paper explores the ability of deep learning models to predict drought phenomena. Taking the Songliao River Basin as an example, the Standardized Precipitation Evapotranspiration Index (SPEI) is used as the drought index predicted by the models, and the prediction accuracy of Autoregressive Integrated Moving Average model (ARIMA), Long Short-Term Memory (LSTM), and Informer models for SPEI values at multiple time scales is analyzed and compared, and the prediction performance of the models is evaluated using NSE, RMSE, and MAE. The results show that (1) the prediction accuracy of the ARIMA, LSTM, and Informer models for SPEI values gradually improves as the time scale increases; (2) the prediction performance of all three models is optimal at the 24-month time scale, with NSE values of 0.928, 0.854, and 0.934; RMSE values of 0.185, 0.295, and 0.169; and MAE values of 0.104, 0.221, and 0.108, respectively; (3) The Informer model outperforms the ARIMA and LSTM models in forecasting at any time scale. Overall, the Informer model is more suitable for drought prediction in the Songliao River Basin." @default.
- W4385864122 created "2023-08-17" @default.
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- W4385864122 date "2023-07-07" @default.
- W4385864122 modified "2023-10-17" @default.
- W4385864122 title "Application of Drought Prediction Based on Deep Learning" @default.
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- W4385864122 doi "https://doi.org/10.1109/isctis58954.2023.10213059" @default.
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