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• W4386906010 startingPage "108253" @default.
• W4386906010 abstract "Although the accurate prediction of the Standardized Precipitation Evapotranspiration Index (SPEI) is considered meaningful in reducing drought losses, its wide applications are limited to substantial meteorological data requirements. Considering Long Short-Term Memory network (LSTM) has proved its potential in estimating drought index, the concern is justified regarding the performance of its variants for estimating SPEI using limited meteorological input at the national level. Therefore, this study established the SPEI models using empirical methods, SVM, RNN, LSTM, BiLSTM, and CNN-LSTM, respectively, to cope with different data-missing scenarios. Based on a comprehensive comparison among different methods for multiscale SPEI estimation at four climatic zones of China, the results showed that BiLSTM was the most recommended model for estimating SPEI at 3-month timescale, with R2, NSE, and RMSE ranging 0.916–0.997, 0.907–0.997, and 0.143–0.353, respectively. Whereas CNN-LSTM was more suitable for other timescales, with R2, NSE, and RMSE being 0.904–0.999, 0.858–0.989, and 0.145–0.365 for estimating SPEI at 6-month timescale, respectively, and 0.858–0.998, 0.795–0.991, and 0.081–0.568 for estimating SPEI at 12-month timescale. Generally, the accuracy performance of SPEI methods can be ranked from best to worst as LSTM-type models, SVM, RNN, and empirical methods. The exception was that H-S exceeded RNN3 in TCZ and MPZ by 4.1–30.0 % for R2, 4.1–65.0 % for NSE, and 2.3–19.6 % for RMSE, respectively. Moreover, this study found that the accuracy performance of machine learning models for SPEI estimation got worse with the number of independent variables decreased. Overall, the variants of LSTM exerted excellent performance for multiscale SPEI estimation, which provided the most accurate prediction of meteorological, agroecological, and hydrological droughts throughout China." @default.
• W4386906010 created "2023-09-21" @default.
• W4386906010 creator A5004387898 @default.
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• W4386906010 date "2023-10-01" @default.
• W4386906010 modified "2023-10-16" @default.
• W4386906010 title "Standardized precipitation evapotranspiration index (SPEI) estimated using variant long short-term memory network at four climatic zones of China" @default.
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• W4386906010 cites W2017732481 @default.
• W4386906010 cites W2021432630 @default.
• W4386906010 cites W2064675550 @default.
• W4386906010 cites W2077968790 @default.
• W4386906010 cites W2079735306 @default.
• W4386906010 cites W2085683636 @default.
• W4386906010 cites W2112796928 @default.
• W4386906010 cites W2133872520 @default.
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• W4386906010 cites W2156909104 @default.
• W4386906010 cites W2165772416 @default.
• W4386906010 cites W2172396214 @default.
• W4386906010 cites W2195388612 @default.
• W4386906010 cites W2267186426 @default.
• W4386906010 cites W2527066115 @default.
• W4386906010 cites W2610778436 @default.
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• W4386906010 cites W2972763557 @default.
• W4386906010 cites W2977638463 @default.
• W4386906010 cites W2984208750 @default.
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• W4386906010 doi "https://doi.org/10.1016/j.compag.2023.108253" @default.
• W4386906010 hasPublicationYear "2023" @default.
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