Applicability of the LSTM and ARIMA model in drought prediction based on CEEMD: A case study of Xinjiang
Received date: 2021-10-12
Revised date: 2021-12-30
Online published: 2022-05-30
The frequent occurrence of droughts seriously affects normal agricultural production and economic development. Accurate prediction of drought occurrence is of great importance in reducing drought losses. Nevertheless, drought occurrences have not been well predicted. Drought indices can be used to quantitatively evaluate the intensity, duration, and influence range of drought. Thus, on the basis of daily precipitation data from 1960 to 2019 in the Xinjiang Uyghur Autonomous Region, the standardized precipitation index (SPI) at timescales of 1, 3, 6, 9, 12, and 24 months were calculated. Aiming for the nonlinear and nonstationary characteristics of SPI, a new drought prediction method was proposed combining the single model and the complementary ensemble empirical mode decomposition (CEEMD), which can process nonlinear and nonstationary signals. In this paper, the autoregressive integrated moving average (ARIMA) model, the long short-term memory (LSTM) network, the CEEMD-ARIMA combined model, and the CEEMD-LSTM combined model were constructed to predict a multiscale SPI. The validity of prediction models was determined using root mean square error, mean absolute error, and coefficient of determination (R2). Kriging interpolation was used to demonstrate the predicted results of the four models. The results revealed that the forecast accuracy of the four models increases with the increase of SPI timescales, and the highest accuracy is obtained at SPI24. CEEMD decomposition can effectively stabilize the time series. Drought prediction based on the CEEMD provides a stable premise for the single model. At each timescale, combined models obtain higher prediction accuracy than single models, which indicates that combined models are more suitable for drought prediction. The forecast accuracy of the four models in order from the lowest to highest accuracy is the LSTM model, followed by the ARIMA, CEEMD-LSTM, and CEEMD-ARIMA models (the maximum R2 values are 0.8882, 0.9103, 0.9403, and 0.9846, respectively). The CEEMD-ARIMA model shows the best ability to forecast SPI values. This study explored the applicability of four drought prediction models and provided a basis for meteorological disaster prevention and mitigation efforts.
DING Yan,XU Dehe,CAO Lianhai,GUAN Xiangrong . Applicability of the LSTM and ARIMA model in drought prediction based on CEEMD: A case study of Xinjiang[J]. Arid Zone Research, 2022 , 39(3) : 734 -744 . DOI: 10.13866/j.azr.2022.03.07
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