ARIMA-LSTM组合模型在基于SPI干旱预测中的应用——以青海省为例

doi:10.12118/j.issn.1000-6060.2020.04.15

Abstract

Abstract: Drought prediction is a precondition to effectively mitigate the risk of drought. Daily precipitation data obtained from 38 meteorological stations in Qinghai Province,China in the period from 1958 to 2017 were used to calculate the multiscale standardized precipitation index （SPI）. In addition,based on these data,the SPI sequence Autoregressive Moving Average model （ARIMA）,Long Short-Term Memory model （LSTM）,and ARIMA-LSTM combination model were constructed. After the calibration and verification of the model parameters,the model was used to predict multiscale SPI values using the Xining area as a case study. Moreover,the validity of all the prediction models was determined by root mean square error （RMSE） and mean absolute percentage error （MAPE）. The results indicated that the RMSE values of the ARIMA-LSTM combined model in SPI1 and SPI12 were 0.159 7 and 0.181 0,respectively,which were lower than those （1.265 4 and 0.293 3） of the ARIMA model. This indicates that the prediction accuracy of the ARIMA and LSTM models for SPI was related to the timescale. Comparing the measured data （using GIS） to the data predicted by the models,the combined ARIMA-LSTM model exhibited a higher prediction accuracy compared to the single ARIMA model. In addition,the combined ARIMA-LSTM model showed an ability to fit the SPI values of different timescales.

Key words: drought forecast, SPI, ARIMA-LSTM, Qinghai Province

ZHANG Jian-hai, ZHANG Qi, XU De-he, DING Yan. Application of a combined ARIMA-LSTM model based on SPI for the forecast of drought：A case study in Qinghai Province[J].Arid Land Geography, 2020, 43(4): 1004-1013.

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Application of a combined ARIMA-LSTM model based on SPI for the forecast of drought：A case study in Qinghai Province

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