Incorporating the ICEEMDAN decomposition to improve the accuracy of models for drought prediction

WEI Yuxin; LI Qiao; LU Chunlei; TAO Hongfei; AIHEMAITI Mahemujiang; JIANG Youwei

doi:10.13522/j.cnki.ggps.2024262

Abstract：[Objective]Drought is one of the most common abiotic stresses affecting crop production worldwide.Accurate forecasting is essential for improving irrigation management and water use efficiency.This study evaluates a multi-dimensional time series model for drought prediction based on the ICEEMDAN decomposition,aiming to provide a new method for improving drought prediction accuracy.[Method]The Santun River Irrigation District in Xinjiang was chosen as a case study.Monthly precipitation data from the Nianpanzhuang Station(1980-2023)were used,and the standardized precipitation index(SPI)was calculated for time intervals of 1,3,6,9,12,and 24 months.Six prediction models were compared:the autoregressive integrated moving average(ARIMA)model,gated recurrent unit(GRU)network,long short-term memory(LSTM)network,and their combinations with the improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN),resulting in ICEEMDAN-ARIMA,ICEEMDAN-GRU,and ICEEMDAN-LSTM models.These models were used to predict the SPI series at multiple time scales.Model accuracy was evaluated using root mean square error(RMSE),mean absolute error(MAE),and the coefficient of determination(R2).[Result]The accuracy of all six models improved as the time interval increased,reaching the highest at the 24-month interval.ICEEMDAN effectively stabilized the time series data and improved model accuracy for drought prediction.The accuracy of the models was ranked as follows:ICEEMDAN-ARIMA＞ICEEMDAN-GRU＞ICEEMDAN-LSTM＞ARIMA＞GRU＞LSTM.[Conclusion]Incorporating ICEEMDAN enhances the accuracy of drought prediction.Among the six models compared,ICEEMDAN-ARIMA was the most accurate and can be used for drought prediction in the studied region.