灌溉排水学报2025，Vol.44Issue(3)：94-103,10.DOI:10.13522/j.cnki.ggps.2024262
基于ICEEMDAN分解的多维时间序列干旱预测模型性能评估
Incorporating the ICEEMDAN decomposition to improve the accuracy of models for drought prediction
摘要
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.关键词
ICEEMDAN/长短期记忆网络/差分自回归移动平均模型/门控循环单元网络/标准化降水指数Key words
ICEEMDAN/LSTM/ARIMA/GRU/SPI分类
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韦余鑫,李巧,卢春雷,陶洪飞,马合木江·艾合买提,姜有为..基于ICEEMDAN分解的多维时间序列干旱预测模型性能评估[J].灌溉排水学报,2025,44(3):94-103,10.基金项目
新疆维吾尔自治区重大专项(2023A02002-1) （2023A02002-1）
国家自然科学基金项目(41762018) （41762018）
"新疆水利工程安全与水灾害防治重点实验室"开放课题(ZDSYS-JS-2021-09) （ZDSYS-JS-2021-09）
水灾害防御全国重点实验室"一带一路"水与可持续发展科技基金面上项目(2020491611) （2020491611）
