基于EMD-GWO-LSTM模型的新疆标准化降水蒸散指数预测方法研究
收稿日期: 2023-07-02
修回日期: 2024-02-08
网络出版日期: 2024-04-26
基金资助
自治区人文社会科学重点研究基地课题(JEDU2023J003);新疆财经大学校级科研基金项目(XJCD20230004);新疆财经大学校级科研基金项目(XJCD20230003)
Prediction of the standardized precipitation evapotranspiration index in the Xinjiang region using the EMD-GWO-LSTM model
Received date: 2023-07-02
Revised date: 2024-02-08
Online published: 2024-04-26
干旱预测一直是干旱研究领域的重大挑战，提高干旱预测的准确性是解决干旱问题的关键。基于1961—2019年新疆34个气象站点月降水和月平均气温数据，计算得到标准化降水蒸散指数（Standardized Precipitation Evapotranspiration Index，SPEI），对新疆气象干湿变化进行分析，提出一种经验模态分解方法（empirical mode decomposition, EMD）-灰狼优化算法（grey wolf optimizer，GWO）-长短期神经网络（long short-term memory network，LSTM）的数据分解型干旱组合预测模型进行预测，并进行模型性能评价。结果表明：（1） 干旱周期性变化整体呈现平稳且周期长的特点；（2） EMD能够有效优化数据的平稳性，GWO优化预测模型参数，组合模型的预测精度相较于单一预测模型有明显提高；（3） 4个预测模型结果精度由高到低的排序为：EMD-GWO-LSTM、GWO-LSTM、GWO-支持向量回归（Support Vactor Regression，SVR）、LSTM，拟合优度分别为0.972、0.939、0.862、0.830，EMD-GWO-LSTM组合预测模型的预测精度优于其余3个预测模型。EMD-GWO-LSTM组合模型可有效提高气象干旱的预测精度，为新疆地区气象干旱预报及抗旱减灾工作提供了新的方法手段。
关键词： EMD-GWO-LSTM模型; 标准化降水蒸散指数; 干旱预测; 新疆
许超杰 , 窦燕 , 孟琪琳 . 基于EMD-GWO-LSTM模型的新疆标准化降水蒸散指数预测方法研究[J]. 干旱区研究, 2024 , 41(4) : 527 -539 . DOI: 10.13866/j.azr.2024.04.01
Drought prediction has always been a major challenge in the field of drought research. Improving the accuracy of drought prediction is the key to solving the drought problem. The standardized precipitation evapotranspiration index (SPEI) was calculated on the basis of the monthly precipitation and average temperature data from 34 meteorological stations in Xinjiang from 1961 to 2019. Dry and wet changes in the Xinjiang region were analyzed. An empirical mode decomposition (EMD)-Gray Wolf Optimizer (GWO)-long short-term memory network is proposed. A combination prediction model based on the data decomposition of LSTM was used to forecast the drought, and the performance of the model was evaluated. The results were as follows: (1) the drought periodicity was stable and the periodicity was long. (2) EMD can effectively optimize the stationarity of data, GWO can optimize the parameters of the prediction model, and the prediction accuracy of the combination model is significantly higher than that of the single prediction model. (3) The accuracy of the results of the four prediction models in descending order was as follows: EMD-GWO-LSTM, GWO-LSTM, GWO-support vector regression (SVR), and LSTM (goodness of fit: 0.972, 0.939, 0.862, 0.830, respectively). The prediction accuracy of the EMD-GWO-LSTM combination prediction model was higher than that of the other three prediction models. The EMD-GWO-LSTM combination prediction model can effectively improve the accuracy of meteorological drought prediction and provide a novel approach for meteorological drought forecasting and drought mitigation in Xinjiang.
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