水资源与水工程学报2025，Vol.36Issue(2)：207-217,11.DOI:10.11705/j.issn.1672-643X.2025.02.24
基于多头LSTM模型的南疆枣树土壤墒情预测
Soil moisture prediction of jujube trees in southern Xinjiang based on multihead LSTM model
摘要
Abstract
Accurate prediction of soil moisture is crucial for optimizing crop planting quality and irrigation schemes of jujube trees(Ziziphus jujuba Mill.).This study established a high-precision soil moisture prediction model to improve the irrigation management of jujube trees in southern Xinjiang.Based on hourly datasets of soil moisture content,meteorological data,and irrigation volume for jujube trees during the entire growing seasons of 2021 and 2022 at soil depths of 20,40,60,and 80 cm,a long short-term memory(LSTM)neural network model was used to perform multi-step predictions of soil moisture for each soil layer.To expand the model's prediction range and improve prediction accuracy,a multihead LSTM(M-LSTM)model consisting of four individual LSTM models was introduced.k-fold cross-valida-tion combined with the sparrow search algorithm(SSA)was used for hyperparameter tuning of each indi-vidual model to ensure the model's generalization ability and accuracy.Finally,the final prediction re-sult was obtained by performing a weighted average of the outputs from each individual model.The results show that the M-LSTM model improved the coefficient of determination(R2)of the soil moisture at 1,12,24,and 48 h to 0.951,0.932,0.870,and 0.815,respectively,according to the dataset of soil moisture content averages from four soil layers.The M-LSTM model effectively enhanced the medium-and long-term prediction accuracy of soil moisture for jujube trees,with particularly significant improve-ments in predictions at 24 and 48 h.These findings can provide a strong support for the precise irrigation management of jujube trees,thus improving water use efficiency and avoiding unnecessary water waste.关键词
土壤墒情预测/多头LSTM/麻雀搜索算法/k折交叉验证/南疆滴灌骏枣Key words
prediction of soil moisture/multihead long short-term memory(M-LSTM)/sparrow search algorithm(SSA)/k-fold cross-validation/southern Xinjiang drip irrigation jujube分类
农业科技引用本文复制引用
杨轶航,吕德生,刘宁宁,王振华,李淼,张金珠,王东旺..基于多头LSTM模型的南疆枣树土壤墒情预测[J].水资源与水工程学报,2025,36(2):207-217,11.基金项目
国家重点研发计划项目(2022YFD1900405) （2022YFD1900405）
兵团科技成果转化引导计划项目(2023BA003) （2023BA003）
国家"十四五"重点研发计划项目(2021YFD1900802-2) （2021YFD1900802-2）
兵团农业GG项目(2023AA305) （2023AA305）
