北京师范大学学报（自然科学版）2026，Vol.62Issue(1)：29-37,9.DOI:10.12202/j.0476-0301.2025179
基于多头长短期记忆网络的土壤水分预测模型研究
Research on soil moisture prediction model based on multihead LSTM:a case study in Tianjun county in the Qinghai Lake basin
摘要
Abstract
Soil moisture plays a significant role in regional water and energy cycles,and dynamic information is crucial for research on water resources management and agricultural production.Site 27 dataset with no missing values in Tianjun dense soil moisture and Freeze-Thaw monitoring network in the Qinghai Lake basin were applied to a multihead long short-term memory network(multihead LSTM).Three input soil parameters for the network were soil moisture,soil conductivity and soil temperature,with sliding coefficients of variation.Deep learning prediction models for soil moisture were established at soil depth of 5,10 and 30 cm to predict soil moisture after 1,7 and 30 days.Coefficient of determination(R2)of the models averaged 0.90 under varied soil depth,with root mean square error(RMSE)at 0.031 and mean absolute percentage error(MAPE)at 15.33％in average.This work therefore performed high-precision time series prediction of multi-layer soil moisture on varied time scales.关键词
土壤水分/传感器网络/深度学习/长短期记忆网络/时间序列预测Key words
soil moisture/sensor network/deep learning/long short-term memory network/time series prediction分类
天文与地球科学引用本文复制引用
赖俊能,徐同仁,汪鉴诚,刘绍民,柴琳娜,朱忠礼,徐自为..基于多头长短期记忆网络的土壤水分预测模型研究[J].北京师范大学学报（自然科学版）,2026,62(1):29-37,9.基金项目
国家自然科学基金资助项目(42571391) （42571391）
