基于IWOA-BP的草原土壤湿度预测研究  

Research on the Prediction of Grassland Soil Moisture Based on IWOA-BP

作　　者：张鹤 He Zhang(School of Mechanical Engineering,University of Shanghai for Science and Technology,Shanghai)

机构地区：[1]上海理工大学机械工程学院,上海

出　　处：《建模与仿真》2025年第3期326-336,共11页Modeling and Simulation

摘　　要：本研究针对传统BP神经网络模型表达能力和预测精度较低的问题,提出了一种基于鲸鱼优化算法(WOA)的改进神经网络预测模型。通过构建时间序列预测模型,对降雨量、NDVI和LAI等缺失数据进行修补,显著提升了数据完整性和模型预测能力。优化后的模型被用于预测2022年和2023年不同深度土壤湿度数据,为草原状态监测提供了精确的基础数据支持,具有重要的应用价值。In this study,an improved neural network prediction model based on Whale Optimization Algo-rithm(WOA)is proposed to address the problem of low expressive ability and prediction accuracy of traditional BP neural network model.By constructing a time series prediction model and patch-ing the missing data such as rainfall,NDVI and LAI,the data completeness and model prediction ability were significantly improved.The optimized model was used to predict soil moisture data at different depths in 2022 and 2023,which provides accurate basic data support for grassland condi-tion monitoring and has important application value.

关 键 词：BP神经网络 时间序列预测 鲸鱼算法 土壤湿度 

分 类 号：S812.2[农业科学—草业科学]