基于LSTM的草原放牧对土壤影响研究  

Research on Grassland Grazing Strategy Based on LSTM

作　　者：于洋[1] Yang Yu(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》2024年第3期3139-3146,共8页Modeling and Simulation

摘　　要：中国的草原面积占全球6%~8%,主要分为温带草原、高寒草原和荒漠草原等类型,其中内蒙古锡林郭勒草原是典型的温带草原,也是国家重要的畜牧业基地和生态屏障。研究放牧策略对草原土壤的影响具有重要意义。使用层次分析法分析了放牧方式对放牧强度的影响。此外,利用LSTM神经网络预测了放牧强度对土壤化学性质的影响,发现放牧强度增大会使土壤有机碳等指标减少。China’s grassland area accounts for 6%to 8%of the global total,primarily divided into temperate grasslands,alpine grasslands,and desert grasslands,among other types.Among them,the Xilingol Grassland in Inner Mongolia is a typical temperate grassland and also serves as an important national base for animal husbandry and an ecological barrier.Researching the impact of grazing strategies on grassland soil is of significant importance.The Analytic Hierarchy Process was used to analyze the impact of grazing methods on grazing intensity.Furthermore,the impact of grazing intensity on soil chemical properties was predicted using an LSTM neural network,revealing that an increase in grazing intensity leads to a decrease in soil organic carbon and other indicators.

关 键 词：放牧方式 放牧强度 土壤化学性质 层次分析法 LSTM算法 

分 类 号：S81[农业科学—畜牧学]