基于T-S模糊神经网络模型的榆林市土壤风蚀危险度评价  

Soil Wind Erosion Risk Assessment in Yulin City Using T-S Fuzzy Neural Network Model

作　　者：刘新颜[1] 曹晓仪[1] 董治宝[1,2] 

机构地区：[1]陕西师范大学旅游与环境学院,陕西西安710062 [2]中国科学院寒区旱区环境与工程研究所,甘肃兰州730000

出　　处：《地理科学》2013年第6期741-747,共7页Scientia Geographica Sinica

基　　金：教育部长江学者特聘教授项目(801813);国家自然科学基金项目(41171090)资助

摘　　要：选择位于风沙过渡区的榆林市为研究区域,以GIS技术和T-S模糊神经网络为依托,从土壤风蚀影响因子及风蚀动力学机制出发构建区域土壤风蚀危险度模型。基于此模型,对榆林市土壤风蚀危险度空间分异特征进行了分析,结果表明:T-S模糊神经网络模型可有效地揭示出区域土壤风蚀危险度与环境之间的映射关系,为土壤风蚀预测提供依据;风力、植被、气温、降水、地形等环境要素控制着土壤风蚀危险度空间分异格局;榆林市土壤风蚀危险度空间分异格局表现为:危险度从西北向东南逐渐降低。Soli wind erosion,one of the major causes of environmental problems such as farmland degradation and desertification,producing large scale of sandstorm,polluting the environment and harming human beings′ health in arid and semi-arid regions,is the detachment,entrainment,transport deposition of soil particles caused by wind.It is also a serious socio-economic issue which limits the development in those ecologically fragile areas.In the previous studies,methods for accessing wind erosion are based on land use information.In this article,however,another approach that uses GIS and T-S fuzzy neural network model to access soil wind erosion risk is presented.The approach is applied to a case study of the Yulin City located on the wind-sand transition regions of Mu Us sandy land,Inner Mongolian plateau and loess plateau,northwest China,where most serious expansion of desertification is proceeding.Environmental elements of soil wind erosion were selected and their spatial distribution data were extracted using GIS technology and T-S fuzzy neural network model was constructed after training the sample data of different wind erosion risk level.The model was applied to mapping wind erosion risk after inputting the extracted date.The entire jobs were based on the software of ArcGIS9.3 and MATLAB.The results show that： Firstly,the comparison of the risk assessment with land use information in Yulin City demonstrates that the result is in conformity with actual conditions.T-S fuzzy neural network model can reveal the relationship between risk of soil wind erosion and its environment effectively,and provide the basis for predicting wind erosion.Secondly,environmental elements such as wind energy,vegetation coverage,temperature,precipitation,topography control the distribution properties of wind erosion risk in Yulin City.Thirdly,there are 4 divisions of wind erosion risk in Yulin City： the severe risk level,the intense risk level,the moderate risk level and the slight risk level,and the area of different risk level are 4

关 键 词：榆林市 土壤风蚀 T-S模糊神经网络 危险度评价 

分 类 号：S157[农业科学—土壤学]