| 基于异质SVM神经网络的土壤盐渍化灾害预测模型 |
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| 引用本文： | 武丹,贾科利,张晓东,张俊华.基于异质SVM神经网络的土壤盐渍化灾害预测模型[J].水文地质工程地质,2018,0(5):143-143. |
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| 作者姓名： | 武丹 贾科利 张晓东 张俊华 |
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| 作者单位： | 1.宁夏大学资源环境学院，宁夏 银川750021；2.宁夏回族自治区遥感测绘勘查院（宁夏回族自治区遥感中心），宁夏 银川750021；3.中国地质大学（北京）信息工程学院，北京100083；4.宁夏回族自治区地质调查院，宁夏 银川750021；5.宁夏大学环境工程研究院，宁夏 银川750021 |
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| 基金项目： | 国家自然科学基金项目资助（41561078）；宁夏自然科学基金（2018AAC03007）；宁夏回族自治区科技惠民项目（2016KJHM130） |
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| 摘 要： | 为研究银川平原普遍存在的土壤盐渍化问题，文章对银川平原的土壤盐渍化程度及潜在的发展趋势作出预测。利用Landsat 8 OLI数据与野外实测数据，选取地面高程、地下水位埋深、地下水溶解性总固体、植被指数、盐分指数及干旱指数为预测指标并提取指标值建立数据集，结合野外实测样点数据，建立基于异质支持向量机（Support Vector Machine，SVM)神经网络算法的盐渍化灾害预测模型。结果表明:（1）建立预测模型时，选择Radial Basis Funciton作为模型的核函数，c=100且g=3时预测精度最高可达85%；（2）研究区轻度盐渍化土壤面积约854 km2，中度盐渍化土壤面积约985 km2，重度盐渍化土壤面积约231 km2，主要分布在平罗县西大滩、银川芦花和吴忠苦水河地区；（3）银川平原北部的土壤盐渍化情况较严重且多分布于耕地周围的撂荒地以及地下水位埋藏较浅的地区，耕地资源中土壤盐渍化状况较严重，应注重耕地的合理灌溉与排水，增加土壤的可持续利用性。
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| 关 键 词： | 土壤盐渍化 SVM 神经网络 银川平原 |
| 收稿时间： | 2018-01-24 |
| 修稿时间： | 2018-04-26 |
Soil salinization disaster prediction model based on heterogeneous SVM neural network |
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| WU Dan,JIA Keli,ZHANG Xiaodong,ZHANG Junhua.Soil salinization disaster prediction model based on heterogeneous SVM neural network[J].Hydrogeology and Engineering Geology,2018,0(5):143-143. |
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| Authors: | WU Dan JIA Keli ZHANG Xiaodong ZHANG Junhua |
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| Affiliation: | 1.College of Resources and Environmental Science, Ningxia University, Yinchuan, Ningxia750021, China; 2.Ningxia Institute of Remote Sensing Survey & Mapping(Ningxia Remote Sensing Center), Yinchuan, Ningxia750021, China; 3.School of Information Engineering, China University of Geosciences, Beijing100083, China;4.Ningxia Geological Survey Institute, Yinchuan, Ningxia750021, China；5.Institute of Enviromental Engineering, Ningxia University, Ningxia, Yinchuan750021, China |
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| Abstract: | In the paper，we analyzes the soil salinization in Yinchuan plain of Ningxia, predicts the degree of soil salinization and the potential development trend of soil salinization. Based on the Landsat 8 OLI data and field measurements, we select ground elevation, groundwater burial depth, TDS, vegetation index, salinity index and drought index as indicator and use these indicators and field test sample data to set up the database and establish a disaster model to predict soil salinization which is based on heterogeneous SVM neural network algorithm. The results show that (1) selecting the Radial Basis Function as the kernel function of the early warning model, when c=100 and g=3, can make the accuracy up to 85%. (2) The soil area of mildly salinized soil is about 854.08 km2, the area of moderate salinized soil is about 985.52 km2, and the area of severe salinized soil is about 231.97 km2.They mainly occur in the Xidatan town of Pingluo county, the Luhua area near Yinchuan and the Kushuihe district of Wuzhong.(3) The soil salinization in the northern part of the Yinchuan plain is more serious, and it widely exists in the abandoned areas around the cultivated land and in the shallow areas where groundwater occurs. The soil salinization is serious in the cultivated land of the Yinchuan plain, and attention should be paid to proper irrigation and drainage to increase the sustainable utilization of soil. |
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