| 利用光学遥感数据、GIS及人工神经网络模型分析区域滑坡灾害(英文) |
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| 作者姓名： | Biswajeet Pradhan Saro Lee |
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| 作者单位： | [1]Cilix Corporation, Lot L4-I-6, Level 4, Enterprise 4, Technology Park Malaysia, Bukit Jalil Highway, Bukit Jalil, 57000, Kuala Lumpur , Malaysia [2]Geoscience Information Center, Korea Institute of Geoscience and Mineral Resources （ KIGAM ） 30, Kajung-Dong , Yusung-Gu , Taejon, Korea |
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| 基金项目： | 韩国科学技术部基础研究项目 |
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| 摘 要： | 用光学遥感数据和地理信息系统(GIS)分析了马来西亚Selangor地区的滑坡灾害。通过遥感图像解译和野外调查,在研究区内确定出滑坡发生区。通过GIS和图像处理,建立了一个集地形、地质和遥感图像等多种信息的空间数据库。滑坡发生的因素主要为:地形坡度、地形方位、地形曲率及与排水设备距离;岩性及与线性构造距离;TM图像解译得到的植被覆盖情况;Landsat图像解译得到的植被指数;降水量。通过建立人工神经网络模型对这些因素进行分析后得到滑坡灾害图:由反向传播训练方法确定每个因素的权重值,然后用该权重值计算出滑坡灾害指数,最后用GIS工具生成滑坡灾害图。用遥感解译和野外观测确定出的滑坡位置资料验证了滑坡灾害图,准确率为82.92%。结果表明推测的滑坡灾害图与滑坡实际发生区域足够吻合。
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| 关 键 词： | 滑坡 灾害 人工神经网络 GIS 马来西亚 |
| 文章编号： | 1005-2321（2007）06-0143-10 |
| 收稿时间： | 2007-09-24 |
UTILIZATION OF OPTICAL REMOTE SENSING DATA AND GIS TOOLS FOR REGIONAL LANDSLIDE HAZARD ANALYSIS USING AN ARTIFICIAL NEURAL NETWORK MODEL |
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| Biswajeet Pradhan Saro Lee.UTILIZATION OF OPTICAL REMOTE SENSING DATA AND GIS TOOLS FOR REGIONAL LANDSLIDE HAZARD ANALYSIS USING AN ARTIFICIAL NEURAL NETWORK MODEL[J].Earth Science Frontiers,2007,14(6):143-152. |
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| Authors: | Biswajeet Pradhan Saro Lee |
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| Abstract: | The aim of this study is to evaluate landslide hazard analysis at Selangor area, Malaysia using optical remote sensing data and a Geographic Information System (GIS). Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys. Topographical, geological data and satellite images were collected, processed and constructed into a spatial database using GIS and image processing.
There are about 10 landslide occurrence factors that were selected as: topographic slope, topographic aspect, topographic curvature and distance from drainage; lithology and distance from lineament; land cover from TM satellite images; the vegetation index value from Landsat satellite images; precipitation data. These factors were analyzed using an advanced artificial neural network model to generate the landslide hazard map. Each factor's weight was determined by the back-propagation training method. Then the landslide hazard indices were calculated using the trained back-propagation weights, and finally the landslide hazard map was generated using GIS tools. Landslide locations were used to verify results of the landslide hazard map and the verification results showed 82. 92% accuracy. The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide areas. |
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| Keywords: | landslide hazard artificial neural network GIS Malaysia |
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