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Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China
The Support Vector Machine (SVM) is an increasingly popular learning procedure based on statistical learning theory, and involves a training phase in which the model is trained by a training dataset of associated input and target output values. The trained model is then used to evaluate a separate s...
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Published in: | Geomorphology (Amsterdam, Netherlands) Netherlands), 2008-11, Vol.101 (4), p.572-582 |
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description | The Support Vector Machine (SVM) is an increasingly popular learning procedure based on statistical learning theory, and involves a training phase in which the model is trained by a training dataset of associated input and target output values. The trained model is then used to evaluate a separate set of testing data. There are two main ideas underlying the SVM for discriminant-type problems. The first is an optimum linear separating hyperplane that separates the data patterns. The second is the use of kernel functions to convert the original non-linear data patterns into the format that is linearly separable in a high-dimensional feature space. In this paper, an overview of the SVM, both one-class and two-class SVM methods, is first presented followed by its use in landslide susceptibility mapping. A study area was selected from the natural terrain of Hong Kong, and slope angle, slope aspect, elevation, profile curvature of slope, lithology, vegetation cover and topographic wetness index (TWI) were used as environmental parameters which influence the occurrence of landslides. One-class and two-class SVM models were trained and then used to map landslide susceptibility respectively. The resulting susceptibility maps obtained by the methods were compared to that obtained by the logistic regression (LR) method. It is concluded that two-class SVM possesses better prediction efficiency than logistic regression and one-class SVM. However, one-class SVM, which only requires failed cases, has an advantage over the other two methods as only “failed” case information is usually available in landslide susceptibility mapping. |
doi_str_mv | 10.1016/j.geomorph.2008.02.011 |
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The resulting susceptibility maps obtained by the methods were compared to that obtained by the logistic regression (LR) method. It is concluded that two-class SVM possesses better prediction efficiency than logistic regression and one-class SVM. However, one-class SVM, which only requires failed cases, has an advantage over the other two methods as only “failed” case information is usually available in landslide susceptibility mapping.</description><identifier>ISSN: 0169-555X</identifier><identifier>EISSN: 1872-695X</identifier><identifier>DOI: 10.1016/j.geomorph.2008.02.011</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Earth sciences ; Earth, ocean, space ; Engineering and environment geology. 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The resulting susceptibility maps obtained by the methods were compared to that obtained by the logistic regression (LR) method. It is concluded that two-class SVM possesses better prediction efficiency than logistic regression and one-class SVM. However, one-class SVM, which only requires failed cases, has an advantage over the other two methods as only “failed” case information is usually available in landslide susceptibility mapping.</description><subject>Earth sciences</subject><subject>Earth, ocean, space</subject><subject>Engineering and environment geology. Geothermics</subject><subject>Exact sciences and technology</subject><subject>Geomorphology, landform evolution</subject><subject>Hong Kong</subject><subject>Landslide susceptibility mapping</subject><subject>Logistic regression method</subject><subject>Marine and continental quaternary</subject><subject>Natural hazards: prediction, damages, etc</subject><subject>One-class sample</subject><subject>Support Vector Machine (SVM)</subject><subject>Surficial geology</subject><subject>Two-class sample</subject><issn>0169-555X</issn><issn>1872-695X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><recordid>eNqFkE1P3DAQhi3USmxp_0LlC5xIsJPYiTmBVrRU3aqHfoibNfEHeJWNXduptP--Xi3lymXm8rzvaB6EPlJSU0L51bZ-NH7nY3iqG0KGmjQ1ofQErejQNxUX7OENWhVQVIyxh1P0LqUtIaTrBVmhvIFZp8lpg9OSlAnZjW5yeY93EIKbH_EIyWjsZ_xjCcHHjH8blX3E30A9udlc41usCoJTXvT-wM2QlwgTTpMPJmFv8b0vPV_LuMTrkoH36K2FKZkPz_sM_fp093N9X22-f_6yvt1U0AqeKy1Aj7ztmBEAShPdNx3rRWfZKDgbbKs5a3mnBWutYNp2AONAxWg1WN2W5Bm6OPaG6P8sJmW5c-XHaYLZ-CXJhhImWNcXkB9BFX1K0VgZottB3EtK5EGy3Mr_kuVBsiSNLJJL8Pz5AiQFk40wK5de0g0ZKOFkKNzNkTPl3b_ORJmUM7My2sWiU2rvXjv1D6XdmEQ</recordid><startdate>20081101</startdate><enddate>20081101</enddate><creator>Yao, X.</creator><creator>Tham, L.G.</creator><creator>Dai, F.C.</creator><general>Elsevier B.V</general><general>Elsevier</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7UA</scope><scope>C1K</scope><scope>F1W</scope><scope>H96</scope><scope>L.G</scope></search><sort><creationdate>20081101</creationdate><title>Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China</title><author>Yao, X. ; Tham, L.G. ; Dai, F.C.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a396t-d9adb6345e9aacd0d7245794f5b9658f3d65364d953f95df4aab819bfdafd3b63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Earth sciences</topic><topic>Earth, ocean, space</topic><topic>Engineering and environment geology. Geothermics</topic><topic>Exact sciences and technology</topic><topic>Geomorphology, landform evolution</topic><topic>Hong Kong</topic><topic>Landslide susceptibility mapping</topic><topic>Logistic regression method</topic><topic>Marine and continental quaternary</topic><topic>Natural hazards: prediction, damages, etc</topic><topic>One-class sample</topic><topic>Support Vector Machine (SVM)</topic><topic>Surficial geology</topic><topic>Two-class sample</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yao, X.</creatorcontrib><creatorcontrib>Tham, L.G.</creatorcontrib><creatorcontrib>Dai, F.C.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Water Resources Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><jtitle>Geomorphology (Amsterdam, Netherlands)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yao, X.</au><au>Tham, L.G.</au><au>Dai, F.C.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China</atitle><jtitle>Geomorphology (Amsterdam, Netherlands)</jtitle><date>2008-11-01</date><risdate>2008</risdate><volume>101</volume><issue>4</issue><spage>572</spage><epage>582</epage><pages>572-582</pages><issn>0169-555X</issn><eissn>1872-695X</eissn><notes>ObjectType-Article-1</notes><notes>SourceType-Scholarly Journals-1</notes><notes>ObjectType-Feature-2</notes><notes>content type line 23</notes><abstract>The Support Vector Machine (SVM) is an increasingly popular learning procedure based on statistical learning theory, and involves a training phase in which the model is trained by a training dataset of associated input and target output values. 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The resulting susceptibility maps obtained by the methods were compared to that obtained by the logistic regression (LR) method. It is concluded that two-class SVM possesses better prediction efficiency than logistic regression and one-class SVM. However, one-class SVM, which only requires failed cases, has an advantage over the other two methods as only “failed” case information is usually available in landslide susceptibility mapping.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.geomorph.2008.02.011</doi><tpages>11</tpages></addata></record> |
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subjects | Earth sciences Earth, ocean, space Engineering and environment geology. Geothermics Exact sciences and technology Geomorphology, landform evolution Hong Kong Landslide susceptibility mapping Logistic regression method Marine and continental quaternary Natural hazards: prediction, damages, etc One-class sample Support Vector Machine (SVM) Surficial geology Two-class sample |
title | Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China |
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