Comparison of Logistic Regression, Information Value, and Comprehensive Evaluating Model for Landslide Susceptibility Mapping
This study validated the robust performances of the recently proposed comprehensive landslide susceptibility index model (CLSI) for landslide susceptibility mapping (LSM) by comparing it to the logistic regression (LR) and the analytical hierarchy process information value (AHPIV) model. Zhushan Cou...
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| Veröffentlicht in: | Sustainability 2021-04, Vol.13 (7), p.3803 |
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| creator | Tang, Rui-Xuan Yan, E-Chuan Wen, Tao Yin, Xiao-Meng Tang, Wei |
| description | This study validated the robust performances of the recently proposed comprehensive landslide susceptibility index model (CLSI) for landslide susceptibility mapping (LSM) by comparing it to the logistic regression (LR) and the analytical hierarchy process information value (AHPIV) model. Zhushan County in China, with 373 landslides identified, was used as the study area. Eight conditioning factors (lithology, slope structure, slope angle, altitude, distance to river, stream power index, slope length, distance to road) were acquired from digital elevation models (DEMs), field survey, remote sensing imagery, and government documentary data. Results indicate that the CLSI model has the highest accuracy and the best classification ability, although all three models can produce reasonable landslide susceptibility (LS) maps. The robust performance of the CLSI model is due to its weight determination by a back-propagation neural network (BPNN), which successfully captures the nonlinear relationship between landslide occurrence and the conditioning factors. |
| doi_str_mv | 10.3390/su13073803 |
| format | Article |
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Zhushan County in China, with 373 landslides identified, was used as the study area. Eight conditioning factors (lithology, slope structure, slope angle, altitude, distance to river, stream power index, slope length, distance to road) were acquired from digital elevation models (DEMs), field survey, remote sensing imagery, and government documentary data. Results indicate that the CLSI model has the highest accuracy and the best classification ability, although all three models can produce reasonable landslide susceptibility (LS) maps. The robust performance of the CLSI model is due to its weight determination by a back-propagation neural network (BPNN), which successfully captures the nonlinear relationship between landslide occurrence and the conditioning factors.</description><identifier>ISSN: 2071-1050</identifier><identifier>EISSN: 2071-1050</identifier><identifier>DOI: 10.3390/su13073803</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Accuracy ; Back propagation ; Back propagation networks ; Conditioning ; Decision making ; Digital Elevation Models ; Digital imaging ; Geographic information systems ; Information processing ; Landslides ; Landslides & mudslides ; Lithology ; Machine learning ; Mapping ; Neural networks ; Propagation ; Remote sensing ; Statistical analysis ; Statistical methods ; Support vector machines ; Susceptibility ; Sustainability</subject><ispartof>Sustainability, 2021-04, Vol.13 (7), p.3803</ispartof><rights>2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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Zhushan County in China, with 373 landslides identified, was used as the study area. Eight conditioning factors (lithology, slope structure, slope angle, altitude, distance to river, stream power index, slope length, distance to road) were acquired from digital elevation models (DEMs), field survey, remote sensing imagery, and government documentary data. Results indicate that the CLSI model has the highest accuracy and the best classification ability, although all three models can produce reasonable landslide susceptibility (LS) maps. The robust performance of the CLSI model is due to its weight determination by a back-propagation neural network (BPNN), which successfully captures the nonlinear relationship between landslide occurrence and the conditioning factors.</description><subject>Accuracy</subject><subject>Back propagation</subject><subject>Back propagation networks</subject><subject>Conditioning</subject><subject>Decision making</subject><subject>Digital Elevation Models</subject><subject>Digital imaging</subject><subject>Geographic information systems</subject><subject>Information processing</subject><subject>Landslides</subject><subject>Landslides & mudslides</subject><subject>Lithology</subject><subject>Machine learning</subject><subject>Mapping</subject><subject>Neural networks</subject><subject>Propagation</subject><subject>Remote sensing</subject><subject>Statistical analysis</subject><subject>Statistical methods</subject><subject>Support vector machines</subject><subject>Susceptibility</subject><subject>Sustainability</subject><issn>2071-1050</issn><issn>2071-1050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpNkE1LAzEQhoMoWGov_oKAN-lqJtnPo5SqhS2CX9clm53UlO1mTXYLPfjfTVHQubwzzPPOwEvIJbAbIQp260cQLBM5EydkwlkGEbCEnf7rz8nM-y0LJQQUkE7I18LueumMtx21mpZ2Y_xgFH3GjUPvje3mdNVp63ZyCAN9l-2Icyq7hh6dDj-w82aPdLkPm8B0G7q2DbY0eGgZON-aBunL6BX2g6lNa4YDXcu-D-gFOdOy9Tj71Sl5u1--Lh6j8ulhtbgrI8WLZIhinteJiJsYmKpBZQzytNCMqaAi50onEqTWSVarGIGnqskAMqmQxTkgSDElVz93e2c_R_RDtbWj68LLiicphyLOYh6o6x9KOeu9Q131zuykO1TAqmPC1V_C4hvKSm8Y</recordid><startdate>20210401</startdate><enddate>20210401</enddate><creator>Tang, Rui-Xuan</creator><creator>Yan, E-Chuan</creator><creator>Wen, Tao</creator><creator>Yin, Xiao-Meng</creator><creator>Tang, Wei</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>4U-</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope></search><sort><creationdate>20210401</creationdate><title>Comparison of Logistic Regression, Information Value, and Comprehensive Evaluating Model for Landslide Susceptibility Mapping</title><author>Tang, Rui-Xuan ; 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Zhushan County in China, with 373 landslides identified, was used as the study area. Eight conditioning factors (lithology, slope structure, slope angle, altitude, distance to river, stream power index, slope length, distance to road) were acquired from digital elevation models (DEMs), field survey, remote sensing imagery, and government documentary data. Results indicate that the CLSI model has the highest accuracy and the best classification ability, although all three models can produce reasonable landslide susceptibility (LS) maps. The robust performance of the CLSI model is due to its weight determination by a back-propagation neural network (BPNN), which successfully captures the nonlinear relationship between landslide occurrence and the conditioning factors.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/su13073803</doi><oa>free_for_read</oa></addata></record> |
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| source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; MDPI - Multidisciplinary Digital Publishing Institute |
| subjects | Accuracy Back propagation Back propagation networks Conditioning Decision making Digital Elevation Models Digital imaging Geographic information systems Information processing Landslides Landslides & mudslides Lithology Machine learning Mapping Neural networks Propagation Remote sensing Statistical analysis Statistical methods Support vector machines Susceptibility Sustainability |
| title | Comparison of Logistic Regression, Information Value, and Comprehensive Evaluating Model for Landslide Susceptibility Mapping |
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