A framework to determine geological hazards conditioning factors: A case study in Xishuangbanna
This study proposes a framework for determining geological hazard (e.g. landslides and floods) conditioning factors, by conducting landslide susceptibility assessment in Xishuangbanna as a case study. The framework aims to enhance urban planning and mitigate damage by providing a detailed area ranke...
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creator | Tantanee, Sarintip Long, Gen Nusit, Korakod |
description | This study proposes a framework for determining geological hazard (e.g. landslides and floods) conditioning factors, by conducting landslide susceptibility assessment in Xishuangbanna as a case study. The framework aims to enhance urban planning and mitigate damage by providing a detailed area ranked landslide susceptibility map. Fourteen most frequently adopted factors were initially selected as primary factors from literature for the landslide conditioning factor determination process. Subsequently, 27 scenarios were designed to test the influence of each factor on the model’s prediction power by evaluating the Area Under the Curve value of the produced maps. The study identified seven key factors that strongly influence landslides in Xishuangbanna. These factors, listed in order of increasing weighting values from low to high, include Distance to Fault, Elevation, Normalized Difference Vegetation Index, Distance to Road, Land Use and Land Cover, Lithology, and Slope Angle. These factors, along with the adopted frequency ratio model, achieved a successful rate and prediction rate of 85.8% and 84.0%, respectively. This indicates a reasonable landslide conditioning factor group and a reliable model for the study area. The proposed framework and case study fill a regional research gap and contribute to the broader field of geological hazards assessment and mitigation. |
doi_str_mv | 10.1063/5.0235988 |
format | Conference Proceeding |
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The framework aims to enhance urban planning and mitigate damage by providing a detailed area ranked landslide susceptibility map. Fourteen most frequently adopted factors were initially selected as primary factors from literature for the landslide conditioning factor determination process. Subsequently, 27 scenarios were designed to test the influence of each factor on the model’s prediction power by evaluating the Area Under the Curve value of the produced maps. The study identified seven key factors that strongly influence landslides in Xishuangbanna. These factors, listed in order of increasing weighting values from low to high, include Distance to Fault, Elevation, Normalized Difference Vegetation Index, Distance to Road, Land Use and Land Cover, Lithology, and Slope Angle. These factors, along with the adopted frequency ratio model, achieved a successful rate and prediction rate of 85.8% and 84.0%, respectively. 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This indicates a reasonable landslide conditioning factor group and a reliable model for the study area. The proposed framework and case study fill a regional research gap and contribute to the broader field of geological hazards assessment and mitigation.</description><subject>Case studies</subject><subject>Conditioning</subject><subject>Damage assessment</subject><subject>Flood predictions</subject><subject>Geological hazards</subject><subject>Geological mapping</subject><subject>Geology</subject><subject>Hazard assessment</subject><subject>Hazard mitigation</subject><subject>Land cover</subject><subject>Land use</subject><subject>Landslides</subject><subject>Landslides & mudslides</subject><subject>Lithology</subject><subject>Normalized difference vegetative index</subject><subject>Urban planning</subject><issn>0094-243X</issn><issn>1551-7616</issn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2024</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNotkE1LAzEURYMoWKsL_0HAnTA1mTdJZ9yV4hcU3HTRXci8JNPUNqnJDFJ_vZV2dTeHe7mHkHvOJpxJeBITVoJo6vqCjLgQvJhKLi_JiLGmKsoKVtfkJucNY2UzndYjombUJb2zPzF90T5SY3ubdj5Y2tm4jZ1HvaVr_auTyRRjML73MfjQUaexjyk_0xlFnS3N_WAO1Ae68nk96NC1OgR9S66c3mZ7d84xWb6-LOfvxeLz7WM-WxR7CXXRalmBAI7Gysa1HISTVugWuXSytaZG0Ai2Mg4FooESHUpe2Sk04BARxuThVLtP8XuwuVebOKRwXFTAWV0xyQUcqccTldH3-v-I2ie_0-mgOFP__pRQZ3_wB3yfZF4</recordid><startdate>20240923</startdate><enddate>20240923</enddate><creator>Tantanee, Sarintip</creator><creator>Long, Gen</creator><creator>Nusit, Korakod</creator><general>American Institute of Physics</general><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope></search><sort><creationdate>20240923</creationdate><title>A framework to determine geological hazards conditioning factors: A case study in Xishuangbanna</title><author>Tantanee, Sarintip ; Long, Gen ; Nusit, Korakod</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p638-ba643531cde69fb135f6e5abc16f6bed8c3ac3e4dfc5ccd32cfc614e7393fccc3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Case studies</topic><topic>Conditioning</topic><topic>Damage assessment</topic><topic>Flood predictions</topic><topic>Geological hazards</topic><topic>Geological mapping</topic><topic>Geology</topic><topic>Hazard assessment</topic><topic>Hazard mitigation</topic><topic>Land cover</topic><topic>Land use</topic><topic>Landslides</topic><topic>Landslides & mudslides</topic><topic>Lithology</topic><topic>Normalized difference vegetative index</topic><topic>Urban planning</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tantanee, Sarintip</creatorcontrib><creatorcontrib>Long, Gen</creatorcontrib><creatorcontrib>Nusit, Korakod</creatorcontrib><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tantanee, Sarintip</au><au>Long, Gen</au><au>Nusit, Korakod</au><au>Suparta, Wayan</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A framework to determine geological hazards conditioning factors: A case study in Xishuangbanna</atitle><btitle>AIP conference proceedings</btitle><date>2024-09-23</date><risdate>2024</risdate><volume>3239</volume><issue>1</issue><issn>0094-243X</issn><eissn>1551-7616</eissn><coden>APCPCS</coden><abstract>This study proposes a framework for determining geological hazard (e.g. landslides and floods) conditioning factors, by conducting landslide susceptibility assessment in Xishuangbanna as a case study. The framework aims to enhance urban planning and mitigate damage by providing a detailed area ranked landslide susceptibility map. Fourteen most frequently adopted factors were initially selected as primary factors from literature for the landslide conditioning factor determination process. Subsequently, 27 scenarios were designed to test the influence of each factor on the model’s prediction power by evaluating the Area Under the Curve value of the produced maps. The study identified seven key factors that strongly influence landslides in Xishuangbanna. These factors, listed in order of increasing weighting values from low to high, include Distance to Fault, Elevation, Normalized Difference Vegetation Index, Distance to Road, Land Use and Land Cover, Lithology, and Slope Angle. These factors, along with the adopted frequency ratio model, achieved a successful rate and prediction rate of 85.8% and 84.0%, respectively. This indicates a reasonable landslide conditioning factor group and a reliable model for the study area. The proposed framework and case study fill a regional research gap and contribute to the broader field of geological hazards assessment and mitigation.</abstract><cop>Melville</cop><pub>American Institute of Physics</pub><doi>10.1063/5.0235988</doi><tpages>12</tpages></addata></record> |
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subjects | Case studies Conditioning Damage assessment Flood predictions Geological hazards Geological mapping Geology Hazard assessment Hazard mitigation Land cover Land use Landslides Landslides & mudslides Lithology Normalized difference vegetative index Urban planning |
title | A framework to determine geological hazards conditioning factors: A case study in Xishuangbanna |
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