Landslide Geo-Hazard Risk Mapping Using Logistic Regression Modeling in Guixi, Jiangxi, China
Reliable prediction of landslide occurrence is important for hazard risk reduction and prevention. Taking Guixi in northeast Jiangxi as an example, this research aimed to conduct such a landslide risk assessment using a multiple logistic regression (MLR) algorithm. Field-investigated landslides and...
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| Veröffentlicht in: | Sustainability 2021-05, Vol.13 (9), p.4830, Article 4830 |
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| creator | Huangfu, Wenchao Wu, Weicheng Zhou, Xiaoting Lin, Ziyu Zhang, Guiliang Chen, Renxiang Song, Yong Lang, Tao Qin, Yaozu Ou, Penghui Zhang, Yang Xie, Lifeng Huang, Xiaolan Fu, Xiao Li, Jie Jiang, Jingheng Zhang, Ming Liu, Yixuan Peng, Shanling Shao, Chongjian Bai, Yonghui Zhang, Xiaofeng Liu, Xiangtong Liu, Wenheng |
| description | Reliable prediction of landslide occurrence is important for hazard risk reduction and prevention. Taking Guixi in northeast Jiangxi as an example, this research aimed to conduct such a landslide risk assessment using a multiple logistic regression (MLR) algorithm. Field-investigated landslides and non-landslide sites were converted into polygons. We randomly generated 50,000 sampling points to intersect these polygons and the intersected points were divided into two parts, a training set (TS) and a validation set (VT) in a ratio of 7 to 3. Thirteen geo-environmental factors, including elevation, slope, and distance from roads were employed as hazard-causative factors, which were intersected by the TS to create the random point (RP)-based dataset. The next step was to compute the certainty factor (CF) of each factor to constitute a CF-based dataset. MLR was applied to the two datasets for landslide risk modeling. The probability of landslides was then calculated in each pixel, and risk maps were produced. The overall accuracy of these two models versus VS was 91.5% and 90.4% with a Kappa coefficient of 0.814 and 0.782, respectively. The RP-based MLR modeling achieved more reliable predictions and its risk map seems more plausible for providing technical support for implementing disaster prevention measures in Guixi. |
| doi_str_mv | 10.3390/su13094830 |
| format | Article |
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Taking Guixi in northeast Jiangxi as an example, this research aimed to conduct such a landslide risk assessment using a multiple logistic regression (MLR) algorithm. Field-investigated landslides and non-landslide sites were converted into polygons. We randomly generated 50,000 sampling points to intersect these polygons and the intersected points were divided into two parts, a training set (TS) and a validation set (VT) in a ratio of 7 to 3. Thirteen geo-environmental factors, including elevation, slope, and distance from roads were employed as hazard-causative factors, which were intersected by the TS to create the random point (RP)-based dataset. The next step was to compute the certainty factor (CF) of each factor to constitute a CF-based dataset. MLR was applied to the two datasets for landslide risk modeling. The probability of landslides was then calculated in each pixel, and risk maps were produced. The overall accuracy of these two models versus VS was 91.5% and 90.4% with a Kappa coefficient of 0.814 and 0.782, respectively. The RP-based MLR modeling achieved more reliable predictions and its risk map seems more plausible for providing technical support for implementing disaster prevention measures in Guixi.</description><identifier>ISSN: 2071-1050</identifier><identifier>EISSN: 2071-1050</identifier><identifier>DOI: 10.3390/su13094830</identifier><language>eng</language><publisher>BASEL: Mdpi</publisher><subject><![CDATA[Algorithms ; Artificial intelligence ; Datasets ; Earth science ; Emergency preparedness ; Environmental factors ; Environmental risk ; Environmental Sciences ; Environmental Sciences & Ecology ; Environmental Studies ; Geology ; Green & Sustainable Science & Technology ; Landslides ; Landslides & mudslides ; Life Sciences & Biomedicine ; Machine learning ; Model accuracy ; Modelling ; Neural networks ; Polygons ; Precipitation ; Prevention ; Risk assessment ; Risk management ; Risk reduction ; Science & Technology ; Science & Technology - Other Topics ; Statistical analysis ; Technical services ; Vegetation ; Zoning]]></subject><ispartof>Sustainability, 2021-05, Vol.13 (9), p.4830, Article 4830</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/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>21</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000650908800001</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c295t-120f0dfdd22681e5657da5b0864464961163f32c698950dc0551e432518875be3</citedby><cites>FETCH-LOGICAL-c295t-120f0dfdd22681e5657da5b0864464961163f32c698950dc0551e432518875be3</cites><orcidid>0000-0001-6362-9840 ; 0000-0003-0662-8045 ; 0000-0003-3056-3069 ; 0000-0003-1998-2916</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,781,785,27928,27929,39261,39262</link.rule.ids></links><search><creatorcontrib>Huangfu, Wenchao</creatorcontrib><creatorcontrib>Wu, Weicheng</creatorcontrib><creatorcontrib>Zhou, Xiaoting</creatorcontrib><creatorcontrib>Lin, Ziyu</creatorcontrib><creatorcontrib>Zhang, Guiliang</creatorcontrib><creatorcontrib>Chen, Renxiang</creatorcontrib><creatorcontrib>Song, Yong</creatorcontrib><creatorcontrib>Lang, Tao</creatorcontrib><creatorcontrib>Qin, Yaozu</creatorcontrib><creatorcontrib>Ou, Penghui</creatorcontrib><creatorcontrib>Zhang, Yang</creatorcontrib><creatorcontrib>Xie, Lifeng</creatorcontrib><creatorcontrib>Huang, Xiaolan</creatorcontrib><creatorcontrib>Fu, Xiao</creatorcontrib><creatorcontrib>Li, Jie</creatorcontrib><creatorcontrib>Jiang, Jingheng</creatorcontrib><creatorcontrib>Zhang, Ming</creatorcontrib><creatorcontrib>Liu, Yixuan</creatorcontrib><creatorcontrib>Peng, Shanling</creatorcontrib><creatorcontrib>Shao, Chongjian</creatorcontrib><creatorcontrib>Bai, Yonghui</creatorcontrib><creatorcontrib>Zhang, Xiaofeng</creatorcontrib><creatorcontrib>Liu, Xiangtong</creatorcontrib><creatorcontrib>Liu, Wenheng</creatorcontrib><title>Landslide Geo-Hazard Risk Mapping Using Logistic Regression Modeling in Guixi, Jiangxi, China</title><title>Sustainability</title><addtitle>SUSTAINABILITY-BASEL</addtitle><description>Reliable prediction of landslide occurrence is important for hazard risk reduction and prevention. Taking Guixi in northeast Jiangxi as an example, this research aimed to conduct such a landslide risk assessment using a multiple logistic regression (MLR) algorithm. Field-investigated landslides and non-landslide sites were converted into polygons. We randomly generated 50,000 sampling points to intersect these polygons and the intersected points were divided into two parts, a training set (TS) and a validation set (VT) in a ratio of 7 to 3. Thirteen geo-environmental factors, including elevation, slope, and distance from roads were employed as hazard-causative factors, which were intersected by the TS to create the random point (RP)-based dataset. The next step was to compute the certainty factor (CF) of each factor to constitute a CF-based dataset. MLR was applied to the two datasets for landslide risk modeling. The probability of landslides was then calculated in each pixel, and risk maps were produced. The overall accuracy of these two models versus VS was 91.5% and 90.4% with a Kappa coefficient of 0.814 and 0.782, respectively. The RP-based MLR modeling achieved more reliable predictions and its risk map seems more plausible for providing technical support for implementing disaster prevention measures in Guixi.</description><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Datasets</subject><subject>Earth science</subject><subject>Emergency preparedness</subject><subject>Environmental factors</subject><subject>Environmental risk</subject><subject>Environmental Sciences</subject><subject>Environmental Sciences & Ecology</subject><subject>Environmental Studies</subject><subject>Geology</subject><subject>Green & Sustainable Science & Technology</subject><subject>Landslides</subject><subject>Landslides & mudslides</subject><subject>Life Sciences & Biomedicine</subject><subject>Machine learning</subject><subject>Model accuracy</subject><subject>Modelling</subject><subject>Neural networks</subject><subject>Polygons</subject><subject>Precipitation</subject><subject>Prevention</subject><subject>Risk assessment</subject><subject>Risk management</subject><subject>Risk reduction</subject><subject>Science & Technology</subject><subject>Science & Technology - 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Taking Guixi in northeast Jiangxi as an example, this research aimed to conduct such a landslide risk assessment using a multiple logistic regression (MLR) algorithm. Field-investigated landslides and non-landslide sites were converted into polygons. We randomly generated 50,000 sampling points to intersect these polygons and the intersected points were divided into two parts, a training set (TS) and a validation set (VT) in a ratio of 7 to 3. Thirteen geo-environmental factors, including elevation, slope, and distance from roads were employed as hazard-causative factors, which were intersected by the TS to create the random point (RP)-based dataset. The next step was to compute the certainty factor (CF) of each factor to constitute a CF-based dataset. MLR was applied to the two datasets for landslide risk modeling. The probability of landslides was then calculated in each pixel, and risk maps were produced. The overall accuracy of these two models versus VS was 91.5% and 90.4% with a Kappa coefficient of 0.814 and 0.782, respectively. The RP-based MLR modeling achieved more reliable predictions and its risk map seems more plausible for providing technical support for implementing disaster prevention measures in Guixi.</abstract><cop>BASEL</cop><pub>Mdpi</pub><doi>10.3390/su13094830</doi><tpages>19</tpages><orcidid>https://orcid.org/0000-0001-6362-9840</orcidid><orcidid>https://orcid.org/0000-0003-0662-8045</orcidid><orcidid>https://orcid.org/0000-0003-3056-3069</orcidid><orcidid>https://orcid.org/0000-0003-1998-2916</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Artificial intelligence Datasets Earth science Emergency preparedness Environmental factors Environmental risk Environmental Sciences Environmental Sciences & Ecology Environmental Studies Geology Green & Sustainable Science & Technology Landslides Landslides & mudslides Life Sciences & Biomedicine Machine learning Model accuracy Modelling Neural networks Polygons Precipitation Prevention Risk assessment Risk management Risk reduction Science & Technology Science & Technology - Other Topics Statistical analysis Technical services Vegetation Zoning |
| title | Landslide Geo-Hazard Risk Mapping Using Logistic Regression Modeling in Guixi, Jiangxi, China |
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