Reducing Local Correlations Among Causal Factor Classifications as a Strategy to Improve Landslide Susceptibility Mapping

A landslide susceptibility map (LSM) is the basis of hazard and risk assessment, guiding land planning and utilization, early warning of disaster, etc. Researchers are often overly keen on hybridizing state-of-the-art models or exploring new mathematical susceptibility models to improve the accuracy...

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Autores principales:	Ting Xiao, Lanbing Yu, Weiming Tian, Chang Zhou, Luqi Wang
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	landslide susceptibility altitude and rivers local correlation reclassification of causal factors accuracy of landslide susceptibility map Science Q
Acceso en línea:	https://doaj.org/article/f7d153ec245845e5a5b56f25f4c3e0f0
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spelling	oai:doaj.org-article:f7d153ec245845e5a5b56f25f4c3e0f02021-11-22T06:12:55ZReducing Local Correlations Among Causal Factor Classifications as a Strategy to Improve Landslide Susceptibility Mapping2296-646310.3389/feart.2021.781674https://doaj.org/article/f7d153ec245845e5a5b56f25f4c3e0f02021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/feart.2021.781674/fullhttps://doaj.org/toc/2296-6463A landslide susceptibility map (LSM) is the basis of hazard and risk assessment, guiding land planning and utilization, early warning of disaster, etc. Researchers are often overly keen on hybridizing state-of-the-art models or exploring new mathematical susceptibility models to improve the accuracy of the susceptibility map in terms of a receiver operator characteristic curve. Correlation analysis of the causal factors is a necessary routine process before susceptibility modeling to ensure that the overall correlation among all factors is low. However, this overall correlation analysis is insufficient to detect a high local correlation among the causal factor classes. The objective of this study is to answer three questions: 1) Is there a high correlation between causal factors in some parts locally? 2) Does it affect the accuracy of landslide susceptibility assessment? and 3) How can this influence be eliminated? To this aim, Wanzhou County was taken as the test site, where landslide susceptibility assessment based on 12 causal factors has been previously performed using the frequency ratio (FR) model and random forest (RF) model. In this work, we conducted a local spatial correlation analysis of the “altitude” and “rivers” factors and found a sizeable spatial overlap between altitude-class-1 and rivers-class-1. The “altitude” and “rivers” factors were reclassified, and then the FR model and RF model were used to reevaluate the susceptibility and analyze the accuracy loss caused by the local spatial correlation of the two factors. The results demonstrated that the accuracy of LSMs was markedly enhanced after reclassification of “altitude” and “rivers,” especially for the RF model–based LSM. This research shed new light on the local correlation of causal factors arising from a particular geomorphology and their impact on susceptibility.Ting XiaoTing XiaoLanbing YuWeiming TianWeiming TianChang ZhouLuqi WangFrontiers Media S.A.articlelandslide susceptibilityaltitude and riverslocal correlationreclassification of causal factorsaccuracy of landslide susceptibility mapScienceQENFrontiers in Earth Science, Vol 9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	landslide susceptibility altitude and rivers local correlation reclassification of causal factors accuracy of landslide susceptibility map Science Q
spellingShingle	landslide susceptibility altitude and rivers local correlation reclassification of causal factors accuracy of landslide susceptibility map Science Q Ting Xiao Ting Xiao Lanbing Yu Weiming Tian Weiming Tian Chang Zhou Luqi Wang Reducing Local Correlations Among Causal Factor Classifications as a Strategy to Improve Landslide Susceptibility Mapping
description	A landslide susceptibility map (LSM) is the basis of hazard and risk assessment, guiding land planning and utilization, early warning of disaster, etc. Researchers are often overly keen on hybridizing state-of-the-art models or exploring new mathematical susceptibility models to improve the accuracy of the susceptibility map in terms of a receiver operator characteristic curve. Correlation analysis of the causal factors is a necessary routine process before susceptibility modeling to ensure that the overall correlation among all factors is low. However, this overall correlation analysis is insufficient to detect a high local correlation among the causal factor classes. The objective of this study is to answer three questions: 1) Is there a high correlation between causal factors in some parts locally? 2) Does it affect the accuracy of landslide susceptibility assessment? and 3) How can this influence be eliminated? To this aim, Wanzhou County was taken as the test site, where landslide susceptibility assessment based on 12 causal factors has been previously performed using the frequency ratio (FR) model and random forest (RF) model. In this work, we conducted a local spatial correlation analysis of the “altitude” and “rivers” factors and found a sizeable spatial overlap between altitude-class-1 and rivers-class-1. The “altitude” and “rivers” factors were reclassified, and then the FR model and RF model were used to reevaluate the susceptibility and analyze the accuracy loss caused by the local spatial correlation of the two factors. The results demonstrated that the accuracy of LSMs was markedly enhanced after reclassification of “altitude” and “rivers,” especially for the RF model–based LSM. This research shed new light on the local correlation of causal factors arising from a particular geomorphology and their impact on susceptibility.
format	article
author	Ting Xiao Ting Xiao Lanbing Yu Weiming Tian Weiming Tian Chang Zhou Luqi Wang
author_facet	Ting Xiao Ting Xiao Lanbing Yu Weiming Tian Weiming Tian Chang Zhou Luqi Wang
author_sort	Ting Xiao
title	Reducing Local Correlations Among Causal Factor Classifications as a Strategy to Improve Landslide Susceptibility Mapping
title_short	Reducing Local Correlations Among Causal Factor Classifications as a Strategy to Improve Landslide Susceptibility Mapping
title_full	Reducing Local Correlations Among Causal Factor Classifications as a Strategy to Improve Landslide Susceptibility Mapping
title_fullStr	Reducing Local Correlations Among Causal Factor Classifications as a Strategy to Improve Landslide Susceptibility Mapping
title_full_unstemmed	Reducing Local Correlations Among Causal Factor Classifications as a Strategy to Improve Landslide Susceptibility Mapping
title_sort	reducing local correlations among causal factor classifications as a strategy to improve landslide susceptibility mapping
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/f7d153ec245845e5a5b56f25f4c3e0f0
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Reducing Local Correlations Among Causal Factor Classifications as a Strategy to Improve Landslide Susceptibility Mapping

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