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MODELLING OF POPULATION EXPOSURE TO LANDSLIDE RISK IN SUKABUMI, INDONESIA USING GIS

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Zeitschriftentitel: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Personen und Körperschaften: Wiguna, S., Gao, J.
In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-3/W8, 2019, S. 461-468
Format: E-Article
Sprache: Englisch
veröffentlicht:
Copernicus GmbH
author_facet Wiguna, S.
Gao, J.
Wiguna, S.
Gao, J.
author Wiguna, S.
Gao, J.
spellingShingle Wiguna, S.
Gao, J.
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
MODELLING OF POPULATION EXPOSURE TO LANDSLIDE RISK IN SUKABUMI, INDONESIA USING GIS
author_sort wiguna, s.
spelling Wiguna, S. Gao, J. 2194-9034 Copernicus GmbH http://dx.doi.org/10.5194/isprs-archives-xlii-3-w8-461-2019 <jats:p>Abstract. Modelling population exposure to landslide risk is essential for mitigating the damage of landslides. This research aims to assess population exposure to the modelled landslide risk in the Sukabumi region, Indonesia. Also assessed in this study is the importance of 10 environmental variables and their spatial association with past landslide occurrence using the Weight of Evidence (WOE) method. The accuracy of the modelled landslide susceptibility is assessed using the AUC ROC method. Village level population was spatially redistributed via dasymetric modelling, and overlaid with the modelled landslide susceptibility map differentiated by the source zone and the runout zone. It is found that slope, curvature, and soil are the three most influential variables of landslides. The WOE method is able to achieve a similar success rate (0.877) and prediction rate (0.876) in modelling landslide susceptibility. In 2017, medium (114,588 ha) and high (106,337 ha) susceptibility levels were the two largest classes while low (94,778 ha), very high (52,560), and very low (51,910 ha) susceptibility classes are much less extensive. An absolute majority of the population faces a high (1,081,875 people or 38.98% of the total population), and a medium (1,036,080 people or 37.33%) level of landslide risk. Those facing a low (409,658 people or 14.76%), very high (168,193 people or 6.06%), and very low susceptibility (79,656 people or 2.87%) account for slightly more than one fifth of the total population. These findings demonstrate the critical role of GIS in assessing the exposure of population to landslide risk from a diverse range of variables. </jats:p> MODELLING OF POPULATION EXPOSURE TO LANDSLIDE RISK IN SUKABUMI, INDONESIA USING GIS The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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title MODELLING OF POPULATION EXPOSURE TO LANDSLIDE RISK IN SUKABUMI, INDONESIA USING GIS
title_unstemmed MODELLING OF POPULATION EXPOSURE TO LANDSLIDE RISK IN SUKABUMI, INDONESIA USING GIS
title_full MODELLING OF POPULATION EXPOSURE TO LANDSLIDE RISK IN SUKABUMI, INDONESIA USING GIS
title_fullStr MODELLING OF POPULATION EXPOSURE TO LANDSLIDE RISK IN SUKABUMI, INDONESIA USING GIS
title_full_unstemmed MODELLING OF POPULATION EXPOSURE TO LANDSLIDE RISK IN SUKABUMI, INDONESIA USING GIS
title_short MODELLING OF POPULATION EXPOSURE TO LANDSLIDE RISK IN SUKABUMI, INDONESIA USING GIS
title_sort modelling of population exposure to landslide risk in sukabumi, indonesia using gis
url http://dx.doi.org/10.5194/isprs-archives-xlii-3-w8-461-2019
publishDate 2019
physical 461-468
description <jats:p>Abstract. Modelling population exposure to landslide risk is essential for mitigating the damage of landslides. This research aims to assess population exposure to the modelled landslide risk in the Sukabumi region, Indonesia. Also assessed in this study is the importance of 10 environmental variables and their spatial association with past landslide occurrence using the Weight of Evidence (WOE) method. The accuracy of the modelled landslide susceptibility is assessed using the AUC ROC method. Village level population was spatially redistributed via dasymetric modelling, and overlaid with the modelled landslide susceptibility map differentiated by the source zone and the runout zone. It is found that slope, curvature, and soil are the three most influential variables of landslides. The WOE method is able to achieve a similar success rate (0.877) and prediction rate (0.876) in modelling landslide susceptibility. In 2017, medium (114,588 ha) and high (106,337 ha) susceptibility levels were the two largest classes while low (94,778 ha), very high (52,560), and very low (51,910 ha) susceptibility classes are much less extensive. An absolute majority of the population faces a high (1,081,875 people or 38.98% of the total population), and a medium (1,036,080 people or 37.33%) level of landslide risk. Those facing a low (409,658 people or 14.76%), very high (168,193 people or 6.06%), and very low susceptibility (79,656 people or 2.87%) account for slightly more than one fifth of the total population. These findings demonstrate the critical role of GIS in assessing the exposure of population to landslide risk from a diverse range of variables. </jats:p>
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author Wiguna, S., Gao, J.
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description <jats:p>Abstract. Modelling population exposure to landslide risk is essential for mitigating the damage of landslides. This research aims to assess population exposure to the modelled landslide risk in the Sukabumi region, Indonesia. Also assessed in this study is the importance of 10 environmental variables and their spatial association with past landslide occurrence using the Weight of Evidence (WOE) method. The accuracy of the modelled landslide susceptibility is assessed using the AUC ROC method. Village level population was spatially redistributed via dasymetric modelling, and overlaid with the modelled landslide susceptibility map differentiated by the source zone and the runout zone. It is found that slope, curvature, and soil are the three most influential variables of landslides. The WOE method is able to achieve a similar success rate (0.877) and prediction rate (0.876) in modelling landslide susceptibility. In 2017, medium (114,588 ha) and high (106,337 ha) susceptibility levels were the two largest classes while low (94,778 ha), very high (52,560), and very low (51,910 ha) susceptibility classes are much less extensive. An absolute majority of the population faces a high (1,081,875 people or 38.98% of the total population), and a medium (1,036,080 people or 37.33%) level of landslide risk. Those facing a low (409,658 people or 14.76%), very high (168,193 people or 6.06%), and very low susceptibility (79,656 people or 2.87%) account for slightly more than one fifth of the total population. These findings demonstrate the critical role of GIS in assessing the exposure of population to landslide risk from a diverse range of variables. </jats:p>
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spelling Wiguna, S. Gao, J. 2194-9034 Copernicus GmbH http://dx.doi.org/10.5194/isprs-archives-xlii-3-w8-461-2019 <jats:p>Abstract. Modelling population exposure to landslide risk is essential for mitigating the damage of landslides. This research aims to assess population exposure to the modelled landslide risk in the Sukabumi region, Indonesia. Also assessed in this study is the importance of 10 environmental variables and their spatial association with past landslide occurrence using the Weight of Evidence (WOE) method. The accuracy of the modelled landslide susceptibility is assessed using the AUC ROC method. Village level population was spatially redistributed via dasymetric modelling, and overlaid with the modelled landslide susceptibility map differentiated by the source zone and the runout zone. It is found that slope, curvature, and soil are the three most influential variables of landslides. The WOE method is able to achieve a similar success rate (0.877) and prediction rate (0.876) in modelling landslide susceptibility. In 2017, medium (114,588 ha) and high (106,337 ha) susceptibility levels were the two largest classes while low (94,778 ha), very high (52,560), and very low (51,910 ha) susceptibility classes are much less extensive. An absolute majority of the population faces a high (1,081,875 people or 38.98% of the total population), and a medium (1,036,080 people or 37.33%) level of landslide risk. Those facing a low (409,658 people or 14.76%), very high (168,193 people or 6.06%), and very low susceptibility (79,656 people or 2.87%) account for slightly more than one fifth of the total population. These findings demonstrate the critical role of GIS in assessing the exposure of population to landslide risk from a diverse range of variables. </jats:p> MODELLING OF POPULATION EXPOSURE TO LANDSLIDE RISK IN SUKABUMI, INDONESIA USING GIS The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
spellingShingle Wiguna, S., Gao, J., The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, MODELLING OF POPULATION EXPOSURE TO LANDSLIDE RISK IN SUKABUMI, INDONESIA USING GIS
title MODELLING OF POPULATION EXPOSURE TO LANDSLIDE RISK IN SUKABUMI, INDONESIA USING GIS
title_full MODELLING OF POPULATION EXPOSURE TO LANDSLIDE RISK IN SUKABUMI, INDONESIA USING GIS
title_fullStr MODELLING OF POPULATION EXPOSURE TO LANDSLIDE RISK IN SUKABUMI, INDONESIA USING GIS
title_full_unstemmed MODELLING OF POPULATION EXPOSURE TO LANDSLIDE RISK IN SUKABUMI, INDONESIA USING GIS
title_short MODELLING OF POPULATION EXPOSURE TO LANDSLIDE RISK IN SUKABUMI, INDONESIA USING GIS
title_sort modelling of population exposure to landslide risk in sukabumi, indonesia using gis
title_unstemmed MODELLING OF POPULATION EXPOSURE TO LANDSLIDE RISK IN SUKABUMI, INDONESIA USING GIS
url http://dx.doi.org/10.5194/isprs-archives-xlii-3-w8-461-2019