Remote sensing and optimized neural networks for landslide risk assessment: Paving the way for mitigating Afghanistan landslide damage
Landslides caused by mega earthquakes and other extreme climate change pose a major threat to lives and infrastructure. However, the lack of a detailed and timely landslide inventory and relevant risk assessment attributable to ongoing conflicts limits the effective prevention measures in Afghanista...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Ecological Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X23013213 |
| _version_ | 1797643632536190976 |
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| author | Ming Chang Xiangyang Dou Fenghuan Su Bo Yu |
| author_facet | Ming Chang Xiangyang Dou Fenghuan Su Bo Yu |
| author_sort | Ming Chang |
| collection | DOAJ |
| description | Landslides caused by mega earthquakes and other extreme climate change pose a major threat to lives and infrastructure. However, the lack of a detailed and timely landslide inventory and relevant risk assessment attributable to ongoing conflicts limits the effective prevention measures in Afghanistan. This study presents the first landslide inventory covering the whole nation of Afghanistan from 2015 to the present utilizing Google Earth Pro imagery and manual interpretation. Based on this inventory of 3,260 mapped landslides, we analyzed the distributional characteristics of landslides in Afghanistan and conducted a risk assessment that included landslide susceptibility and hazard, and vulnerability of the bearing areas. The existing regional studies attest to the accuracy and reliability of the inventory, and the results of the risk assessment using the optimized neural network method in this study are well validated. This study can provide a good database for the Afghan government to carry out relevant pre-disaster warnings and post-disaster reconstruction, which can help to delineate hotspots where landslides may occur, and reduce potential economic losses and human casualties from future landslides. |
| first_indexed | 2024-03-11T14:17:43Z |
| format | Article |
| id | doaj.art-e5a2a831570b4999be16d9296fbe8fdd |
| institution | Directory Open Access Journal |
| issn | 1470-160X |
| language | English |
| last_indexed | 2024-03-11T14:17:43Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj.art-e5a2a831570b4999be16d9296fbe8fdd2023-11-01T04:46:36ZengElsevierEcological Indicators1470-160X2023-12-01156111179Remote sensing and optimized neural networks for landslide risk assessment: Paving the way for mitigating Afghanistan landslide damageMing Chang0Xiangyang Dou1Fenghuan Su2Bo Yu3State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, Sichuan, China; Key Laboratory of Mountain Hazards and Earth Surface Processes, Chinese Academy of Sciences, Chengdu 610044, ChinaState Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, Sichuan, China; Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7522 NB, Enschede, NetherlandsKey Laboratory of Mountain Hazards and Earth Surface Processes, Chinese Academy of Sciences, Chengdu 610044, China; Corresponding author.State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, Sichuan, ChinaLandslides caused by mega earthquakes and other extreme climate change pose a major threat to lives and infrastructure. However, the lack of a detailed and timely landslide inventory and relevant risk assessment attributable to ongoing conflicts limits the effective prevention measures in Afghanistan. This study presents the first landslide inventory covering the whole nation of Afghanistan from 2015 to the present utilizing Google Earth Pro imagery and manual interpretation. Based on this inventory of 3,260 mapped landslides, we analyzed the distributional characteristics of landslides in Afghanistan and conducted a risk assessment that included landslide susceptibility and hazard, and vulnerability of the bearing areas. The existing regional studies attest to the accuracy and reliability of the inventory, and the results of the risk assessment using the optimized neural network method in this study are well validated. This study can provide a good database for the Afghan government to carry out relevant pre-disaster warnings and post-disaster reconstruction, which can help to delineate hotspots where landslides may occur, and reduce potential economic losses and human casualties from future landslides.http://www.sciencedirect.com/science/article/pii/S1470160X23013213LandslideAfghanistanRisk assessmentRemote sensingNeural networks |
| spellingShingle | Ming Chang Xiangyang Dou Fenghuan Su Bo Yu Remote sensing and optimized neural networks for landslide risk assessment: Paving the way for mitigating Afghanistan landslide damage Ecological Indicators Landslide Afghanistan Risk assessment Remote sensing Neural networks |
| title | Remote sensing and optimized neural networks for landslide risk assessment: Paving the way for mitigating Afghanistan landslide damage |
| title_full | Remote sensing and optimized neural networks for landslide risk assessment: Paving the way for mitigating Afghanistan landslide damage |
| title_fullStr | Remote sensing and optimized neural networks for landslide risk assessment: Paving the way for mitigating Afghanistan landslide damage |
| title_full_unstemmed | Remote sensing and optimized neural networks for landslide risk assessment: Paving the way for mitigating Afghanistan landslide damage |
| title_short | Remote sensing and optimized neural networks for landslide risk assessment: Paving the way for mitigating Afghanistan landslide damage |
| title_sort | remote sensing and optimized neural networks for landslide risk assessment paving the way for mitigating afghanistan landslide damage |
| topic | Landslide Afghanistan Risk assessment Remote sensing Neural networks |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X23013213 |
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