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Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Physical Model-Based Landslide Susceptibility Mapping of Himalayan Highways Considering the Coupled Effect of Rainfall and Earthquake
https://orcid.org/0000-0001-7056-0650
https://orcid.org/0000-0002-6283-4364
The impact of earthquake-induced landslides is a global concern, particularly in the Himalayas and its nearby region, leading to substantial socioeconomic losses. High seismicity along with intense rainfall and steep slopes make the Himalayan region more vulnerable to these landslides. Existing research on regional scale analysis has mainly focused on studying landslides triggered by rainfall or earthquakes separately, with limited studies considering the combined effects of both. The objective of this research is to develop a GIS-based data integration approach for mapping shallow translational landslide susceptibility at regional scale, considering the combined effect of earthquakes and rainfall. The methodology utilizes a combination of the pseudostatic approach and hydrological models, taking advantage of publicly available topographic, seismic, geological, and remote sensing data and requiring basic geotechnical properties obtained from conventional tests to determine factor of safety (FoS). The approach has been implemented along an important and strategic highway corridor in the Indian state of Sikkim, located in the Eastern Himalayan region. The FoS distribution maps developed using the proposed approach have been validated against the reported landslides triggered in the study area due to the 2011 moment magnitude () 6.9 Sikkim earthquake. The proposed approach performed well in terms of prediction, with a success rate of 74.75%. Finally, the FoS distribution maps are generated for different scenarios. These maps provide significant insights that can be utilized by local governments, transportation agencies, and policymakers to proactively identify high-risk zones and develop early warning systems to mitigate the risk.
The Himalayan region has experienced a surge in tourist activity and infrastructure development in the past decade. However, the steep slopes, coupled with high seismic activity and heavy monsoonal rainfall, make it naturally prone to landslides. Landslides triggered by earthquakes can further worsen the effects of the seismic activity by damaging infrastructure, including roads, leading to challenges in rescue operations, disruptions in the supply of essential commodities, and inconvenience to travelers. The occurrence of earthquake during the monsoonal season can significantly increase the likelihood of landslides. To address this issue, a methodology has been proposed to identify areas prone to landslides caused by combined action of rainfall and earthquakes, focusing on a strategic highway in the Eastern Himalayas. By considering different scenarios of rainfall and earthquakes, various maps have been created which highlights the high-risk zones. These maps serve as valuable guides, indicating where it’s unsafe to construct infrastructure and which highway sections are more susceptible to landslides. Equipped with this information, policy makers can construct more robust roads, plan alternative routes, ensure better connectivity, and provide early warnings to minimize the disruptive effects of landslides on people and infrastructure.
Physical Model-Based Landslide Susceptibility Mapping of Himalayan Highways Considering the Coupled Effect of Rainfall and Earthquake
https://orcid.org/0000-0001-7056-0650
https://orcid.org/0000-0002-6283-4364
The impact of earthquake-induced landslides is a global concern, particularly in the Himalayas and its nearby region, leading to substantial socioeconomic losses. High seismicity along with intense rainfall and steep slopes make the Himalayan region more vulnerable to these landslides. Existing research on regional scale analysis has mainly focused on studying landslides triggered by rainfall or earthquakes separately, with limited studies considering the combined effects of both. The objective of this research is to develop a GIS-based data integration approach for mapping shallow translational landslide susceptibility at regional scale, considering the combined effect of earthquakes and rainfall. The methodology utilizes a combination of the pseudostatic approach and hydrological models, taking advantage of publicly available topographic, seismic, geological, and remote sensing data and requiring basic geotechnical properties obtained from conventional tests to determine factor of safety (FoS). The approach has been implemented along an important and strategic highway corridor in the Indian state of Sikkim, located in the Eastern Himalayan region. The FoS distribution maps developed using the proposed approach have been validated against the reported landslides triggered in the study area due to the 2011 moment magnitude () 6.9 Sikkim earthquake. The proposed approach performed well in terms of prediction, with a success rate of 74.75%. Finally, the FoS distribution maps are generated for different scenarios. These maps provide significant insights that can be utilized by local governments, transportation agencies, and policymakers to proactively identify high-risk zones and develop early warning systems to mitigate the risk.
The Himalayan region has experienced a surge in tourist activity and infrastructure development in the past decade. However, the steep slopes, coupled with high seismic activity and heavy monsoonal rainfall, make it naturally prone to landslides. Landslides triggered by earthquakes can further worsen the effects of the seismic activity by damaging infrastructure, including roads, leading to challenges in rescue operations, disruptions in the supply of essential commodities, and inconvenience to travelers. The occurrence of earthquake during the monsoonal season can significantly increase the likelihood of landslides. To address this issue, a methodology has been proposed to identify areas prone to landslides caused by combined action of rainfall and earthquakes, focusing on a strategic highway in the Eastern Himalayas. By considering different scenarios of rainfall and earthquakes, various maps have been created which highlights the high-risk zones. These maps serve as valuable guides, indicating where it’s unsafe to construct infrastructure and which highway sections are more susceptible to landslides. Equipped with this information, policy makers can construct more robust roads, plan alternative routes, ensure better connectivity, and provide early warnings to minimize the disruptive effects of landslides on people and infrastructure.
Physical Model-Based Landslide Susceptibility Mapping of Himalayan Highways Considering the Coupled Effect of Rainfall and Earthquake
https://orcid.org/0000-0001-7056-0650
https://orcid.org/0000-0002-6283-4364
The impact of earthquake-induced landslides is a global concern, particularly in the Himalayas and its nearby region, leading to substantial socioeconomic losses. High seismicity along with intense rainfall and steep slopes make the Himalayan region more vulnerable to these landslides. Existing research on regional scale analysis has mainly focused on studying landslides triggered by rainfall or earthquakes separately, with limited studies considering the combined effects of both. The objective of this research is to develop a GIS-based data integration approach for mapping shallow translational landslide susceptibility at regional scale, considering the combined effect of earthquakes and rainfall. The methodology utilizes a combination of the pseudostatic approach and hydrological models, taking advantage of publicly available topographic, seismic, geological, and remote sensing data and requiring basic geotechnical properties obtained from conventional tests to determine factor of safety (FoS). The approach has been implemented along an important and strategic highway corridor in the Indian state of Sikkim, located in the Eastern Himalayan region. The FoS distribution maps developed using the proposed approach have been validated against the reported landslides triggered in the study area due to the 2011 moment magnitude () 6.9 Sikkim earthquake. The proposed approach performed well in terms of prediction, with a success rate of 74.75%. Finally, the FoS distribution maps are generated for different scenarios. These maps provide significant insights that can be utilized by local governments, transportation agencies, and policymakers to proactively identify high-risk zones and develop early warning systems to mitigate the risk.
The Himalayan region has experienced a surge in tourist activity and infrastructure development in the past decade. However, the steep slopes, coupled with high seismic activity and heavy monsoonal rainfall, make it naturally prone to landslides. Landslides triggered by earthquakes can further worsen the effects of the seismic activity by damaging infrastructure, including roads, leading to challenges in rescue operations, disruptions in the supply of essential commodities, and inconvenience to travelers. The occurrence of earthquake during the monsoonal season can significantly increase the likelihood of landslides. To address this issue, a methodology has been proposed to identify areas prone to landslides caused by combined action of rainfall and earthquakes, focusing on a strategic highway in the Eastern Himalayas. By considering different scenarios of rainfall and earthquakes, various maps have been created which highlights the high-risk zones. These maps serve as valuable guides, indicating where it’s unsafe to construct infrastructure and which highway sections are more susceptible to landslides. Equipped with this information, policy makers can construct more robust roads, plan alternative routes, ensure better connectivity, and provide early warnings to minimize the disruptive effects of landslides on people and infrastructure.
Physical Model-Based Landslide Susceptibility Mapping of Himalayan Highways Considering the Coupled Effect of Rainfall and Earthquake
Nat. Hazards Rev.
Kumar, Saurav (Autor:in) / Sengupta, Aniruddha (Autor:in)
01.08.2024
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Rainfall-Induced Landslide Susceptibility Using a Rainfall–Runoff Model and Logistic Regression
| DOAJ | 2018