Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
GIS-Based Bivariate Statistical Model for Landslide Susceptibility Assessment Along a Srinagar-Bandipora Highway, Kashmir Himalaya
The Srinagar-Bandipora highway is one of the historical roads of Kashmir Valley and has always remained a supportive lifeline for the people in this region. This road is particularly vulnerable to landslides due to extreme weather conditions, complex mountainous terrain, and anthropological activity. Because of this, there are always frequent traffic disruptions and a severe threat to ongoing traffic and all activities associated with the region's social and economic development. As a result, it is critical to identify and mitigate the region's landslide risk, which causes significant inconvenience and economic and human losses. In the current study, landslide susceptibility mapping (LSM) was performed utilizing a bivariate frequency ratio model (FRM) and a geographical information system (GIS) platform. Besides, the landslide inventory details were prepared from primary and secondary data sources. The landslide susceptibility map (LSM) was generated from landslide inventory data and fifteen landslide causative factors (CFs). Therefore, the frequency ratio model examined the link between landslide CFs and landslides. LSM divided the area into five susceptibility categories, viz., very high (20.17Km2), high (39.22Km2), medium (62.49Km2), low (76.87Km2), and very low (56.90Km2). Subsequently, the landslide susceptibility map was then evaluated and compared to the existing landslide inventory data using the area under the curve (AUC) method. Moreover, the LSM created will be helpful to various stakeholders, including planners, designers, engineers, and the local community, for the upcoming building and maintenance projects in the study region.
GIS-Based Bivariate Statistical Model for Landslide Susceptibility Assessment Along a Srinagar-Bandipora Highway, Kashmir Himalaya
The Srinagar-Bandipora highway is one of the historical roads of Kashmir Valley and has always remained a supportive lifeline for the people in this region. This road is particularly vulnerable to landslides due to extreme weather conditions, complex mountainous terrain, and anthropological activity. Because of this, there are always frequent traffic disruptions and a severe threat to ongoing traffic and all activities associated with the region's social and economic development. As a result, it is critical to identify and mitigate the region's landslide risk, which causes significant inconvenience and economic and human losses. In the current study, landslide susceptibility mapping (LSM) was performed utilizing a bivariate frequency ratio model (FRM) and a geographical information system (GIS) platform. Besides, the landslide inventory details were prepared from primary and secondary data sources. The landslide susceptibility map (LSM) was generated from landslide inventory data and fifteen landslide causative factors (CFs). Therefore, the frequency ratio model examined the link between landslide CFs and landslides. LSM divided the area into five susceptibility categories, viz., very high (20.17Km2), high (39.22Km2), medium (62.49Km2), low (76.87Km2), and very low (56.90Km2). Subsequently, the landslide susceptibility map was then evaluated and compared to the existing landslide inventory data using the area under the curve (AUC) method. Moreover, the LSM created will be helpful to various stakeholders, including planners, designers, engineers, and the local community, for the upcoming building and maintenance projects in the study region.
GIS-Based Bivariate Statistical Model for Landslide Susceptibility Assessment Along a Srinagar-Bandipora Highway, Kashmir Himalaya
Springer Transactions in Civil and Environmental Engineering
Rastogi, Bal Krishna (Herausgeber:in) / Kothyari, Girish Chandra (Herausgeber:in) / Luirei, Khayingshing (Herausgeber:in) / Beigh, Iftikhar Hussain (Autor:in) / Ganaie, Mohmad Ashraf (Autor:in) / Bukhari, Syed Kaiser (Autor:in) / Ahmad, Shabir (Autor:in)
20.11.2024
27 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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