Affiliation:
1. Jagannath University
2. UTS: University of Technology Sydney
Abstract
Abstract
In recent time, landslide has become the major concern in the southeast part of Bangladesh. The study aims to develop comprehensive landslide risk mapping by applying the analytical hierarchy process (AHP) and geospatial techniques in Ukhiya and Teknaf Upazilas (highly populated Rohingya Refugee Settlement area) located in the southeast part of Bangladesh. To assess the landslide risk, 12 influencing criteria of hazard, vulnerability and exposure such as precipitation intensity, landslide inventory, distance to fault line, stream density, distance to stream network, elevation, aspect, slope, geology, normalized difference vegetation index (NDVI), landuse-landcover (LULC), and population density have been selected under the relevant components of risk. The spatial criteria were weighted using AHP, and the weighted overlay techniques were used to produce the risk map. The findings demonstrate that 2.19% of the total area is classified as a very-high risk zone and 12.74% is categorized as a high-risk zone. Moderate risk areas cover 23.08% of the total area. The risk map is validated by the landslides inventory. The outcomes can be used by any of the concerned authorities to take the necessary steps to reduce the impact of landslides.
Publisher
Research Square Platform LLC
Reference75 articles.
1. Landslide susceptibility modelling applying user-defined weighting and data-driven statistical techniques in Cox’s Bazar Municipality, Bangladesh;Ahmed B;Nat Hazards,2015
2. Ahmed B (2017) Community Vulnerability to Landslides in Bangladesh. Ph.D. Thesis, University College London
3. The root causes of landslide vulnerability in Bangladesh;Ahmed B;Landslides,2021
4. Ahmed B, Rahman M, Sammonds P, Islam R, Uddin K (2020) Application of geospatial technologies in developing a dynamic landslide early warning system in a humanitarian context: the Rohingya refugee crisis in Cox’s Bazar, Bangladesh. Geomatics, Natural Hazards and Risk, 446–468. doi:10.1080/19475705.2020.1730988
5. Machine learning for predicting landslide risk of Rohingya refugee camp infrastructure;Ahmed N;J Inform Telecommunication,2020