Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Landslide Susceptibility Mapping Using Bivariate Statistical Models and GIS in Chattagram District, Bangladesh
https://orcid.org/0000-0001-5046-1671
Abstract Landslide is one of the most devastating hazards in Chattagram Dsitrict and has become a recurrent phenomenon in this region. This study attempts to produce Landslide Susceptibility Map (LSM) for Chattagram District of Bangladesh by using five GIS based bivariate statistical models, namely the Frequency Ratio (FR), Shanon’s Entropy (SE), Weight of Evidence (WofE), Information Value (IV) and Certainty Factor (CF). Landslide Inventory (2001–2017) of Chittagong Hilly Areas database was used to measure the relationship between the previous landslides with the landslide conditioning factors. SRTM DEM and Landsat satellite images were collected from USGS and the geological data were collected from GSB to produce the thematic layer of conditioning factors. Sixteen landslide conditioning factors of Slope Aspect, Slope Angle, Geology, Elevation, Plan Curvature, Profile Curvature, General Curvature, Topographic Wetness Index, Stream Power Index, Sediment Transport Index, Topographic Roughness Index, Distance to Stream, Distance to Anticline, Distance to Fault, Distance to Road and NDVI were used. The Area Under Curve (AUC) was used for validation of the LSMs. The predictive rate of AUC for FR, SE, WofE, IV and CF were 76.11%, 70.11%, 78.93%, 76.57% and 80.43% respectively. CF model indicates 15.04% of areas are highly susceptible to landslide. All the models showed that the high elevated areas are more susceptible to landslide where the low-lying river basin areas have a low probability of landslide occurrence. The findings of this research will contribute to land use planning, management and hazard mitigation of the CHT region.
Landslide Susceptibility Mapping Using Bivariate Statistical Models and GIS in Chattagram District, Bangladesh
https://orcid.org/0000-0001-5046-1671
Abstract Landslide is one of the most devastating hazards in Chattagram Dsitrict and has become a recurrent phenomenon in this region. This study attempts to produce Landslide Susceptibility Map (LSM) for Chattagram District of Bangladesh by using five GIS based bivariate statistical models, namely the Frequency Ratio (FR), Shanon’s Entropy (SE), Weight of Evidence (WofE), Information Value (IV) and Certainty Factor (CF). Landslide Inventory (2001–2017) of Chittagong Hilly Areas database was used to measure the relationship between the previous landslides with the landslide conditioning factors. SRTM DEM and Landsat satellite images were collected from USGS and the geological data were collected from GSB to produce the thematic layer of conditioning factors. Sixteen landslide conditioning factors of Slope Aspect, Slope Angle, Geology, Elevation, Plan Curvature, Profile Curvature, General Curvature, Topographic Wetness Index, Stream Power Index, Sediment Transport Index, Topographic Roughness Index, Distance to Stream, Distance to Anticline, Distance to Fault, Distance to Road and NDVI were used. The Area Under Curve (AUC) was used for validation of the LSMs. The predictive rate of AUC for FR, SE, WofE, IV and CF were 76.11%, 70.11%, 78.93%, 76.57% and 80.43% respectively. CF model indicates 15.04% of areas are highly susceptible to landslide. All the models showed that the high elevated areas are more susceptible to landslide where the low-lying river basin areas have a low probability of landslide occurrence. The findings of this research will contribute to land use planning, management and hazard mitigation of the CHT region.
Landslide Susceptibility Mapping Using Bivariate Statistical Models and GIS in Chattagram District, Bangladesh
https://orcid.org/0000-0001-5046-1671
Abstract Landslide is one of the most devastating hazards in Chattagram Dsitrict and has become a recurrent phenomenon in this region. This study attempts to produce Landslide Susceptibility Map (LSM) for Chattagram District of Bangladesh by using five GIS based bivariate statistical models, namely the Frequency Ratio (FR), Shanon’s Entropy (SE), Weight of Evidence (WofE), Information Value (IV) and Certainty Factor (CF). Landslide Inventory (2001–2017) of Chittagong Hilly Areas database was used to measure the relationship between the previous landslides with the landslide conditioning factors. SRTM DEM and Landsat satellite images were collected from USGS and the geological data were collected from GSB to produce the thematic layer of conditioning factors. Sixteen landslide conditioning factors of Slope Aspect, Slope Angle, Geology, Elevation, Plan Curvature, Profile Curvature, General Curvature, Topographic Wetness Index, Stream Power Index, Sediment Transport Index, Topographic Roughness Index, Distance to Stream, Distance to Anticline, Distance to Fault, Distance to Road and NDVI were used. The Area Under Curve (AUC) was used for validation of the LSMs. The predictive rate of AUC for FR, SE, WofE, IV and CF were 76.11%, 70.11%, 78.93%, 76.57% and 80.43% respectively. CF model indicates 15.04% of areas are highly susceptible to landslide. All the models showed that the high elevated areas are more susceptible to landslide where the low-lying river basin areas have a low probability of landslide occurrence. The findings of this research will contribute to land use planning, management and hazard mitigation of the CHT region.
Landslide Susceptibility Mapping Using Bivariate Statistical Models and GIS in Chattagram District, Bangladesh
Chowdhury, Md. Sharafat (Autor:in) / Hafsa, Bibi (Autor:in)
2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
BKL:
57.00$jBergbau: Allgemeines
/
38.58
Geomechanik
/
57.00
Bergbau: Allgemeines
/
56.20
Ingenieurgeologie, Bodenmechanik
/
38.58$jGeomechanik
/
56.20$jIngenieurgeologie$jBodenmechanik
Landslide Susceptibility Mapping Using Different GIS-Based Bivariate Models
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