Landslide susceptibility mapping using combined geospatial, FR and AHP models: a case study from Ethiopia’s highlands
Abstract This study performed landslide susceptibility mapping in Awabel Woreda, situated in the east Gojjam zone of the Amhara region in Ethiopia. The occurrence of landslides and slope instability is widespread in Awabel Woreda, leading to the devastation of agricultural fields, crops, and residen...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2024-12-01
|
| Series: | Discover Sustainability |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s43621-024-00730-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846112988711354368 |
|---|---|
| author | Tesfaldet Sisay Getachew Tesfaye Muralitharan Jothimani Talema Moged Reda Alemu Tadese |
| author_facet | Tesfaldet Sisay Getachew Tesfaye Muralitharan Jothimani Talema Moged Reda Alemu Tadese |
| author_sort | Tesfaldet Sisay |
| collection | DOAJ |
| description | Abstract This study performed landslide susceptibility mapping in Awabel Woreda, situated in the east Gojjam zone of the Amhara region in Ethiopia. The occurrence of landslides and slope instability is widespread in Awabel Woreda, leading to the devastation of agricultural fields, crops, and residences, the demise of animal life, and the forced relocation of local inhabitants from their dwellings. The present investigation utilized remote sensing and GIS techniques in conjunction with the Frequency Ratio (FR) and Analytical Hierarchy Process (AHP) models to map landslide risk zones. For the landslide susceptibility mapping of this study, nine causative factors, such as “elevation, slope, aspect, drainage density, lineament density, land use and land cover (LULC), soil texture, rainfall, and lithology”, were considered. A total of 130 past landslide events were identified via field survey and Google Earth image, of which 70% (91) of them were used as training datasets and 30% (39) were used as validation datasets of the proposed models (i.e., FR and AHP). The nine contributing variables and their classes were evaluated, and factor weights were computed using the IDRISI Selva 17.0 expert programme. The landslide susceptibility indexes (LSI) of the FR and AHP models were calculated and categorized into five relative zones using ArcGIS 10.7. The landslide susceptibility map (LSM) produced by the FR model shows that 93.2 km2 (14.52%) of the study area is classified as very low susceptibility, 167.51 km2 (26.09%) as low susceptibility, 174.10 km2 (27.12%) as moderate susceptibility, 137.03 km2 (21.34%) as high susceptibility, and 70.30 km2 (10.93%) as very high susceptibility to landslides. Based on the AHP model's LSM, different landslide susceptibility zones were identified in the study area. Specifically, 140.46 km2 (21.88%) of the region falls into the very low susceptibility zone, 116.78 km2 (18.59%) falls into the low susceptibility zone, 147.94 km2 (23.04%) falls into the moderate susceptibility zone, 154.04 km2 (23.99%) falls into the high susceptibility zone, and 82.78 km2 (12.89%) falls into the very high susceptibility zone. The validation investigation demonstrated that the FR and AHP models had accuracy rates of 89.73 and 87.18%, respectively. The FR model exhibited marginally more accurate results than AHP, primarily because of the direct correlation between previous and current occurrences of landslides. Nevertheless, the AHP model’s effectiveness relies on the individual’s expertise and the characteristics of the components that cause the outcome. The landslide susceptibility maps generated through these models provide valuable insights for land management and disaster mitigation efforts, with delineated zones indicating very low to very high susceptibility areas. |
| format | Article |
| id | doaj-art-466fd5295b2449059b6dca5ea4f72415 |
| institution | Kabale University |
| issn | 2662-9984 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Sustainability |
| spelling | doaj-art-466fd5295b2449059b6dca5ea4f724152024-12-22T12:08:19ZengSpringerDiscover Sustainability2662-99842024-12-015112510.1007/s43621-024-00730-4Landslide susceptibility mapping using combined geospatial, FR and AHP models: a case study from Ethiopia’s highlandsTesfaldet Sisay0Getachew Tesfaye1Muralitharan Jothimani2Talema Moged Reda3Alemu Tadese4Department of Geology, Mekdela Amba UniversityDepartment of Remote Sensing, Space Science and Geospatial InstituteDepartment of Geology, College of Natural and Computational Sciences, Arba Minch UniversityDepartment of Remote Sensing, Space Science and Geospatial InstituteDepartment of Geology, College of Natural and Computational Sciences, Arba Minch UniversityAbstract This study performed landslide susceptibility mapping in Awabel Woreda, situated in the east Gojjam zone of the Amhara region in Ethiopia. The occurrence of landslides and slope instability is widespread in Awabel Woreda, leading to the devastation of agricultural fields, crops, and residences, the demise of animal life, and the forced relocation of local inhabitants from their dwellings. The present investigation utilized remote sensing and GIS techniques in conjunction with the Frequency Ratio (FR) and Analytical Hierarchy Process (AHP) models to map landslide risk zones. For the landslide susceptibility mapping of this study, nine causative factors, such as “elevation, slope, aspect, drainage density, lineament density, land use and land cover (LULC), soil texture, rainfall, and lithology”, were considered. A total of 130 past landslide events were identified via field survey and Google Earth image, of which 70% (91) of them were used as training datasets and 30% (39) were used as validation datasets of the proposed models (i.e., FR and AHP). The nine contributing variables and their classes were evaluated, and factor weights were computed using the IDRISI Selva 17.0 expert programme. The landslide susceptibility indexes (LSI) of the FR and AHP models were calculated and categorized into five relative zones using ArcGIS 10.7. The landslide susceptibility map (LSM) produced by the FR model shows that 93.2 km2 (14.52%) of the study area is classified as very low susceptibility, 167.51 km2 (26.09%) as low susceptibility, 174.10 km2 (27.12%) as moderate susceptibility, 137.03 km2 (21.34%) as high susceptibility, and 70.30 km2 (10.93%) as very high susceptibility to landslides. Based on the AHP model's LSM, different landslide susceptibility zones were identified in the study area. Specifically, 140.46 km2 (21.88%) of the region falls into the very low susceptibility zone, 116.78 km2 (18.59%) falls into the low susceptibility zone, 147.94 km2 (23.04%) falls into the moderate susceptibility zone, 154.04 km2 (23.99%) falls into the high susceptibility zone, and 82.78 km2 (12.89%) falls into the very high susceptibility zone. The validation investigation demonstrated that the FR and AHP models had accuracy rates of 89.73 and 87.18%, respectively. The FR model exhibited marginally more accurate results than AHP, primarily because of the direct correlation between previous and current occurrences of landslides. Nevertheless, the AHP model’s effectiveness relies on the individual’s expertise and the characteristics of the components that cause the outcome. The landslide susceptibility maps generated through these models provide valuable insights for land management and disaster mitigation efforts, with delineated zones indicating very low to very high susceptibility areas.https://doi.org/10.1007/s43621-024-00730-4Landslide susceptibilityFrequency ratioAHPRemote sensingGISEthiopia |
| spellingShingle | Tesfaldet Sisay Getachew Tesfaye Muralitharan Jothimani Talema Moged Reda Alemu Tadese Landslide susceptibility mapping using combined geospatial, FR and AHP models: a case study from Ethiopia’s highlands Discover Sustainability Landslide susceptibility Frequency ratio AHP Remote sensing GIS Ethiopia |
| title | Landslide susceptibility mapping using combined geospatial, FR and AHP models: a case study from Ethiopia’s highlands |
| title_full | Landslide susceptibility mapping using combined geospatial, FR and AHP models: a case study from Ethiopia’s highlands |
| title_fullStr | Landslide susceptibility mapping using combined geospatial, FR and AHP models: a case study from Ethiopia’s highlands |
| title_full_unstemmed | Landslide susceptibility mapping using combined geospatial, FR and AHP models: a case study from Ethiopia’s highlands |
| title_short | Landslide susceptibility mapping using combined geospatial, FR and AHP models: a case study from Ethiopia’s highlands |
| title_sort | landslide susceptibility mapping using combined geospatial fr and ahp models a case study from ethiopia s highlands |
| topic | Landslide susceptibility Frequency ratio AHP Remote sensing GIS Ethiopia |
| url | https://doi.org/10.1007/s43621-024-00730-4 |
| work_keys_str_mv | AT tesfaldetsisay landslidesusceptibilitymappingusingcombinedgeospatialfrandahpmodelsacasestudyfromethiopiashighlands AT getachewtesfaye landslidesusceptibilitymappingusingcombinedgeospatialfrandahpmodelsacasestudyfromethiopiashighlands AT muralitharanjothimani landslidesusceptibilitymappingusingcombinedgeospatialfrandahpmodelsacasestudyfromethiopiashighlands AT talemamogedreda landslidesusceptibilitymappingusingcombinedgeospatialfrandahpmodelsacasestudyfromethiopiashighlands AT alemutadese landslidesusceptibilitymappingusingcombinedgeospatialfrandahpmodelsacasestudyfromethiopiashighlands |