Spatial assessment employing fusion logistic regression and frequency ratio models to monitor landslide susceptibility in the upper Blue Nile basin of Ethiopia: Muger watershed
Abstract At the global level, landslides are a dreadful hazard that restricts socio-economic and ecological balances. Recent human activities in hilly areas, coupled with geological predispositions, have potentially exacerbated landslide frequency and magnitude. However, the impacts of these factors...
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SpringerOpen
2024-11-01
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| Series: | Environmental Systems Research |
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| Online Access: | https://doi.org/10.1186/s40068-024-00382-3 |
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| author | Samuel Hailu Kiros Tsegay Deribew Ermias Teferi Mitiku Badasa Moisa Zenebe Reta Roba Shimelis Sishah Dagne Muluneh Woldetsadik |
| author_facet | Samuel Hailu Kiros Tsegay Deribew Ermias Teferi Mitiku Badasa Moisa Zenebe Reta Roba Shimelis Sishah Dagne Muluneh Woldetsadik |
| author_sort | Samuel Hailu |
| collection | DOAJ |
| description | Abstract At the global level, landslides are a dreadful hazard that restricts socio-economic and ecological balances. Recent human activities in hilly areas, coupled with geological predispositions, have potentially exacerbated landslide frequency and magnitude. However, the impacts of these factors on landslide occurrences in the upper Blue Nile basin of Ethiopia remain largely unexplored. This study aims to identify landslide triggers, quantify landslide-susceptible zones, and validate the landslide models. Topographic parameters, geology, hydrology, and land use-land cover inventories were utilized to generate a landslide susceptibility map. The factors were analyzed using a combination of logistic regression (LR) and frequency ratio (FR) models. The area under the curve (AUC) under the receiver operating characteristic (ROC) was used to compare the performance of the models. The result indicates that about 185 sq. km (40.2%) of the total falls under high to very-high susceptible landslide zones, and 92 sq. km (20%) falls under moderate susceptibility. Yet, 183.1 sq. km (40.2%) of the total is classified in the low-to-no landslide hazard zones. The LR and FR model validation demonstrated an average predictive performance of 75 and 81.45%, indicating good precision. The landslide evaluation can help policymakers identify LSH zones for early warning systems and mitigation purposes. |
| format | Article |
| id | doaj-art-0e13da42ac224f6b847bd95004dd4f61 |
| institution | OA Journals |
| issn | 2193-2697 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Environmental Systems Research |
| spelling | doaj-art-0e13da42ac224f6b847bd95004dd4f612025-08-20T02:22:29ZengSpringerOpenEnvironmental Systems Research2193-26972024-11-0113112110.1186/s40068-024-00382-3Spatial assessment employing fusion logistic regression and frequency ratio models to monitor landslide susceptibility in the upper Blue Nile basin of Ethiopia: Muger watershedSamuel Hailu0Kiros Tsegay Deribew1Ermias Teferi2Mitiku Badasa Moisa3Zenebe Reta Roba4Shimelis Sishah Dagne5Muluneh Woldetsadik6Horn of Africa Regional Environment Center and Network, Addis Ababa UniversityDepartment of Geography and Environmental Studies, Raya UniversityCenter for Environmental Studies, Addis Ababa UniversityDepartment of Earth Science, College of Natural and Computational Sciences, Wollega University Nekemte CampusDepartment of Forestry, College of Natural Resource and Agricultural Economics, Mattu UniversityDepartment of Geography and Environmental Studies, College of Social Science and Humanities, Arsi UniversityDepartment of Geography and Environmental Studies, Addis Ababa UniversityAbstract At the global level, landslides are a dreadful hazard that restricts socio-economic and ecological balances. Recent human activities in hilly areas, coupled with geological predispositions, have potentially exacerbated landslide frequency and magnitude. However, the impacts of these factors on landslide occurrences in the upper Blue Nile basin of Ethiopia remain largely unexplored. This study aims to identify landslide triggers, quantify landslide-susceptible zones, and validate the landslide models. Topographic parameters, geology, hydrology, and land use-land cover inventories were utilized to generate a landslide susceptibility map. The factors were analyzed using a combination of logistic regression (LR) and frequency ratio (FR) models. The area under the curve (AUC) under the receiver operating characteristic (ROC) was used to compare the performance of the models. The result indicates that about 185 sq. km (40.2%) of the total falls under high to very-high susceptible landslide zones, and 92 sq. km (20%) falls under moderate susceptibility. Yet, 183.1 sq. km (40.2%) of the total is classified in the low-to-no landslide hazard zones. The LR and FR model validation demonstrated an average predictive performance of 75 and 81.45%, indicating good precision. The landslide evaluation can help policymakers identify LSH zones for early warning systems and mitigation purposes.https://doi.org/10.1186/s40068-024-00382-3Frequency ratio modelLandslide incidenceLogistic regression modelROC |
| spellingShingle | Samuel Hailu Kiros Tsegay Deribew Ermias Teferi Mitiku Badasa Moisa Zenebe Reta Roba Shimelis Sishah Dagne Muluneh Woldetsadik Spatial assessment employing fusion logistic regression and frequency ratio models to monitor landslide susceptibility in the upper Blue Nile basin of Ethiopia: Muger watershed Environmental Systems Research Frequency ratio model Landslide incidence Logistic regression model ROC |
| title | Spatial assessment employing fusion logistic regression and frequency ratio models to monitor landslide susceptibility in the upper Blue Nile basin of Ethiopia: Muger watershed |
| title_full | Spatial assessment employing fusion logistic regression and frequency ratio models to monitor landslide susceptibility in the upper Blue Nile basin of Ethiopia: Muger watershed |
| title_fullStr | Spatial assessment employing fusion logistic regression and frequency ratio models to monitor landslide susceptibility in the upper Blue Nile basin of Ethiopia: Muger watershed |
| title_full_unstemmed | Spatial assessment employing fusion logistic regression and frequency ratio models to monitor landslide susceptibility in the upper Blue Nile basin of Ethiopia: Muger watershed |
| title_short | Spatial assessment employing fusion logistic regression and frequency ratio models to monitor landslide susceptibility in the upper Blue Nile basin of Ethiopia: Muger watershed |
| title_sort | spatial assessment employing fusion logistic regression and frequency ratio models to monitor landslide susceptibility in the upper blue nile basin of ethiopia muger watershed |
| topic | Frequency ratio model Landslide incidence Logistic regression model ROC |
| url | https://doi.org/10.1186/s40068-024-00382-3 |
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