Machine learning methods for landslide mapping studies: A comparative study of SVM and RF algorithms in the Oued Aoulai watershed (Morocco)
Effective management of watershed risks and landslides necessitates comprehensive landslide susceptibility mapping. Support vector machine (SVM) and random forest (RF) machine learning models were used to map the landslide susceptibility in Morocco’s Taounate Province. Detailed landslide inventory m...
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| Format: | Article |
| Language: | English |
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De Gruyter
2025-03-01
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| Series: | Open Geosciences |
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| Online Access: | https://doi.org/10.1515/geo-2022-0740 |
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| author | Ladel Latifa Mastere Mohamed Kader Shuraik Spalević Velibor Dudic Branislav |
| author_facet | Ladel Latifa Mastere Mohamed Kader Shuraik Spalević Velibor Dudic Branislav |
| author_sort | Ladel Latifa |
| collection | DOAJ |
| description | Effective management of watershed risks and landslides necessitates comprehensive landslide susceptibility mapping. Support vector machine (SVM) and random forest (RF) machine learning models were used to map the landslide susceptibility in Morocco’s Taounate Province. Detailed landslide inventory maps were generated based on aerial pictures, field research, and geotechnical survey reports. Factor correlation analysis carefully eliminated redundant factors from the original 14 landslide triggering factors. As a result, 30% of the sites were randomly chosen for testing, whereas 70% of the landslide locations were randomly picked for model training. The RF model achieved an area under the curve (AUC) of 94.7%, categorizing 30.07% of the region as low susceptibility, while the SVM model reached an AUC of 80.65%, indicating high sensitivity in 53.5% of the locations. These results provide crucial information for local authorities, supporting sound catchment planning and development strategies. |
| format | Article |
| id | doaj-art-1d95c91237e44f8d90f883dec47e4e67 |
| institution | DOAJ |
| issn | 2391-5447 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Open Geosciences |
| spelling | doaj-art-1d95c91237e44f8d90f883dec47e4e672025-08-20T02:47:04ZengDe GruyterOpen Geosciences2391-54472025-03-0117111010.1515/geo-2022-0740Machine learning methods for landslide mapping studies: A comparative study of SVM and RF algorithms in the Oued Aoulai watershed (Morocco)Ladel Latifa0Mastere Mohamed1Kader Shuraik2Spalević Velibor3Dudic Branislav4Geophysics and Natural Hazards Laboratory, Department of Geomorphology and Geomatics, Scientific Institute, Mohammed V University in Rabat, Avenue Ibn Batouta, Agdal, PO Box 703, 10106, Rabat-City, MoroccoGeophysics and Natural Hazards Laboratory, Department of Geomorphology and Geomatics, Scientific Institute, Mohammed V University in Rabat, Avenue Ibn Batouta, Agdal, PO Box 703, 10106, Rabat-City, MoroccoSchool of Engineering and Built Environment, Griffith University, Nathan, QLD 4111, AustraliaBiotechnical Faculty, University of Montenegro, Podgorica, 81000, MontenegroFaculty of Management, Comenius University Bratislava, Bratislava, 81499, SlovakiaEffective management of watershed risks and landslides necessitates comprehensive landslide susceptibility mapping. Support vector machine (SVM) and random forest (RF) machine learning models were used to map the landslide susceptibility in Morocco’s Taounate Province. Detailed landslide inventory maps were generated based on aerial pictures, field research, and geotechnical survey reports. Factor correlation analysis carefully eliminated redundant factors from the original 14 landslide triggering factors. As a result, 30% of the sites were randomly chosen for testing, whereas 70% of the landslide locations were randomly picked for model training. The RF model achieved an area under the curve (AUC) of 94.7%, categorizing 30.07% of the region as low susceptibility, while the SVM model reached an AUC of 80.65%, indicating high sensitivity in 53.5% of the locations. These results provide crucial information for local authorities, supporting sound catchment planning and development strategies.https://doi.org/10.1515/geo-2022-0740managementlandslide susceptibilitymachine learning modelsfactor correlation analysisarea under the curvemorocco |
| spellingShingle | Ladel Latifa Mastere Mohamed Kader Shuraik Spalević Velibor Dudic Branislav Machine learning methods for landslide mapping studies: A comparative study of SVM and RF algorithms in the Oued Aoulai watershed (Morocco) Open Geosciences management landslide susceptibility machine learning models factor correlation analysis area under the curve morocco |
| title | Machine learning methods for landslide mapping studies: A comparative study of SVM and RF algorithms in the Oued Aoulai watershed (Morocco) |
| title_full | Machine learning methods for landslide mapping studies: A comparative study of SVM and RF algorithms in the Oued Aoulai watershed (Morocco) |
| title_fullStr | Machine learning methods for landslide mapping studies: A comparative study of SVM and RF algorithms in the Oued Aoulai watershed (Morocco) |
| title_full_unstemmed | Machine learning methods for landslide mapping studies: A comparative study of SVM and RF algorithms in the Oued Aoulai watershed (Morocco) |
| title_short | Machine learning methods for landslide mapping studies: A comparative study of SVM and RF algorithms in the Oued Aoulai watershed (Morocco) |
| title_sort | machine learning methods for landslide mapping studies a comparative study of svm and rf algorithms in the oued aoulai watershed morocco |
| topic | management landslide susceptibility machine learning models factor correlation analysis area under the curve morocco |
| url | https://doi.org/10.1515/geo-2022-0740 |
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