Landslide Susceptibility Mapping Using Multi-Criteria Decision-Making (MCDM), Statistical, and Machine Learning Models in the Aube Department, France
Landslides are among the most relevant and potentially damaging natural risks, causing material and human losses. The department of Aube in France is well known for several major landslide occurrences. This study focuses on the assessment of Landslide Susceptibility (LS) using the Frequency Ratio (F...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Earth |
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| Online Access: | https://www.mdpi.com/2673-4834/4/3/37 |
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| author | Abdessamad Jari Achraf Khaddari Soufiane Hajaj El Mostafa Bachaoui Sabine Mohammedi Amine Jellouli Hassan Mosaid Abderrazak El Harti Ahmed Barakat |
| author_facet | Abdessamad Jari Achraf Khaddari Soufiane Hajaj El Mostafa Bachaoui Sabine Mohammedi Amine Jellouli Hassan Mosaid Abderrazak El Harti Ahmed Barakat |
| author_sort | Abdessamad Jari |
| collection | DOAJ |
| description | Landslides are among the most relevant and potentially damaging natural risks, causing material and human losses. The department of Aube in France is well known for several major landslide occurrences. This study focuses on the assessment of Landslide Susceptibility (LS) using the Frequency Ratio (FR) as a statistical method, the Analytic Hierarchy Process (AHP) as a Multi-Criteria Decision-Making (MCDM) method, and Random Forest (RF) and k-Nearest Neighbor (kNN) as machine learning methods in the Aube department, northeast of France. Subsequently, the thematic layers of eight landslide causative factors, including distance to hydrography, density of quarries, elevation, slope, lithology, distance to roads, distance to faults, and rainfall, were generated in the geographic information system (GIS) environment. The thematic layers were integrated and processed to map landslide susceptibility in the study area. On the other hand, an inventory of landslides was carried out based on the database created by the French Geological Survey (BRGM), where 157 landslide occurrences were selected, and then RF and kNN models were trained to generate landslide maps (LSMs) of the study area. The generated maps were assessed by using the Area Under the Receiver Operating Characteristic Curve (ROC AUC). Subsequently, the accuracy assessment of the FR model revealed more accurate results (AUC = 66.0%) than AHP, outperforming the latter by 6%, while machine learning models results showed that RF gave better results than kNN (<7.3%) with AUC = 95%. Following the analysis of LS mapping results, lithology, distance to the hydrographic network, distance to roads, and elevation were the four main factors controlling landslide susceptibility in the study area. Future mitigation and protection activities within the Aube department can benefit from the present study mapping results, implicating an optimized land management for decision-makers. |
| first_indexed | 2024-03-10T22:51:19Z |
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| institution | Directory Open Access Journal |
| issn | 2673-4834 |
| language | English |
| last_indexed | 2024-03-10T22:51:19Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Earth |
| spelling | doaj.art-8d880f015d90450ba72055bd533b0c232023-11-19T10:17:49ZengMDPI AGEarth2673-48342023-09-014369871310.3390/earth4030037Landslide Susceptibility Mapping Using Multi-Criteria Decision-Making (MCDM), Statistical, and Machine Learning Models in the Aube Department, FranceAbdessamad Jari0Achraf Khaddari1Soufiane Hajaj2El Mostafa Bachaoui3Sabine Mohammedi4Amine Jellouli5Hassan Mosaid6Abderrazak El Harti7Ahmed Barakat8Applied Sciences Ile-de-France Institute, Gustave Eiffel University, 77420 Champs-sur-Marne, FranceLaboratory of Geosciences, Faculty of Sciences, Ibn Tofail University, Morocco University Campus, Kenitra PB 133, MoroccoGeomatics, Georesources and Environment Laboratory, Faculty of Sciences and Techniques, Sultan Moulay Slimane University, Beni-Mellal 23000, MoroccoGeomatics, Georesources and Environment Laboratory, Faculty of Sciences and Techniques, Sultan Moulay Slimane University, Beni-Mellal 23000, MoroccoApplied Sciences Ile-de-France Institute, Gustave Eiffel University, 77420 Champs-sur-Marne, FranceGeomatics, Georesources and Environment Laboratory, Faculty of Sciences and Techniques, Sultan Moulay Slimane University, Beni-Mellal 23000, MoroccoGeomatics, Georesources and Environment Laboratory, Faculty of Sciences and Techniques, Sultan Moulay Slimane University, Beni-Mellal 23000, MoroccoGeomatics, Georesources and Environment Laboratory, Faculty of Sciences and Techniques, Sultan Moulay Slimane University, Beni-Mellal 23000, MoroccoGeomatics, Georesources and Environment Laboratory, Faculty of Sciences and Techniques, Sultan Moulay Slimane University, Beni-Mellal 23000, MoroccoLandslides are among the most relevant and potentially damaging natural risks, causing material and human losses. The department of Aube in France is well known for several major landslide occurrences. This study focuses on the assessment of Landslide Susceptibility (LS) using the Frequency Ratio (FR) as a statistical method, the Analytic Hierarchy Process (AHP) as a Multi-Criteria Decision-Making (MCDM) method, and Random Forest (RF) and k-Nearest Neighbor (kNN) as machine learning methods in the Aube department, northeast of France. Subsequently, the thematic layers of eight landslide causative factors, including distance to hydrography, density of quarries, elevation, slope, lithology, distance to roads, distance to faults, and rainfall, were generated in the geographic information system (GIS) environment. The thematic layers were integrated and processed to map landslide susceptibility in the study area. On the other hand, an inventory of landslides was carried out based on the database created by the French Geological Survey (BRGM), where 157 landslide occurrences were selected, and then RF and kNN models were trained to generate landslide maps (LSMs) of the study area. The generated maps were assessed by using the Area Under the Receiver Operating Characteristic Curve (ROC AUC). Subsequently, the accuracy assessment of the FR model revealed more accurate results (AUC = 66.0%) than AHP, outperforming the latter by 6%, while machine learning models results showed that RF gave better results than kNN (<7.3%) with AUC = 95%. Following the analysis of LS mapping results, lithology, distance to the hydrographic network, distance to roads, and elevation were the four main factors controlling landslide susceptibility in the study area. Future mitigation and protection activities within the Aube department can benefit from the present study mapping results, implicating an optimized land management for decision-makers.https://www.mdpi.com/2673-4834/4/3/37landslide susceptibilityanalytic hierarchy processfrequency ratiorandom forestk-nearest neighborAube department |
| spellingShingle | Abdessamad Jari Achraf Khaddari Soufiane Hajaj El Mostafa Bachaoui Sabine Mohammedi Amine Jellouli Hassan Mosaid Abderrazak El Harti Ahmed Barakat Landslide Susceptibility Mapping Using Multi-Criteria Decision-Making (MCDM), Statistical, and Machine Learning Models in the Aube Department, France Earth landslide susceptibility analytic hierarchy process frequency ratio random forest k-nearest neighbor Aube department |
| title | Landslide Susceptibility Mapping Using Multi-Criteria Decision-Making (MCDM), Statistical, and Machine Learning Models in the Aube Department, France |
| title_full | Landslide Susceptibility Mapping Using Multi-Criteria Decision-Making (MCDM), Statistical, and Machine Learning Models in the Aube Department, France |
| title_fullStr | Landslide Susceptibility Mapping Using Multi-Criteria Decision-Making (MCDM), Statistical, and Machine Learning Models in the Aube Department, France |
| title_full_unstemmed | Landslide Susceptibility Mapping Using Multi-Criteria Decision-Making (MCDM), Statistical, and Machine Learning Models in the Aube Department, France |
| title_short | Landslide Susceptibility Mapping Using Multi-Criteria Decision-Making (MCDM), Statistical, and Machine Learning Models in the Aube Department, France |
| title_sort | landslide susceptibility mapping using multi criteria decision making mcdm statistical and machine learning models in the aube department france |
| topic | landslide susceptibility analytic hierarchy process frequency ratio random forest k-nearest neighbor Aube department |
| url | https://www.mdpi.com/2673-4834/4/3/37 |
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