Gully erosion mapping susceptibility in a Mediterranean environment: A hybrid decision-making model
Gully erosion is one of the main natural hazards, especially in arid and semi-arid regions, destroying ecosystem service and human well-being. Thus, gully erosion susceptibility maps (GESM) are urgently needed for identifying priority areas on which appropriate measurements should be considered. Her...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-06-01
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| Series: | International Soil and Water Conservation Research |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2095633923000898 |
| _version_ | 1797226698982293504 |
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| author | Sliman Hitouri Mohajane Meriame Ali Sk Ajim Quevedo Renata Pacheco Thong Nguyen-Huy Pham Quoc Bao Ismail ElKhrachy Antonietta Varasano |
| author_facet | Sliman Hitouri Mohajane Meriame Ali Sk Ajim Quevedo Renata Pacheco Thong Nguyen-Huy Pham Quoc Bao Ismail ElKhrachy Antonietta Varasano |
| author_sort | Sliman Hitouri |
| collection | DOAJ |
| description | Gully erosion is one of the main natural hazards, especially in arid and semi-arid regions, destroying ecosystem service and human well-being. Thus, gully erosion susceptibility maps (GESM) are urgently needed for identifying priority areas on which appropriate measurements should be considered. Here, we proposed four new hybrid Machine learning models, namely weight of evidence -Multilayer Perceptron (MLP- WoE), weight of evidence –K Nearest neighbours (KNN- WoE), weight of evidence - Logistic regression (LR- WoE), and weight of evidence - Random Forest (RF- WoE), for mapping gully erosion exploring the opportunities of GIS tools and Remote sensing techniques in the El Ouaar watershed located in the Souss plain in Morocco. Inputs of the developed models are composed of the dependent (i.e., gully erosion points) and a set of independent variables. In this study, a total of 314 gully erosion points were randomly split into 70% for the training stage (220 gullies) and 30% for the validation stage (94 gullies) sets were identified in the study area. 12 conditioning variables including elevation, slope, plane curvature, rainfall, distance to road, distance to stream, distance to fault, TWI, lithology, NDVI, and LU/LC were used based on their importance for gully erosion susceptibility mapping. We evaluate the performance of the above models based on the following statistical metrics: Accuracy, precision, and Area under curve (AUC) values of receiver operating characteristics (ROC). The results indicate the RF- WoE model showed good accuracy with (AUC = 0.8), followed by KNN-WoE (AUC = 0.796), then MLP-WoE (AUC = 0.729) and LR-WoE (AUC = 0.655), respectively. Gully erosion susceptibility maps provide information and valuable tool for decision-makers and planners to identify areas where urgent and appropriate interventions should be applied. |
| first_indexed | 2024-04-24T14:29:03Z |
| format | Article |
| id | doaj.art-80b614db821447ffb572229ff4240c5e |
| institution | Directory Open Access Journal |
| issn | 2095-6339 |
| language | English |
| last_indexed | 2024-04-24T14:29:03Z |
| publishDate | 2024-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | International Soil and Water Conservation Research |
| spelling | doaj.art-80b614db821447ffb572229ff4240c5e2024-04-03T04:26:28ZengKeAi Communications Co., Ltd.International Soil and Water Conservation Research2095-63392024-06-01122279297Gully erosion mapping susceptibility in a Mediterranean environment: A hybrid decision-making modelSliman Hitouri0Mohajane Meriame1Ali Sk Ajim2Quevedo Renata Pacheco3Thong Nguyen-Huy4Pham Quoc Bao5Ismail ElKhrachy6Antonietta Varasano7Geosciences Laboratory, Department of Geology, Faculty of Sciences, University Ibn Tofail, Kenitra, 14000, MoroccoConstruction Technologies Institute, National Research Council of Italy, Polo Tecnologico di San Giovanni a Teduccio, 80146, Napoli, Italy; Corresponding author.Department of Geography, Faculty of Science, Aligarh Muslim University (AMU), Aligarh, UP, 202002, IndiaEarth Observation and Geoinformatics Division, National Institute for Space Research (INPE), Sao Jose dos Campos, Sao Paulo, 12227010, BrazilCentre for Applied Climate Sciences, University of Southern Queensland, Toowoomba, 4350, QLD, AustraliaFaculty of Natural Sciences, Institute of Earth Sciences, University of Silesia in Katowice, Będzińska street 60, 41-200, Sosnowiec, PolandCollege of Engineering, Civil Engineering Department, Najran University, Najran, 66291, Saudi ArabiaITC-CNR, Construction Technologies Institute, National Research Council, 70124, Bari, ItalyGully erosion is one of the main natural hazards, especially in arid and semi-arid regions, destroying ecosystem service and human well-being. Thus, gully erosion susceptibility maps (GESM) are urgently needed for identifying priority areas on which appropriate measurements should be considered. Here, we proposed four new hybrid Machine learning models, namely weight of evidence -Multilayer Perceptron (MLP- WoE), weight of evidence –K Nearest neighbours (KNN- WoE), weight of evidence - Logistic regression (LR- WoE), and weight of evidence - Random Forest (RF- WoE), for mapping gully erosion exploring the opportunities of GIS tools and Remote sensing techniques in the El Ouaar watershed located in the Souss plain in Morocco. Inputs of the developed models are composed of the dependent (i.e., gully erosion points) and a set of independent variables. In this study, a total of 314 gully erosion points were randomly split into 70% for the training stage (220 gullies) and 30% for the validation stage (94 gullies) sets were identified in the study area. 12 conditioning variables including elevation, slope, plane curvature, rainfall, distance to road, distance to stream, distance to fault, TWI, lithology, NDVI, and LU/LC were used based on their importance for gully erosion susceptibility mapping. We evaluate the performance of the above models based on the following statistical metrics: Accuracy, precision, and Area under curve (AUC) values of receiver operating characteristics (ROC). The results indicate the RF- WoE model showed good accuracy with (AUC = 0.8), followed by KNN-WoE (AUC = 0.796), then MLP-WoE (AUC = 0.729) and LR-WoE (AUC = 0.655), respectively. Gully erosion susceptibility maps provide information and valuable tool for decision-makers and planners to identify areas where urgent and appropriate interventions should be applied.http://www.sciencedirect.com/science/article/pii/S2095633923000898 |
| spellingShingle | Sliman Hitouri Mohajane Meriame Ali Sk Ajim Quevedo Renata Pacheco Thong Nguyen-Huy Pham Quoc Bao Ismail ElKhrachy Antonietta Varasano Gully erosion mapping susceptibility in a Mediterranean environment: A hybrid decision-making model International Soil and Water Conservation Research |
| title | Gully erosion mapping susceptibility in a Mediterranean environment: A hybrid decision-making model |
| title_full | Gully erosion mapping susceptibility in a Mediterranean environment: A hybrid decision-making model |
| title_fullStr | Gully erosion mapping susceptibility in a Mediterranean environment: A hybrid decision-making model |
| title_full_unstemmed | Gully erosion mapping susceptibility in a Mediterranean environment: A hybrid decision-making model |
| title_short | Gully erosion mapping susceptibility in a Mediterranean environment: A hybrid decision-making model |
| title_sort | gully erosion mapping susceptibility in a mediterranean environment a hybrid decision making model |
| url | http://www.sciencedirect.com/science/article/pii/S2095633923000898 |
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