Performance Assessment of Individual and Ensemble Learning Models for Gully Erosion Susceptibility Mapping in a Mountainous and Semi-Arid Region

Meryem El Bouzekraoui, Abdenbi Elaloui, Samira Krimissa, Kamal Abdelrahman, Ali Y. Kahal, Sonia Hajji, Maryem Ismaili (), Biraj Kanti Mondal and Mustapha Namous
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Meryem El Bouzekraoui: Data Science for Sustainable Earth Laboratory (Data 4 Sustainable Earth), Sultan Moulay Slimane University, Beni Mellal 23000, Morocco
Abdenbi Elaloui: Data Science for Sustainable Earth Laboratory (Data 4 Sustainable Earth), Sultan Moulay Slimane University, Beni Mellal 23000, Morocco
Samira Krimissa: Data Science for Sustainable Earth Laboratory (Data 4 Sustainable Earth), Sultan Moulay Slimane University, Beni Mellal 23000, Morocco
Kamal Abdelrahman: Department of Geology and Geophysics, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
Ali Y. Kahal: Department of Geology and Geophysics, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
Sonia Hajji: Data Science for Sustainable Earth Laboratory (Data 4 Sustainable Earth), Sultan Moulay Slimane University, Beni Mellal 23000, Morocco
Maryem Ismaili: Data Science for Sustainable Earth Laboratory (Data 4 Sustainable Earth), Sultan Moulay Slimane University, Beni Mellal 23000, Morocco
Biraj Kanti Mondal: Department of Geography, School of Sciences, Netaji Subhas Open University, Kolkata 700020, India
Mustapha Namous: Data Science for Sustainable Earth Laboratory (Data 4 Sustainable Earth), Sultan Moulay Slimane University, Beni Mellal 23000, Morocco

Land, 2024, vol. 13, issue 12, 1-30

Abstract: High-accuracy gully erosion susceptibility maps play a crucial role in erosion vulnerability assessment and risk management. The principal purpose of the present research is to evaluate the predictive power of individual machine learning models such as random forest (RF), decision tree (DT), and support vector machine (SVM), and ensemble machine learning approaches such as stacking, voting, bagging, and boosting with k-fold cross validation resampling techniques for modeling gully erosion susceptibility in the Oued El Abid watershed in the Moroccan High Atlas. A dataset comprising 200 gully points, identified through field observations and high-resolution Google Earth imagery, was used, alongside 21 gully erosion conditioning factors selected based on their importance, information gain, and multi-collinearity analysis. The exploratory results indicate that all derived gully erosion susceptibility maps had a good accuracy for both individual and ensemble models. Based on the receiver operating characteristic (ROC), the RF and the SVM models had better predictive performances, with AUC = 0.82, than the DT model. However, ensemble models significantly outperformed individual models. Among the ensembles, the RF-DT-SVM stacking model achieved the highest predictive accuracy, with an AUC value of 0.86, highlighting its robustness and superior predictive capability. The prioritization results also confirmed the RF-DT-SVM ensemble model as the best. These findings highlight the superiority of ensemble learning models over individual ones and underscore their potential for application in similar geo-environmental contexts.

Keywords: gully erosion modeling; susceptibility mapping; machine learning; ensemble learning; bagging; stacking; Oued El Abid basin (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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