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Implementation of random forest, adaptive boosting, and gradient boosting decision trees algorithms for gully erosion susceptibility mapping using remote sensing and GIS

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Abstract

Gullying is one of the problems that cause soil degradation in semi-arid areas and should be predicted to mitigate its damaging effects. Three machine learning models have been employed in this work to map the susceptibility to gully erosion in the N'fis river basin in the Moroccan High Atlas. Utilizing high-resolution images from Google Earth alongside fieldwork data, we digitized 434 gully erosion events to construct the comprehensive inventory map. These data were divided into two groups: training (70%) and test (30%). Based on the literature research and the multicollinearity test, 11 conditioning factors were selected. The receiver operating characteristic (ROC) approach and other statistical measures were used to quantify the model's accuracy. The study findings highlight the significance of drainage density, slope, NDVI, and distance from roads as crucial factors influencing gully erosion in the study area. Among the evaluated machine learning algorithms, the random forest (RF) model exhibited the highest performance, with an area under the curve (AUC) value of 0.932. It was followed by adaptive boosting (AB) with an AUC of 0.902 and gradient-boosted decision trees (GBDT) with an AUC of 0.893. The maps produced reveal that the southern and central regions of the study area have the classes of very high and high gully erosion susceptibility. The outputs of the current study can be used by decision-makers to improve prevention planning and mitigation techniques against gully erosion damage.

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Availability of data

The data that support the findings of this study are available on request from the corresponding author.

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Acknowledgements

The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia, for funding this research. (IFKSURC-1-7316).

Funding

This research was funded by the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia, project no. (IFKSURC-1-7316).

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Authors and Affiliations

  1. Laboratory of Georesources, Geoenvironment and Civil Engineering (L3G), Faculty of Sciences and Techniques, Cadi Ayyad University, Marrakesh, Morocco

    Hassan Ait Naceur & Brahim Igmoullan

  2. Abdullah Alrushaid Chair for Earth Science Remote Sensing Research, Geology and Geophysics Department, King Saud University, Riyadh, 11451, Saudi Arabia

    Hassan Ait Naceur, Hazem Ghassan Abdo & Fahad Alshehri

  3. Data Science for Sustainable Earth Laboratory (Data4Earth), Sultan Moulay Slimane University, Beni Mellal, Beni Mellal-Khenifra, Morocco

    Mustapha Namous

  4. School of Geography and the Environment, University of Oxford, Oxford, UK

    Jasem A Albanai

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  1. Hassan Ait Naceur
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  3. Brahim Igmoullan
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  4. Mustapha Namous
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  6. Jasem A Albanai
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Contributions

HAN, HGA, II and MN: Methodology, HAN, HGA, II and MN: Software, II, HAN, MN and FA: Formal analysis and investigation. MN, II, HAN and HGA: visualization, HAN, MN, HGA and FA: Writing—original draft preparation, HGA, HAN, II, MN, JA and FA: Writing—review and editing, HGA, MN, II, JA and FA: Supervision. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Hazem Ghassan Abdo.

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Ait Naceur, H., Abdo, H.G., Igmoullan, B. et al. Implementation of random forest, adaptive boosting, and gradient boosting decision trees algorithms for gully erosion susceptibility mapping using remote sensing and GIS. Environ Earth Sci 83, 121 (2024). https://doi.org/10.1007/s12665-024-11424-5

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