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Spatial Prediction and Mapping of Gully Erosion Susceptibility Using Machine Learning Techniques in a Degraded Semi-Arid Region of Kenya

Author

Listed:
  • Kennedy Were

    (Kenya Agricultural and Livestock Research Organization, Kenya Soil Survey, P.O. Box 14733, Nairobi 00800, Kenya)

  • Syphyline Kebeney

    (School of Agriculture and Biotechnology, University of Eldoret, P.O. Box 1125, Eldoret 30100, Kenya)

  • Harrison Churu

    (School of Agriculture and Biotechnology, University of Eldoret, P.O. Box 1125, Eldoret 30100, Kenya)

  • James Mumo Mutio

    (School of Agriculture and Biotechnology, University of Eldoret, P.O. Box 1125, Eldoret 30100, Kenya)

  • Ruth Njoroge

    (School of Agriculture and Biotechnology, University of Eldoret, P.O. Box 1125, Eldoret 30100, Kenya)

  • Denis Mugaa

    (School of Agriculture and Biotechnology, University of Eldoret, P.O. Box 1125, Eldoret 30100, Kenya)

  • Boniface Alkamoi

    (School of Agriculture and Biotechnology, University of Eldoret, P.O. Box 1125, Eldoret 30100, Kenya)

  • Wilson Ng’etich

    (School of Agriculture and Biotechnology, University of Eldoret, P.O. Box 1125, Eldoret 30100, Kenya)

  • Bal Ram Singh

    (Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, 1432 Ås, Norway)

Abstract

This study aimed at (i) developing, evaluating and comparing the performance of support vector machines (SVM), boosted regression trees (BRT), random forest (RF) and logistic regression (LR) models in mapping gully erosion susceptibility, and (ii) determining the important gully erosion conditioning factors (GECFs) in a Kenyan semi-arid landscape. A total of 431 geo-referenced gully erosion points were gathered through a field survey and visual interpretation of high-resolution satellite imagery on Google Earth, while 24 raster-based GECFs were retrieved from the existing geodatabases for spatial modeling and prediction. The resultant models exhibited excellent performance, although the machine learners outperformed the benchmark LR technique. Specifically, the RF and BRT models returned the highest area under the receiver operating characteristic curve (AUC = 0.89 each) and overall accuracy (OA = 80.2%; 79.7%, respectively), followed by the SVM and LR models (AUC = 0.86; 0.85 & OA = 79.1%; 79.6%, respectively). In addition, the importance of the GECFs varied among the models. The best-performing RF model ranked the distance to a stream, drainage density and valley depth as the three most important GECFs in the region. The output gully erosion susceptibility maps can support the efficient allocation of resources for sustainable land management in the area.

Suggested Citation

  • Kennedy Were & Syphyline Kebeney & Harrison Churu & James Mumo Mutio & Ruth Njoroge & Denis Mugaa & Boniface Alkamoi & Wilson Ng’etich & Bal Ram Singh, 2023. "Spatial Prediction and Mapping of Gully Erosion Susceptibility Using Machine Learning Techniques in a Degraded Semi-Arid Region of Kenya," Land, MDPI, vol. 12(4), pages 1-19, April.
  • Handle: RePEc:gam:jlands:v:12:y:2023:i:4:p:890-:d:1124317
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    References listed on IDEAS

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    1. Omid Rahmati & Ali Haghizadeh & Hamid Reza Pourghasemi & Farhad Noormohamadi, 2016. "Gully erosion susceptibility mapping: the role of GIS-based bivariate statistical models and their comparison," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 82(2), pages 1231-1258, June.
    2. Hamed Ahmadpour & Ommolbanin Bazrafshan & Elham Rafiei-Sardooi & Hossein Zamani & Thomas Panagopoulos, 2021. "Gully Erosion Susceptibility Assessment in the Kondoran Watershed Using Machine Learning Algorithms and the Boruta Feature Selection," Sustainability, MDPI, vol. 13(18), pages 1-24, September.
    3. Mareike Ließ & Johannes Schmidt & Bruno Glaser, 2016. "Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-22, April.
    4. Massimo Conforti & Pietro Aucelli & Gaetano Robustelli & Fabio Scarciglia, 2011. "Geomorphology and GIS analysis for mapping gully erosion susceptibility in the Turbolo stream catchment (Northern Calabria, Italy)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 56(3), pages 881-898, March.
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    Cited by:

    1. Jorge da Paixão Marques Filho & Antônio José Teixeira Guerra & Carla Bernadete Madureira Cruz & Maria do Carmo Oliveira Jorge & Colin A. Booth, 2024. "Machine Learning Models for the Spatial Prediction of Gully Erosion Susceptibility in the Piraí Drainage Basin, Paraíba Do Sul Middle Valley, Southeast Brazil," Land, MDPI, vol. 13(10), pages 1-21, October.

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