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Gully Erosion Susceptibility Assessment in the Kondoran Watershed Using Machine Learning Algorithms and the Boruta Feature Selection

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  • Hamed Ahmadpour

    (Department of Natural Resources Engineering, Faculty of Agriculture and Natural Resources Engineering, University of Hormozgan, Bandar Abbas 7916193145, Iran)

  • Ommolbanin Bazrafshan

    (Department of Natural Resources Engineering, Faculty of Agriculture and Natural Resources Engineering, University of Hormozgan, Bandar Abbas 7916193145, Iran)

  • Elham Rafiei-Sardooi

    (Department of Ecological Engineering, Faculty of Natural Resources, University of Jiroft, Kerman 7867161167, Iran)

  • Hossein Zamani

    (Department of Mathematics and Statistics, Faculty of Science, University of Hormozgan, Bandar Abbas 7916193145, Iran)

  • Thomas Panagopoulos

    (Research Center for Spatial and Organizational Dynamics, University of Algarve, Gambelas Campus, 8005 Faro, Portugal)

Abstract

Gully erosion susceptibility mapping is an essential land management tool to reduce soil erosion damages. This study investigates gully susceptibility based on multiple diagnostic analysis, support vector machine and random forest algorithms, and also a combination of these models, namely the ensemble model. Thus, a gully susceptibility map in the Kondoran watershed of Iran was generated by applying these models on the occurrence and non-occurrence points (as the target variable) and several predictors (slope, aspect, elevation, topographic wetness index, drainage density, plan curvature, distance to streams, lithology, soil texture and land use). The Boruta algorithm was used to select the most effective variables in modeling gully erosion susceptibility. The area under the receiver operating characteristic curve (AUC), the receiver operating characteristics, and true skill statistics (TSS) were used to assess the model performance. The results indicated that the ensemble model had the best performance (AUC = 0.982, TSS = 0.93) compared to the others. The most effective factors in gully erosion susceptibility mapping of the study region were topological, anthropogenic, and geological. The methodology and variables of this study can be used in other regions to control and mitigate the gully erosion phenomenon by applying biophilic and regenerative techniques at the locations of the most influential factors.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:18:p:10110-:d:632254
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    References listed on IDEAS

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    1. Kursa, Miron B. & Rudnicki, Witold R., 2010. "Feature Selection with the Boruta Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i11).
    2. Álvaro Gómez-Gutiérrez & Christian Conoscenti & Silvia Angileri & Edoardo Rotigliano & Susanne Schnabel, 2015. "Using topographical attributes to evaluate gully erosion proneness (susceptibility) in two mediterranean basins: advantages and limitations," 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. 79(1), pages 291-314, November.
    3. 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.
    4. Vera Ferreira & André Samora-Arvela & Thomas Panagopoulos, 2016. "Soil erosion vulnerability under scenarios of climate land-use changes after the development of a large reservoir in a semi-arid area," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 59(7), pages 1238-1256, July.
    5. Rahman, Md. Rejaur & Shi, Z.H. & Chongfa, Cai, 2009. "Soil erosion hazard evaluation—An integrated use of remote sensing, GIS and statistical approaches with biophysical parameters towards management strategies," Ecological Modelling, Elsevier, vol. 220(13), pages 1724-1734.
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    1. 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.

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