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Predictive machine learning for gully susceptibility modeling with geo-environmental covariates: main drivers, model performance, and computational efficiency

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  • Kwanele Phinzi

    (University of Zululand)

  • Szilárd Szabó

    (University of Debrecen)

Abstract

Currently, machine learning (ML) based gully susceptibility prediction is a rapidly expanding research area. However, when assessing the predictive performance of ML models, previous research frequently overlooked the critical component of computational efficiency in favor of accuracy. This study aimed to evaluate and compare the predictive performance of six commonly used algorithms in gully susceptibility modeling. Artificial neural networks (ANN), partial least squares, regularized discriminant analysis, random forest (RF), stochastic gradient boosting, and support vector machine (SVM) were applied. The comparison was conducted under three scenarios of input feature set sizes: small (six features), medium (twelve features), and large (sixteen features). Results indicated that SVM was the most efficient algorithm with a medium-sized feature set, outperforming other algorithms across all overall accuracy (OA) metrics (OA = 0.898, F1-score = 0.897) and required a relatively short computation time (

Suggested Citation

  • Kwanele Phinzi & Szilárd Szabó, 2024. "Predictive machine learning for gully susceptibility modeling with geo-environmental covariates: main drivers, model performance, and computational efficiency," 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. 120(8), pages 7211-7244, June.
    • Handle: RePEc:spr:nathaz:v:120:y:2024:i:8:d:10.1007_s11069-024-06481-9
    DOI: 10.1007/s11069-024-06481-9
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