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May 25, 2026CATENA1 citationsOpen Access

Gully erosion studies using artificial intelligence approaches: A systematic review

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OUObinna Uzodimma UbaniENEsdras NgezahayoIJIan Jefferson

Key Points

  • The aim is to evaluate AI models used in gully erosion studies and identify key conditioning factors.
  • Conducted a systematic review of existing literature on gully erosion and AI applications.
  • Developed frameworks for accuracy metrics and semantic segmentation relevant to gully erosion.
  • Analyzed the influence of climate and soil type on the conditioning factors for gully erosion.
  • Identified the best performing AI models for predicting gully erosion outcomes.
  • Established a structured framework for evaluating the accuracy of AI applications in this context.
  • Highlighted the significant role of climate and soil type as conditioning factors influencing gully erosion.

Abstract

• Data-backed identification of the best performing AI models in gully erosion studies • Development of framework for accuracy metrics • Development of hierarchical framework for semantic-segmentation • Identification of the most important conditioning factors in gully erosion studies • Influence of climate and soil type on gully erosion conditioning factors

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Cite This Study

Ubani et al. (2026) studied this question.

synapsesocial.com/papers/6a13e8680e02ee3982d332fchttps://doi.org/10.1016/j.catena.2026.110249
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