Gully erosion studies using artificial intelligence approaches: A systematic review

CATENA 2026 Open access

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Authors: Obinna Uzodimma Ubani, Esdras Ngezahayo, Ian Jefferson
DOI: 10.1016/j.catena.2026.110249
Cited by: 1
References: 190

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

Fields of Study & Subjects

Soil erosion and sediment transport Flood Risk Assessment and Management Groundwater and Watershed Analysis

Publication details

Journal / source
CATENA
Publisher
Elsevier BV
Volume
271
Pages
110249–110249
Publication date
23 May 2026
Language
EN