Comparing Traditional Hydrological Models and Machine Learning Approaches for Gully Erosion Control: A Systematic Review

Abstract

Gully erosion remains a significant environmental challenge globally, causing severe land degradation, loss of agricultural productivity, and damage to infrastructure, particularly in regions with insufficient erosion control measures. This systematic review compares traditional hydrological models (such as the Universal Soil Loss Equation , Revised Universal Soil Loss Equation , and the Rational Method) with machine learning (ML) approaches, including Support Vector Regression , Random Forest, and XGBoost, for predicting and controlling gully erosion. While traditional models provide useful baseline predictions, their empirical nature and inability to handle complex, non-linear processes limit their application in dynamic, real-world environments. In contrast, ML models, leveraging high-dimensional datasets such as remote sensing, GIS data, and climatic variables, offer superior flexibility and predictive power. This review emphasizes the benefits of hybrid models that combine traditional hydrological models with machine learning techniques, offering a comprehensive framework for gully erosion management. The review also discusses the limitations of ML models, including data quality and interpretability challenges, and provides recommendations for future research directions, including data collection, model transparency, and integration with local knowledge. Overall, the paper advocates for the adoption of ML-based approaches for sustainable land management and early-warning systems to mitigate the impact of gully erosion, particularly in data-limited regions. IIARD International Journal of Geography & Environmental Management

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