A systematic review of artificial intelligence approaches for sedimentation modeling in hydrological systems
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Soil erosion and sedimentation are important environmental challenges, particularly within data-scarce and climate-sensitive regions. Accurate sediment yield prediction is essential for mitigating reservoir siltation and supporting sustainable land and water management. This study systematically reviews and synthesizes recent applications of Artificial Intelligence (AI) in sedimentation modeling, clearly outlining its objectives, screening scope, key findings, and novelty. From 277 screened publications, 16 studies were selected following the PRISMA 2020 guidelines. The results show that hybrid AI models, which integrate physical process-based equations with data-driven algorithms (ANN and LSTM), consistently outperform traditional and standalone machine learning approaches, improving R² by up to 15% and reducing the RMSE by 25-40%. The integration of Google Earth Engine (GEE) enables multi-source remote sensing, climatic, and hydrological data fusion, enhancing spatiotemporal sediment monitoring and reproducibility. Despite these advances, challenges remain regarding data quality, computational cost, and model transferability. This review contributes a standardized and reproducible framework to assess AI-based sedimentation models and to outline future research directions towards scalable, interpretable, and climate-resilient sediment management.
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