Machine learning and deep learning in river-basin modeling: A comprehensive review

Document Type

Article

Department

Faculty of Arts and Sciences

Abstract

This study highlights a key question about the capacity of Machine Learning (ML) and Deep Learning (DL) models as standalone, futuristic approaches for river basin modeling. To answer the main research question, a PRISMA-based systematic review framework was used. This study evaluates 133 peer-reviewed studies that used standalone ML/DL models or compared them with conventional hydrological and process-based models to predict various hydrological variables and basin dynamics. Findings synthesis indicates that even though the ML/DL models are better in terms of pattern recognition, data integration, and predictive capacity, especially in non-stationary and non-heterogeneous hydroclimatic conditions, their application alone is not yet as physiologically interpretable and generalized across basins. Overall, the reviewed studies show that hybrid and physics-informed ML/DL models are more effective than traditional empirical models and data-driven models. Nevertheless, there are still difficulties, such as the lack of data, the inequality of computations in different regions, and the uneven assessment of extreme and low-flow states. This study highlights the necessity of hybrid modeling approaches, explainable and physics-guided AI, open data ecosystems, and standardized benchmarking frameworks to enable reliable, transparent, and fair river basin management in changing climatic conditions.

Comments

Issue and pagination are not provided by the author/publisher.

Publication (Name of Journal)

Applied Soft Computing

DOI

doi.org/10.1016/j.asoc.2026.115295

Link to Full Text

Share

COinS