A Review of the Advances and Emerging Approaches in Hydrological Forecasting: From Traditional to AI-Powered Models

Abstract

Hydrological forecasting has evolved rapidly in response to intensifying climate variability, increasing data availability, and advances in computational modeling. This review synthesizes developments from 2006 to 2025, examining four major forecasting domains: statistical approaches, physically based models, data-driven machine learning and deep learning techniques, and hybrid or emerging physics–AI frameworks. Recent literature shows a decisive shift toward integrated, data-rich systems that leverage remote sensing, IoT networks, and artificial intelligence to overcome limitations in traditional forecasting. While hybrid and physics-informed AI models achieve notable improvements in accuracy, lead time, and scalability, persistent challenges remain, especially regarding data scarcity, model interpretability, cross-basin generalization, climate non-stationarity, and operational computational demands. This review highlights these limitations and outlines future directions needed to strengthen hydrological forecasting as a tool for climate adaptation, early warning systems, and long-term water resource planning. By consolidating methodological advances and emerging gaps, the study provides insights into how hydrological forecasting can transition toward more resilient, transparent, and decision-oriented frameworks.