Submission Deadline: 31 August 2026 View: 89 Submit to Special Issue
Dr. Rana Muhammad Adnan
Email: rana@gzhu.edu.cn
Affiliation: College of Chemical and Environmental Engineering, Water Science and Environmental Engineering Research Center, Shenzhen University, Shenzhen, 518060, China
Research Interests: artificial intelligence, time series analysis, novel meta-heuristic approaches applications, rainfall–runoff relationship, hydro-meteorological droughts, groundwater, water quality parameters modeling, trend analysis, clustering, watershed planning and management
Prof. Dr. Ozgur Kisi
Email: ozgur.kisi@th-luebeck.de
Affiliation: Department of Civil Engineering, Technical University of Lübeck, 23562 Lübeck, Germany
Research Interests: developing novel algorithms and methods towards the innovative solution of hydrologic forecasting and modeling, suspended sediment modeling, forecasting, estimating, spatial and temporal analysis of hydro-climatic variables
Dr. Jing-Cheng Han
Email: hanjc@szu.edu.cn
Affiliation: College of Chemical and Environmental Engineering, Water Science and Environmental Engineering Research Center, Shenzhen University, Shenzhen, 518060, China
Research Interests: artificial intelligence, hydrological modeling, ecohydrology, hydroinformatics
Dr. Mo Wang
Email: saupwangmo@gzhu.edu.cn
Affiliation: College of architecture and urban planning, Guangzhou University, Guangzhou, 510006, China
Research Interests: stormwater management, nature-based solution, landscape hydrology, urban planning, landscape architecture, artificial intelligence and optimization algorithms
Rapid urbanization, climate variability, and increasing water demand are intensifying hydrological and water-quality challenges worldwide. Traditional process-based models often struggle to capture the nonlinear and uncertain behavior of complex environmental systems. Recent advances in artificial intelligence (AI), hybrid deep learning, and digital twin technologies offer new opportunities for dynamic, data-driven understanding and decision support in urban and regional water systems. Explainable AI (XAI) and uncertainty quantification methods are particularly vital to enhance model transparency, interpretability, and reliability for sustainable water resource management.
This Special Issue aims to highlight recent progress and emerging trends in applying AI-driven hybrid modeling, digital twins, and explainable frameworks to hydrological and environmental systems. It seeks contributions that integrate decomposition algorithms, feature selection, deep learning, Bayesian inference, and uncertainty analysis to improve the accuracy and interpretability of water-related predictions.
This special issue specifically encourages high-quality submissions about advanced AI in hydrology. The topics of interest include, but are not limited to, the following:
· Hybrid decomposition–deep learning models for streamflow, flood, and water-quality forecasting
· Explainable AI and uncertainty quantification in hydrology
· Digital twins for real-time monitoring and risk management
· Bayesian networks and probabilistic modeling for environmental decision support
· Data fusion, remote sensing, and AI for urban–regional water systems
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