Submission Deadline: 31 May 2026 View: 588 Submit to Special Issue
Prof. Dr. Upaka Rathnayake
Email: upaka.rathnayake@atu.ie
Affiliation: Department of Civil Engineering and Construction, Atlantic Technological University, Sligo, F91 YW50, Ireland
Research Interests: smart agriculture and irrigation, water management, artificial intelligence, explainability, climate change, sustainability
Dr. Komali Kantamaneni
Email: kkantamaneni@uclan.ac.uk
Affiliation: Faculty of Science and Technology, University of Central Lancashire, Preston, PR1 2HE, United Kingdom
Research Interests: sustainability, water management, coastal environment, climate change
Artificial intelligence (AI) is transforming hydraulic and hydrological modelling by enabling faster, more accurate, and adaptive decision-making in complex water systems. From real-time flood forecasting and drought prediction to optimizing reservoir operations and groundwater management, AI-powered technologies are bridging the gap between data abundance and actionable insights. Machine learning, deep learning, and hybrid physics–AI models are enhancing the representation of nonlinear processes, improving prediction skill under data scarcity, and supporting climate-resilient water management. This special issue invites original research and innovative applications that harness AI to advance modelling capabilities, integrate multi-source datasets, and deliver explainable, reliable, and scalable solutions for contemporary and future water challenges.
· AI-driven flood and drought forecasting – real-time prediction, early warning systems, and hazard mapping.
· Hybrid modelling approaches – integrating AI with physics-based hydraulic and hydrological models.
· Machine learning for parameter estimation – improving calibration, validation, and uncertainty quantification.
· Deep learning in spatio-temporal water data analysis – handling satellite, IoT, and sensor network datasets.
· AI for groundwater modelling – aquifer simulation, recharge estimation, and depletion forecasting.
· Explainable AI (XAI) in water resources – interpretable models for decision support and policy-making.
· AI-enhanced climate change impact assessments – modelling hydrological extremes under future scenarios.
· Data assimilation and gap-filling techniques – leveraging AI to handle missing or noisy hydrological datasets.
· Urban water systems modelling – AI applications in stormwater, wastewater, and smart city water management.
· AI for hydraulic structure optimization – dam operation, irrigation scheduling, and channel design.
· Uncertainty analysis using AI – probabilistic forecasts and risk-based decision-making.
· Edge AI and IoT integration – decentralized, low-latency modelling for water monitoring in remote areas.
· Ethical and practical considerations – data privacy, model bias, and sustainable AI adoption in water modelling.
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