In this Hydroinformatics session, we explore innovative and state-of-the-art computational and data-driven methods that transform modern Earth observations, climate data, and IoT streams into actionable water management solutions. We focus on novel modeling frameworks, machine learning, and optimization techniques that advance our ability to simulate, forecast, and manage water resource systems. This session offers a forum for showcasing and debating the integration of emerging techniques and technologies across water-related applications, from novel algorithms to real-world deployment.
Topics addressed in the session include:
• Methods for analysing Big Data and complex datasets (remote sensing, IoT, Earth system and climate models): representation learning, time series analysis, clustering, information theory
• Predictive and exploratory models drawing on statistics, computational intelligence, machine learning, and data science
• Multi-model and hybrid approaches blending process-based (mechanistic) and data-driven (machine learning) models, including data assimilation and methods for quantifying and propagating model uncertainty
• Optimisation methods based on heuristic search (genetic and evolutionary algorithms, randomised and adaptive search) applied to hydrology and water resources systems
• Scalable and efficient computational methods and software tools for hydrological modelling and analysis, including model reduction, graph- and network-based approaches, and High-Performance Computing (HPC)
• Decision-support systems for water management, with particular interest in those including digital twins and integrated tools for monitoring, forecasting and control, also focusing on how agentic AI can enhance their interactivity and capabilities
Applications span the full breadth of hydrology and water resources — rainfall-runoff modelling, hydrometeorological forecasting, groundwater level modelling, sedimentation modelling, analysis of meteorological and hydrologic datasets, coupling of numerical weather prediction with hydrologic models, model calibration and uncertainty, optimisation of water resources, and smart water management.