Hydroinformatics has emerged over the last decades to become a recognised and established field of independent research within the hydrological sciences. It is concerned with the development and application of mathematical modelling, information technology, systems science and computational intelligence tools in hydrology. Hydroinformatics nowadays also deals with collecting, handling, analysing and visualising Big Data sourced from remote sensing, Internet of Things (IoT), earth and climate models, and defining tools and technologies for smart water management solutions.
This session aims to provide an active forum in which to demonstrate and discuss the integration and appropriate application of emergent techniques and technologies in water-related contexts.
Topics addressed in the session include:
* Predictive and exploratory models based on the methods of statistics, computational intelligence, machine learning and data science: neural networks, fuzzy systems, genetic programming, cellular automata, chaos theory, etc.
* Methods for analysing Big Data and complex datasets (remote sensing, IoT, earth system models, climate models): principal and independent component analysis, time series analysis, clustering, information theory, etc.
* Optimisation methods associated with heuristic search procedures (various types of genetic and evolutionary algorithms, randomised and adaptive search, etc.) and their application to hydrology and water resources systems
* Multi-model approaches and hybrid modelling approaches that blend process-based (mechanistic) and data-driven (machine learning) models
* Data assimilation, model reduction in integrated modelling, and High-Performance Computing (HPC) in water modelling
* Novel methods for analysing and quantifying model uncertainty and sensitivity
* Smart water data models and software architectures for linking different types of models and data sources
* IoT and Smart Water Management solutions
* Digital Twins for hydrology and water resources
Applications could belong to any area of hydrology or water resources, such as rainfall-runoff modelling, hydrometeorological forecasting, sedimentation modelling, analysis of meteorological and hydrologic datasets, linkages between numerical weather prediction and hydrologic models, model calibration, model uncertainty, optimisation of water resources, smart water management.
This session aims to provide an active forum in which to demonstrate and discuss the integration and appropriate application of emergent techniques and technologies in water-related contexts.
Topics addressed in the session include:
* Predictive and exploratory models based on the methods of statistics, computational intelligence, machine learning and data science: neural networks, fuzzy systems, genetic programming, cellular automata, chaos theory, etc.
* Methods for analysing Big Data and complex datasets (remote sensing, IoT, earth system models, climate models): principal and independent component analysis, time series analysis, clustering, information theory, etc.
* Optimisation methods associated with heuristic search procedures (various types of genetic and evolutionary algorithms, randomised and adaptive search, etc.) and their application to hydrology and water resources systems
* Multi-model approaches and hybrid modelling approaches that blend process-based (mechanistic) and data-driven (machine learning) models
* Data assimilation, model reduction in integrated modelling, and High-Performance Computing (HPC) in water modelling
* Novel methods for analysing and quantifying model uncertainty and sensitivity
* Smart water data models and software architectures for linking different types of models and data sources
* IoT and Smart Water Management solutions
* Digital Twins for hydrology and water resources
Applications could belong to any area of hydrology or water resources, such as rainfall-runoff modelling, hydrometeorological forecasting, sedimentation modelling, analysis of meteorological and hydrologic datasets, linkages between numerical weather prediction and hydrologic models, model calibration, model uncertainty, optimisation of water resources, smart water management.