Hydroinformatics has emerged over the last decades to become a recognised and established field of independent research within the hydrological sciences. Hydroinformatics is concerned with data acquisition, development and hydrological application of mathematical modelling, information technology, systems science and computational intelligence tools. We also have to face the challenges of the so-called Big Data: large data sets, both in size and complexity. Methods and technologies for data handling, visualization and knowledge acquisition are often referred to as Data Science.
The aim of this session is to provide an active forum in which to demonstrate and discuss the integration and appropriate application of emergent computational technologies in a hydrological modelling context. Topics of interest are expected to cover a broad spectrum of theoretical and practical activities that would be of interest to hydro-scientists and water-engineers. We aim to address the following classes of methods and technologies:
* Predictive and analytical models based on the methods of statistics, computational intelligence, machine learning : neural networks (including deep learning), fuzzy systems, genetic programming, cellular automata, chaos theory, etc.
* Innovative sensing techniques: satellites, gauges and citizens (crowdsourcing)
* Methods for the analysis of complex data sets, including remote sensing data: principal and independent component analysis, time series analysis, information theory, etc.
* Specific concepts and methods of Big Data and Data Science
* Optimisation methods associated with heuristic search procedures: various types of evolutionary algorithms, randomised and adaptive search, etc.
* Applications of systems analysis and optimisation in water resources
* Hybrid modelling involving different types of models both process-based and data-driven, combination of models (multi-models), etc.
* Data assimilation and model reduction in integrated modelling
* Novel methods of analysing model uncertainty and sensitivity
* Software architectures for linking different types of models and data sources
Applications could belong to any area of hydrology or water resources: rainfall-runoff modelling, flow forecasting, sedimentation modelling, analysis of meteorological and hydrologic data sets, linkages between numerical weather prediction and hydrologic models, model calibration, model uncertainty, optimisation of water resources, etc.
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