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HS3.1

Hydroinformatics: computational intelligence, uncertainty, systems analysis, optimisation, data science, and data-driven modelling of social-hydrologic systems
Convener: Dimitri Solomatine  | Co-Conveners: Robert J. Abrahart , Amin Elshorbagy , Ghada El Serafy , Elena Toth , Matthias Cuntz , Juliane Mai , George P. Petropoulos , Dr. Prashant K. Srivastava 
Orals
 / Thu, 21 Apr, 13:30–17:00  / Room 2.20
Posters
 / Attendance Thu, 21 Apr, 17:30–19:00  / Hall A
Hydroinformatics has emerged over the last decade to become a recognised and established field of independent research within the hydrological sciences. Hydroinformatics is concerned with the development and hydrological application of mathematical modelling, information technology, systems science and computational intelligence tools. It provides the computer-based decision-support systems that are now entering more and more into the offices of consulting engineers, water authorities and government agencies.

More and more we have to face the challenges of Big Data, and employ the relevant techniques and tools. Methods and technologies for data handling, visualization and knowledge acquisition are more and more 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, taking into account the issues of various types of uncertainty. 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. The main topics will address the following classes of methods and technologies:

* Predictive and analytical models based on the methods of statistics, computational intelligence and data science: neural networks, fuzzy systems, support vector machines, genetic programming, cellular automata, chaos theory, etc.
* Methods for the analysis of complex data sets, including remote sensing data: principal and independent component analysis, feature extraction, time series analysis, data-infilling, information theory, etc.
* Specific concepts and methods of Big Data and Data Science such as data thinning, data fusion, information integration
* Optimisation methods associated with heuristic search procedures in multi-objective context: various types of genetic and evolutionary algorithms, randomised and adaptive search, ant colony, particle swarm optimisation, 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
* Appropriate 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.

Within the framework of the new scientific decade 2013-2022 of IAHS, 'Panta Rhei - Everything Flows', this session also explores how data-driven approaches may be used to help gain a better understanding of the complex feedbacks between hydrological and socio-economic systems. It will explicitly consider how we enhance hydrological systems modelling by applying data-mining and data-driven modelling approaches utilising information describing the role and influence of people as water users and decision-makers, and measurements and observations collected through citizen science and crowdsourcing approaches, also through the use of web-technologies for data collection and visualisation aimed at improving citizen engagement and public communication.