Special Issue "Machine Learning Applied to Hydraulic and Hydrological Modelling"
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The computational power available nowadays allow us to tackle simulation challenges in hydraulic and hydrological modelling at different scales that were impossible a few decades ago. However, even in the current situation, the time needed for these simulations is inadequate for many scientific and engineering applications, such as decision support systems, flood warning systems, design or optimization of hydraulic structures, calibration of model parameters, uncertainty quantification, real-time model-based control, etc.
To address these issues, the development of fast surrogate models to increase the simulation speed seems to be promising strategy: it does not require a huge investment in new hardware and software, and the same tools can be used to solve very different problems. The field of Machine Learning offers a huge library of methods to build surrogate models, many of which have been successfully used in hydraulic and hydrological modelling.
In this Special Issue we would like to invite research works which incorporate Machine Learning techniques in hydraulic and hydrological modelling, such as (but not restricted to):
Artificial Science, in which a relation between input and output is learned using only data, also known as data-driven methods.
Scientific Numerical Modelling, such as simplified numerical models, model calibration (system identification) or optimization, renormalized models, up (down)scaled models, coarse models, etc.
Emulation, where a fast emulator is developed based on training data derived by a slow simulator
Dr. Vasilis Bellos
Dr. Juan Pablo Carbajal