Arup

There are strengths and weaknesses to both mathematical models and machine learning approaches, for instance mathematical models may be difficult to fully specify or become intractable when representing complex natural or built environments whilst machine learning models can be inscrutable (“black box”) and perform poorly when driven outside of the range of data they have been trained on. At the same time measured data from sensors is becoming increasing available.

We have been working to try and bring the best of both worlds together and we would like to discuss our work and the challenges it presents. Such challenges include model simplification or reduction, model performance in previously unobserved extreme conditions, quantification of uncertainty and techniques to parameterise mathematical models from data.

This challenge relates to problems (of a mathematical nature) in generating optimal solutions for natural flood management.  Natural flood management involves large numbers of small scale interventions in a much larger context through exploiting natural features in place of, for example, large civil engineering construction works. There is an optimisation problem related to the catchment hydrology and present methods use several unsatisfactory simplifications and assumptions that we would like to improve on.