Nick Mount

My research interest are focused primarily on fluvial systems, where I have adopted a trans-disciplinary approach to exploring and modelling hydrological and geomorphological processes and (increasingly) the impacts that these can have on humans.

Much of my work has been computational in nature and has reflected my particular interest in the application of methods from computer science to physical geography - especially the exploration of the potential of computationally-intelligent algorithms and machine learning techniques to provide 'data-driven' models of the fluvial system that can deliver improved forecasts for water resource and flood management. My goal in this work is to move beyond established approaches that have used data-driven modelling techniques to deliver improved, empirical forecasts of how a fluvial system will respond to changes in its hydrological or geomorphological inputs. Instead, my work pursues analytical methods that can help to reveal, characterise and interpret the internal behaviours of the data-driven models that are produced - in so doing providing important heuristic insights into the mechanistic functioning of the fluvial system.

My research also recognises the central role of humans in hydrological and fluvial systems and the need for modelling approaches that can represent the interplay between physical and social systems. This is particularly true in context of flood risk management, where a 'physical system' focus on mitigating flood sources and pathways must be accompanied by a 'social system' focus on reducing the vulnerability of receptors to flooding so that overall resilience can be improved. To this end, I am pursuing new approaches to local flood risk management that combine participatory approaches with system modelling tools to deliver conceptual flood risk models that can represent and integrate both physical and social system knowledge. These models can then be used to support the identification and assessment of a diverse array of local flood risk interventions can highlight the potential of adapting socio-economic behaviours in addition to the value of engineering the physical system.