https://research-information.bris.ac.uk/files/178389620/1_s2.0_S0043135418310091_main.pdf

Recent insights on uncertainties present in integrated catchment water quality modelling

. In many countries, urban flooding due to local,
intense rainfall is expected to become more frequent because of climate
change and urbanization. Cities trying to adapt to this growing risk are
challenged by a chronic lack of surface flooding data that are needed for
flood risk assessment and planning. In this work, we propose a new approach
that exploits existing surveillance camera systems to provide qualitative
flood level trend information at scale. The approach uses a deep
convolutional neural network (DCNN) to detect floodwater in surveillance
footage and a novel qualitative flood index (namely, the static observer flooding index – SOFI) as a proxy for water
level fluctuations visible from a surveillance camera's viewpoint. To
demonstrate the approach, we trained the DCNN on 1218 flooding images
collected from the Internet and applied it to six surveillance videos
representing different flooding and lighting conditions. The SOFI signal
obtained from the videos had a 75 % correlation to the actual
water level fluctuation on average. By retraining the DCNN with a few frames from a
given video, the correlation is increased to 85 % on average. The results
confirm that the approach is versatile, with the potential to be applied to
a variety of surveillance camera models and flooding situations without the
need for on-site camera calibration. Thanks to this flexibility, this
approach could be a cheap and highly scalable alternative to conventional
sensing methods.

/pdf/scalable-flood-level-trend-monitoring-with-surveillance-h6dtf6agev.pdf

Scalable flood level trend monitoring with surveillance cameras using a deep convolutional neural network

Towards a smart water city: A comprehensive review of applications, data requirements, and communication technologies for integrated management

Future trajectories of urban drainage systems: A simple exploratory modeling approach for assessing socio-technical transitions

Lost in calibration: why people still do not calibrate their models, and why they still should – a case study from urban drainage modelling

On the sensitivity of geospatial low impact development locations to the centralized sewer network

Urban water management will face various challenges in the future. Growing population in cities, changing climatic conditions and uncertain availability of water resources necessitate forward-looking water policy strategies. In this paper, we introduce a new water balance model to evaluate urban water strategies at a city scale. The aim is to evaluate decentralised water management measures within a large-scale investigation and to reduce external potable water demand. The upscaling process of local information (water demand, areal data) to a conceptual model approach is described. The modelling approach requires simplification of detailed processes to enable the execution with limited computing capacity. The model was applied to Greater Metropolitan Melbourne, Australia, a highly sprawled city with nearly four million inhabitants. Scenario analysis demonstrated the impact of using different water resources of different quality classes, the extensive implementation of water saving appliances and decentralised water storage strategies on the city’s water balance. Results indicate a potential reduction of potable water demand of up to 25% with a conservative rainwater reuse and, even 60% with widespread implementation of rain- and greywater recycling. Furthermore, we demonstrate that even small systems implemented at a local level can have noticeable effects when operated as clustered schemes.

/pdf/conceptual-urban-water-balance-model-for-water-policy-4dvroldw0w.pdf

Peter Zeisl

Papers

Lost in calibration: why people still do not calibrate their models, and why they still should – a case study from urban drainage modelling

On the sensitivity of geospatial low impact development locations to the centralized sewer network

Conceptual Urban Water Balance Model for Water Policy Testing: An Approach for Large Scale Investigation