Calibration and sensitivity analysis of a novel water flow and pollution model for future city planning: Future Urban Stormwater Simulation (FUSS)

Abstract

Planning for future urban development and water infrastructure is uncertain due to changing human activities and climate. To quantify these changes, we need adaptable and fast models that can reliably explore scenarios without requiring extensive data and inputs. While such models have been recently considered for urban development, they are lacking for stormwater pollution assessment. This work proposes a novel Future Urban Stormwater Simulation (FUSS) model, utilizing previously developed urban planning algorithm (UrbanBEATS) to dynamically assess pollution changes in urban catchments. By using minimal input data and adding stochastic point-source pollution to the build-up/wash-off approach, this study highlights calibration and sensitivity analysis of flow and pollution modules, across the range of common stormwater pollutants. The results highlight excellent fit to measured values in a continuous rainfall simulation for the flow model, with one significant calibration parameter. The pollution model was more variable, with TSS, TP and Pb showing high model efficiency, while TN was predicted well only across event-based assessment. The work further explores the framework for the model application in future pollution assessment, and points to the future work aiming to developing land-use dependent model parameter sets, to achieve flexibility for model application across varied urban catchments.

Urban nature-based solutions planning for biodiversity outcomes: human, ecological, and artificial intelligence perspectives

“…Firstly, current modelling tools for urban planning are often too slow for real-time planning and adaptive changes, due to computationally and time restrictive process-based methods (Prodanovic et al, 2022b). AI approaches have already been utilised to speed up flood extent predictions with Convolutional Neural Networks (CNN) (Leitao et al, 2018) with new research showing promise in the application of predictive AI for fast water pollution modelling .…”

“…A further opportunity and complexity is the advancements in computer science, which have made planning of multilayered NBS systems significantly easier over the years (Kuller et al, 2019). Many urban planning models include NBS integration in the urban fabric (e.g., Kuller et al, 2019;Bach et al, 2020;Prodanovic et al, 2022b), but models are still limited by their users' initial boundary conditions, or model assumptions, and are often run for human-centric design. Even the most advanced models nowadays are not capable of incorporating all the human-centric considerations (e.g., history of the space, shifting community interests, etc., -Coyne et al, 2020;Naserisafavi et al, 2022), and ecological considerations are even less promoted.…”

A Low-Cost, Low-Power Water Velocity Sensor Utilizing Acoustic Doppler Measurement

“…The velocity of stormwater, wastewater, and surface water is an essential parameter that requires continuous monitoring to estimate the discharge volume and conduct further analyses, such as pollution load estimation [ 1 ], flow and water quality modelling [ 2 , 3 ], and normalisation of pathogen concentrations in wastewater [ 4 , 5 ]. Current sensors available on the market to measure water velocity in storm and waste water systems are expensive, are high-power, are not available freely as open-source or open-hardware, and/or require significant preparation and overhead to setup and maintain [ 6 , 7 , 8 ].…”

Incorporating water quality into land use scenario analysis with random forest models

“…The other focus area is to assess how different land uses and development scenarios influence water quality. Process-based hydrological models can dynamically assess pollutants in urban catchments with specific land uses in different weather conditions (Abdul-Aziz and Al-Amin, 2016; Prodanovic et al, 2022). However, intensive flow measurement and continuous water quality samplings are needed for model setup and calibration (Clark et al 2017).…”