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Wastewater-based epidemiology (WBE) has been proven to be a useful tool in monitoring public health-related issues such as drug use, and disease. By sampling wastewater and applying WBE methods, wastewater-detectable pathogens such as viruses can be cheaply and effectively monitored, tracking people who might be missed or under-represented in traditional disease surveillance. There is a gap in current knowledge in combining hydraulic modeling with WBE. Recent literature has also identified a gap in combining machine learning with WBE for the detection of viral outbreaks. In this study, we loosely coupled a physically-based hydraulic model of pathogen introduction and transport with a machine learning model to track and trace the source of a pathogen within a sewer network and to evaluate its usefulness under various conditions. The methodology developed was applied to a hypothetical sewer network for the rapid detection of disease hotspots of the disease caused by the SARS-CoV-2 virus. Results showed that the machine learning model's ability to recognize hotspots is promising, but requires a high time-resolution of monitoring data and is highly sensitive to the sewer system's physical layout and properties such as flow velocity, the pathogen sampling procedure, and the model's boundary conditions. The methodology proposed and developed in this paper opens new possibilities for WBE, suggesting a rapid back-tracing of human-excreted biomarkers based on only sampling at the outlet or other key points, but would require high-frequency, contaminant-specific sensor systems that are not available currently., Sanitary Engineering, Hydraulic Structures and Flood Risk

The accuracy of two-dimensional hydrodynamic models (2D models) is improved when high-resolution Digital Elevation Models (DEMs) are used. However, the entailed high spatial discretisation results in excessive computational expenses, thus prohibiting their implementation in real-time forecasting especially at a large scale. This paper presents a sub-model approach that adapts 1D static models to tailor high-resolution 2D model grids relevant to specified targets, such that the tailor-made 2D hydrodynamic sub-models yield fast processing without significant loss of accuracy via a GIS-based multi-scale simulation framework. To validate the proposed approach, model experiments were first designed to separately test the impact of two outcomes (i.e., the reduced computational domains and the optimised boundary conditions) towards final 2D prediction results. Then, the robustness of the sub-model approach was evaluated by selecting four focus areas with distinct catchment terrain morphologies as well as distinct rainfall return periods of 1-100 years. The sub-model approach resulted in a 45-553 times faster processing with a 99% reduction in the number of computational cells for all four cases; the goodness of fit regarding predicted flood extents was above 0.88 of F-2, flood depths yield Root Mean Square Errors (RMSE) below 1.5 cm and the discrepancies of u- and v-directional velocities at selected points were less than 0.015 ms(-1). As such, this approach reduces the 2D models' computing expenses significantly, thus paving the way for large-scale high-resolution 2D real-time forecasting.

Many cities face issues with rain induced flooding and combined sewer overflows, which can be addressed by using hydrodynamic models. These models are often simplified in a real-time setting to make them faster, and their performance can be improved by using data assimilation. In this study we use the Ensemble Kalman Filter to update a simplified model of a small area of Copenhagen, Denmark. The model is evaluated using perfect rain data for one summer month in 2016, and flow forecasts are used to quantify the performance of the update. We found that the 1-60 minutes forecast can be improved by updating the model. Having a small noise on the rain gives slightly worse results on a short forecast horizon and slightly better forecasts on a longer horizon. The forecast performance is also dependent on which model parts are updated.

We have developed a setup that couples the urban development model DANCE4WATER with the 1D-2D hydraulic model MIKE FLOOD. The setup makes it possible to assess the impact of urban development and infrastructural change scenarios on flood risk in an automated manner. In addition, it permits us to use the results of the hydraulic simulation to condition DANCE4WATER and to account for flood risk in the simulated urban development. In an Australian case study, we demonstrate that future flood risk can be significantly reduced while maintaining the overall speed of urban development.