Data-driven Modeling of Combined Sewer Systems for Urban Sustainability: An Empirical Evaluation

TL;DR

This paper evaluates deep learning models for predicting sewer system dynamics in urban areas, demonstrating their accuracy and resilience during network outages, which can improve urban sustainability and infrastructure management.

Contribution

It provides a comprehensive empirical comparison of state-of-the-art DL time series models for sewer system prediction using real data, highlighting their robustness during outages.

Findings

DL models accurately predict sewer load dynamics

Global models outperform local models during outages

DL models enhance urban infrastructure resilience

Abstract

Climate change poses complex challenges, with extreme weather events becoming increasingly frequent and difficult to model. Examples include the dynamics of Combined Sewer Systems (CSS). Overburdened CSS during heavy rainfall will overflow untreated wastewater into surface water bodies. Classical approaches to modeling the impact of extreme rainfall events rely on physical simulations, which are particularly challenging to create for large urban infrastructures. Deep Learning (DL) models offer a cost-effective alternative for modeling the complex dynamics of sewer systems. In this study, we present a comprehensive empirical evaluation of several state-of-the-art DL time series models for predicting sewer system dynamics in a large urban infrastructure, utilizing three years of measurement data. We especially investigate the potential of DL models to maintain predictive precision during network outages by comparing global models, which have access to all variables within the sewer system, and local models, which are limited to data from a restricted set of local sensors. Our findings demonstrate that DL models can accurately predict the dynamics of sewer system load, even under network outage conditions. These results suggest that DL models can effectively aid in balancing the load redistribution in CSS, thereby enhancing the sustainability and resilience of urban infrastructures.