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

arXiv: https://arxiv.org/abs/2408.11619 · HTML: https://mlsanity.com/paper/2408.11619/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 21 Aug 2024.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [Transformers in Time Series: A Survey](https://mlsanity.com/paper/2202.07125/) (2022)
- [N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting](https://mlsanity.com/paper/2201.12886/) (2022)
- [Hidden Fluid Mechanics: A Navier-Stokes Informed Deep Learning Framework for Assimilating Flow Visualization Data](https://mlsanity.com/paper/1808.04327/) (2018)
- [FlowPrecision: Advancing FPGA-Based Real-Time Fluid Flow Estimation with Linear Quantization](https://mlsanity.com/paper/2403.01922/) (2024)
- [A Look at the Evaluation Setup of the M5 Forecasting Competition](https://mlsanity.com/paper/2108.03588/) (2021)
- [Interpretable Time Series Models for Wastewater Modeling in Combined Sewer Overflows](https://mlsanity.com/paper/2401.02465/) (2024)

## Built on by (most cited first, 3 of 3)

- [Automated Energy-Aware Time-Series Model Deployment on Embedded FPGAs for Resilient Combined Sewer Overflow Management](https://mlsanity.com/paper/2508.13905/) (2025)
- [Evaluating Time Series Models for Urban Wastewater Management: Predictive Performance, Model Complexity and Resilience](https://mlsanity.com/paper/2504.17461/) (2025)
- [A Resilient Solution for Sewer Overflow Monitoring across Cloud and Edge](https://mlsanity.com/paper/2605.10592/) (2026)

## Abstract (arXiv)

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.