# Interpretable Time Series Models for Wastewater Modeling in Combined Sewer Overflows

arXiv: https://arxiv.org/abs/2401.02465 · HTML: https://mlsanity.com/paper/2401.02465/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 15 references that are in the map.
- First posted on arXiv 4 Jan 2024.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [TabNet: Attentive Interpretable Tabular Learning](https://mlsanity.com/paper/1908.07442/) (2019)
- [N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting](https://mlsanity.com/paper/2201.12886/) (2022)
- [Stochastic Weight Averaging Revisited](https://mlsanity.com/paper/2201.00519/) (2022)
- [Accelerating hydrodynamic simulations of urban drainage systems with physics-guided machine learning](https://mlsanity.com/paper/2206.01538/) (2022)

## Built on by (most cited first, 2 of 2)

- [Data-driven Modeling of Combined Sewer Systems for Urban Sustainability: An Empirical Evaluation](https://mlsanity.com/paper/2408.11619/) (2024)
- [Evaluating Time Series Models for Urban Wastewater Management: Predictive Performance, Model Complexity and Resilience](https://mlsanity.com/paper/2504.17461/) (2025)

## Abstract (arXiv)

Climate change poses increasingly complex challenges to our society. Extreme weather events such as floods, wild fires or droughts are becoming more frequent, spontaneous and difficult to foresee or counteract. In this work we specifically address the problem of sewage water polluting surface water bodies after spilling over from rain tanks as a consequence of heavy rain events. We investigate to what extent state-of-the-art interpretable time series models can help predict such critical water level points, so that the excess can promptly be redistributed across the sewage network. Our results indicate that modern time series models can contribute to better waste water management and prevention of environmental pollution from sewer systems. All the code and experiments can be found in our repository: https://github.com/TeodorChiaburu/RIWWER_TimeSeries.