Interpretable Time Series Models for Wastewater Modeling in Combined Sewer Overflows
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911748946132992 |
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| author | Chiaburu, Teodor Biessmann, Felix |
| author_facet | Chiaburu, Teodor Biessmann, Felix |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_02465 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Interpretable Time Series Models for Wastewater Modeling in Combined Sewer Overflows Chiaburu, Teodor Biessmann, Felix Machine Learning Artificial Intelligence 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. |
| title | Interpretable Time Series Models for Wastewater Modeling in Combined Sewer Overflows |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2401.02465 |