Interpretable Time Series Models for Wastewater Modeling in Combined Sewer Overflows

Autor:	Chiaburu, Teodor, Biessmann, Felix
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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. Comment: 8 pages, 5 figures, 2 tables, presented at iSCSi 2023 Lisbon
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2401.02465 Zobrazit plný text záznamu View this record from Arxiv