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2024 (English)In: 3rd International Conference on Water Management in Changing Conditions: Book of abstracts, European Water Association; IFAT , 2024, p. 5-8Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]
Climate change has impacted global temperatures, triggering extreme weather and adverse environmental effects. In Sweden, these changes have caused shifts in weather patterns, leading to disruptions in infrastructure. This, in turn, has influenced water turbidity levels, negatively impacting water quality. To tackle these issues, a study was conducted using machine learning to predict turbidity with six meteorological variables collected for two years. Our preliminary research showed a substantial influence of seasonal changes on water turbidity, especially air temperature. Identifying supporting indicators such as lagged features is crucial and considerably improved the turbidity prediction performance for two of the machine learning models used. However, the study also identified challenges like data collection and uncertainty issues. We recommend improving data collection quality with higher frequency, minimizing geographical gaps between data collection points, sharing calibration assumptions, checking the sensors regularly, and accounting for data anomalies. Understanding these challenges and their potential implications could lead to more methodological enhancements.
Place, publisher, year, edition, pages
European Water Association; IFAT, 2024
Keywords
Water quality, turbidity, climate change, feature engineering, machine learning
National Category
Oceanography, Hydrology and Water Resources Climate Science Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-24148 (URN)
Conference
3rd International Conference on Water Management in Changing Conditions, WMCC-2024, EWA-IWA Water Management in Changing Climates Conference, 14-15 May 2024, Munich, Germany
Funder
Vinnova, DNR 2021-02460
Note
Corresponding author: juhee.bae@his.se
This project has been funded by VINNOVA, the Swedish Government Agency for Innovation Systems, “AI för klimatanpassning - metoder för att skapa en mer resilient dricksvattenproduktion och leverans” (DNR 2021-02460) and was conducted in cooperation with IVL Svenska Miljöinstitutet AB.
2024-07-022024-07-022025-02-01Bibliographically approved