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Practical data-based modelling approach for estimating river water turbidity and total organic carbon
Jani Tomperi1, Ari Isokangas1, Mika Ruusunen1
1Control Engineering research group, Environmental and Chemical Engineering research unit, University of Oulu, Oulu, Finland.
Environmental Technology
|June 10, 2025
Summary
This study presents a cost-effective method using data modeling to estimate river water quality variables like turbidity and total organic carbon in real-time. This approach enhances water safety monitoring and process efficiency without expensive sensors.
Area of Science:
- Environmental Science
- Water Resource Management
- Data Science
Background:
- Freshwater quality impacts aquatic ecosystems, human health, and industrial processes.
- Continuous raw water quality monitoring is crucial for optimal process operation.
- High costs and maintenance of hardware sensors limit real-time river water monitoring.
Purpose of the Study:
- To develop a cost-effective, data-based model for real-time estimation of river water quality.
- To provide an alternative to expensive hardware sensors for monitoring turbidity and total organic carbon.
- To enable proactive adjustments in water-dependent industrial processes and improve water safety awareness.
Main Methods:
- Utilized a multiple linear regression model.
- Employed river water level and water temperature as input variables.
- Validated the model using a year-round dataset and three independent testing datasets.
Main Results:
- The model accurately estimated water turbidity (R: 0.80) and total organic carbon (R: 0.85) during the training period.
- The model demonstrated accuracy across diverse environmental conditions in independent tests.
- The approach proved practical, straightforward, and cost-effective.
Conclusions:
- Data-based modeling offers a viable alternative for real-time river water quality monitoring.
- The developed model enables continuous assessment of key water quality parameters.
- This approach supports enhanced water safety, resource efficiency, and proactive industrial process management.
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