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Impact of rainfall characteristics on urban stormwater quality using data mining framework
Haibin Yan1, David Z Zhu2, Mark R Loewen1
1Department of Civil and Environmental Engineering University of Alberta, Edmonton, AB T6G 1H9, Canada.
The Science of the Total Environment
|December 6, 2022
Summary
This study reveals that antecedent dry days, rainfall intensity, and duration significantly impact urban stormwater quality, unlike total rainfall volume. A novel rainfall type-based model improves stormwater quality predictions.
Area of Science:
- Environmental Science
- Hydrology
- Water Quality Management
Background:
- Effective urban stormwater management necessitates understanding how rainfall characteristics influence water quality.
- Existing models struggle to integrate rainfall-stormwater quality relationships due to complexities.
- Data mining offers a framework to address these challenges.
Purpose of the Study:
- To develop and evaluate a data mining framework for assessing rainfall impacts on urban stormwater quality.
- To introduce a rainfall type-based (RTB) calibration approach for enhancing water quality model performance.
- To identify critical rainfall characteristics influencing key stormwater pollutants.
Main Methods:
- Utilized principal component analysis and correlation analysis to examine rainfall-stormwater quality relationships.
- Employed K-means clustering to classify rainfall events into distinct types based on characteristics.
- Developed and calibrated RTB models for each rainfall type to optimize stormwater quality model parameters.
Main Results:
- Antecedent dry days, average rainfall intensity, and rainfall duration were identified as critical factors influencing event mean concentrations (EMCs) of total suspended solids, total nitrogen, and total phosphorus.
- Total rainfall volume showed negligible importance for stormwater quality.
- K-means clustering successfully categorized rainfall events into four representative types.
- The RTB model calibration significantly improved water quality model accuracy, reducing relative error by 11.4%–16.4% compared to traditional models.
Conclusions:
- The developed data mining framework and RTB calibration approach effectively improve urban stormwater quality modeling.
- Critical rainfall characteristics provide a robust basis for classifying events and calibrating models.
- Calibrated model parameters demonstrate transferability to similar catchments, enhancing practical application in stormwater management.
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