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ABSTRACT
The occurrence of combined sewer overflow (CSO) is a pressing environmental issue in many cities. This study aims to predict CSO occurrence using rainfall event characteristics, including rainfall depth, maximum intensity, and duration, and to determine which characteristic is the best predictor. Buffalo, New York, was selected as a case study. The results indicate that the prediction accuracy ranges from 80% to 100% for rainfall depth and maximum intensity, while rainfall duration is not a good predictor. Furthermore, rainfall depth is more likely to be the best predictor for sewersheds with a larger area. Additionally, combining the three rainfall event characteristics using the decision tree can only improve the average prediction accuracy slightly, from 93% (using a single characteristic) to 95% (using three characteristics). Using rainfall event characteristics and this simple method can be an effective alternative to complex urban hydrological models and/or expensive monitoring for predicting CSO occurrence.
Acknowledgements
We thank the Buffalo Sewer Authority (BSA) for providing the PCSWMM model, Computational Hydraulics International (CHI) for providing a university grant to use PCSWMM. Dr Maria Narine Torres and Dr Alan Rabideau are acknowledged for their helpful discussion. The opinions and findings presented in this paper are solely those of the authors and do not represent the opinions of the BSA and/or any other agency mentioned in the manuscript.
Disclosure statement
No potential conflict of interest was reported by the authors.
Data availability statement
The hourly precipitation data used in this study are openly available at https://www.ncdc.noaa.gov/cdo-web/datasets/LCD/stations/WBAN:14733/detail.