Impact of rainfall characteristics on urban stormwater quality using data mining framework
Understanding the impact of rainfall characteristics on urban stormwater quality is important for stormwater management. Even though significant attempts have been undertaken to study the relationship between rainfall and urban stormwater quality, the knowledge developed may be difficult to apply in...
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| Veröffentlicht in: | The Science of the total environment Jg. 862; S. 160689 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Netherlands
Elsevier B.V
01.03.2023
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| ISSN: | 0048-9697, 1879-1026, 1879-1026 |
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| Abstract | Understanding the impact of rainfall characteristics on urban stormwater quality is important for stormwater management. Even though significant attempts have been undertaken to study the relationship between rainfall and urban stormwater quality, the knowledge developed may be difficult to apply in commercial stormwater management models. A data mining framework was proposed to study the impacts of rainfall characteristics on stormwater quality. A rainfall type-based calibration approach was developed to improve water quality model performance. Specifically, the relationship between rainfall characteristics and stormwater quality was studied using principal component analysis and correlation analysis. Rainfall events were classified using a K-means clustering method based on the selected rainfall characteristics. A rainfall type-based (RTB) model was independently calibrated for each rainfall type to obtain optimal parameter sets of stormwater quality models. The results revealed that antecedent dry days, average rainfall intensity, and rainfall duration were the most critical rainfall characteristics affecting the event mean concentrations (EMCs) of total suspended solids, total nitrogen, and total phosphorus, while total rainfall was found to be of negligible importance. The K-means method effectively clustered the rainfall events into four types that could represent the rainfall characteristics in the study areas. The rainfall type-based calibration approach can considerably improve water quality model accuracy. Compared to the traditional continuous simulation model, the relative error of the RTB model was reduced by 11.4 % to 16.4 % over the calibration period. The calibrated stormwater quality parameters can be transferred to adjacent catchments with similar characteristics.
[Display omitted]
•A data mining framework is proposed to study the water quality response on rainfall.•Rainfall events are classified into four types based on screened rainfall indices.•A new calibration approach is developed to improve water quality model performance.•New approach improves water quality model performance by 11.4 % to 16.4 %. |
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| AbstractList | Understanding the impact of rainfall characteristics on urban stormwater quality is important for stormwater management. Even though significant attempts have been undertaken to study the relationship between rainfall and urban stormwater quality, the knowledge developed may be difficult to apply in commercial stormwater management models. A data mining framework was proposed to study the impacts of rainfall characteristics on stormwater quality. A rainfall type-based calibration approach was developed to improve water quality model performance. Specifically, the relationship between rainfall characteristics and stormwater quality was studied using principal component analysis and correlation analysis. Rainfall events were classified using a K-means clustering method based on the selected rainfall characteristics. A rainfall type-based (RTB) model was independently calibrated for each rainfall type to obtain optimal parameter sets of stormwater quality models. The results revealed that antecedent dry days, average rainfall intensity, and rainfall duration were the most critical rainfall characteristics affecting the event mean concentrations (EMCs) of total suspended solids, total nitrogen, and total phosphorus, while total rainfall was found to be of negligible importance. The K-means method effectively clustered the rainfall events into four types that could represent the rainfall characteristics in the study areas. The rainfall type-based calibration approach can considerably improve water quality model accuracy. Compared to the traditional continuous simulation model, the relative error of the RTB model was reduced by 11.4 % to 16.4 % over the calibration period. The calibrated stormwater quality parameters can be transferred to adjacent catchments with similar characteristics.Understanding the impact of rainfall characteristics on urban stormwater quality is important for stormwater management. Even though significant attempts have been undertaken to study the relationship between rainfall and urban stormwater quality, the knowledge developed may be difficult to apply in commercial stormwater management models. A data mining framework was proposed to study the impacts of rainfall characteristics on stormwater quality. A rainfall type-based calibration approach was developed to improve water quality model performance. Specifically, the relationship between rainfall characteristics and stormwater quality was studied using principal component analysis and correlation analysis. Rainfall events were classified using a K-means clustering method based on the selected rainfall characteristics. A rainfall type-based (RTB) model was independently calibrated for each rainfall type to obtain optimal parameter sets of stormwater quality models. The results revealed that antecedent dry days, average rainfall intensity, and rainfall duration were the most critical rainfall characteristics affecting the event mean concentrations (EMCs) of total suspended solids, total nitrogen, and total phosphorus, while total rainfall was found to be of negligible importance. The K-means method effectively clustered the rainfall events into four types that could represent the rainfall characteristics in the study areas. The rainfall type-based calibration approach can considerably improve water quality model accuracy. Compared to the traditional continuous simulation model, the relative error of the RTB model was reduced by 11.4 % to 16.4 % over the calibration period. The calibrated stormwater quality parameters can be transferred to adjacent catchments with similar characteristics. Understanding the impact of rainfall characteristics on urban stormwater quality is important for stormwater management. Even though significant attempts have been undertaken to study the relationship between rainfall and urban stormwater quality, the knowledge developed may be difficult to apply in commercial stormwater management models. A data mining framework was proposed to study the impacts of rainfall characteristics on stormwater quality. A rainfall type-based calibration approach was developed to improve water quality model performance. Specifically, the relationship between rainfall characteristics and stormwater quality was studied using principal component analysis and correlation analysis. Rainfall events were classified using a K-means clustering method based on the selected rainfall characteristics. A rainfall type-based (RTB) model was independently calibrated for each rainfall type to obtain optimal parameter sets of stormwater quality models. The results revealed that antecedent dry days, average rainfall intensity, and rainfall duration were the most critical rainfall characteristics affecting the event mean concentrations (EMCs) of total suspended solids, total nitrogen, and total phosphorus, while total rainfall was found to be of negligible importance. The K-means method effectively clustered the rainfall events into four types that could represent the rainfall characteristics in the study areas. The rainfall type-based calibration approach can considerably improve water quality model accuracy. Compared to the traditional continuous simulation model, the relative error of the RTB model was reduced by 11.4 % to 16.4 % over the calibration period. The calibrated stormwater quality parameters can be transferred to adjacent catchments with similar characteristics. [Display omitted] •A data mining framework is proposed to study the water quality response on rainfall.•Rainfall events are classified into four types based on screened rainfall indices.•A new calibration approach is developed to improve water quality model performance.•New approach improves water quality model performance by 11.4 % to 16.4 %. Understanding the impact of rainfall characteristics on urban stormwater quality is important for stormwater management. Even though significant attempts have been undertaken to study the relationship between rainfall and urban stormwater quality, the knowledge developed may be difficult to apply in commercial stormwater management models. A data mining framework was proposed to study the impacts of rainfall characteristics on stormwater quality. A rainfall type-based calibration approach was developed to improve water quality model performance. Specifically, the relationship between rainfall characteristics and stormwater quality was studied using principal component analysis and correlation analysis. Rainfall events were classified using a K-means clustering method based on the selected rainfall characteristics. A rainfall type-based (RTB) model was independently calibrated for each rainfall type to obtain optimal parameter sets of stormwater quality models. The results revealed that antecedent dry days, average rainfall intensity, and rainfall duration were the most critical rainfall characteristics affecting the event mean concentrations (EMCs) of total suspended solids, total nitrogen, and total phosphorus, while total rainfall was found to be of negligible importance. The K-means method effectively clustered the rainfall events into four types that could represent the rainfall characteristics in the study areas. The rainfall type-based calibration approach can considerably improve water quality model accuracy. Compared to the traditional continuous simulation model, the relative error of the RTB model was reduced by 11.4 % to 16.4 % over the calibration period. The calibrated stormwater quality parameters can be transferred to adjacent catchments with similar characteristics. |
| ArticleNumber | 160689 |
| Author | Zhang, Wenming Ahmed, Sherif Zhu, David Z. van Duin, Bert Mahmood, Khizar Yan, Haibin Liang, Shuntian Zhao, Stacey Loewen, Mark R. |
| Author_xml | – sequence: 1 givenname: Haibin surname: Yan fullname: Yan, Haibin organization: Department of Civil and Environmental Engineering University of Alberta, Edmonton, AB T6G 1H9, Canada – sequence: 2 givenname: David Z. surname: Zhu fullname: Zhu, David Z. email: dzhu@ualberta.ca organization: Department of Civil and Environmental Engineering University of Alberta, Edmonton, AB T6G 1H9, Canada – sequence: 3 givenname: Mark R. surname: Loewen fullname: Loewen, Mark R. organization: Department of Civil and Environmental Engineering University of Alberta, Edmonton, AB T6G 1H9, Canada – sequence: 4 givenname: Wenming surname: Zhang fullname: Zhang, Wenming organization: Department of Civil and Environmental Engineering University of Alberta, Edmonton, AB T6G 1H9, Canada – sequence: 5 givenname: Shuntian surname: Liang fullname: Liang, Shuntian organization: Department of Civil and Environmental Engineering University of Alberta, Edmonton, AB T6G 1H9, Canada – sequence: 6 givenname: Sherif surname: Ahmed fullname: Ahmed, Sherif organization: Department of Civil and Environmental Engineering University of Alberta, Edmonton, AB T6G 1H9, Canada – sequence: 7 givenname: Bert surname: van Duin fullname: van Duin, Bert organization: Department of Civil and Environmental Engineering University of Alberta, Edmonton, AB T6G 1H9, Canada – sequence: 8 givenname: Khizar surname: Mahmood fullname: Mahmood, Khizar organization: Climate & Environment Business Unit, City of Calgary, Mail Code #437, P.O. Box 2100, Station M, Calgary, AB T2P 2M5, Canada – sequence: 9 givenname: Stacey surname: Zhao fullname: Zhao, Stacey organization: Climate & Environment Business Unit, City of Calgary, Mail Code #437, P.O. Box 2100, Station M, Calgary, AB T2P 2M5, Canada |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36473661$$D View this record in MEDLINE/PubMed |
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| Keywords | K-means clustering algorithm Calibration PCSWMM Parameter transferability Principal component analysis |
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