A new LID spatial allocation optimization system at neighborhood scale: Integrated SWMM with PICEA-g using MATLAB as the platform
Despite the growing interest, limited studies have been conducted on LID spatial allocation optimization (SAO) at neighborhood scale, and no study has applied modifications to the optimization algorithm to improve its performance. In this study, such a new LID SAO system was proposed, which integrat...
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| Published in | The Science of the total environment Vol. 831; p. 154843 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Netherlands
Elsevier B.V
20.07.2022
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 0048-9697 1879-1026 1879-1026 |
| DOI | 10.1016/j.scitotenv.2022.154843 |
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| Abstract | Despite the growing interest, limited studies have been conducted on LID spatial allocation optimization (SAO) at neighborhood scale, and no study has applied modifications to the optimization algorithm to improve its performance. In this study, such a new LID SAO system was proposed, which integrated a hydrological computing engine (SWMM) with an optimization algorithm (PICEA-g) using a programming language (MATLAB) as the platform. The specific modifications to the PICEA-g algorithm include: new methodologies for initializing candidate solutions, defining goal vector boundaries and enhanced genetic operators. The new LID SAO system was tested in a typical urban residential neighborhood in western Canada, and optimal solutions for LID implementation (bioretention, rain garden, permeable pavement and green roof) were obtained. The results showed that promising hydrologic benefits of reducing peak flow rate and total volume of stormwater runoff from the catchment, can be achieved with a relatively low cost. The LID SAO system provides users with flexibility and feasibility for a variety of drainage locations, scales and objectives (e.g., water quality).
[Display omitted]
•The proposed new LID spatial allocation optimization system couples SWMM & MATLAB.•PICEA-g algorithm was modified to further enhance performance of the system.•The system was demonstrated in neighborhood for cost effective hydrologic benefits.•The LID SAO system has flexibility and feasibility for alternative applications. |
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| AbstractList | Despite the growing interest, limited studies have been conducted on LID spatial allocation optimization (SAO) at neighborhood scale, and no study has applied modifications to the optimization algorithm to improve its performance. In this study, such a new LID SAO system was proposed, which integrated a hydrological computing engine (SWMM) with an optimization algorithm (PICEA-g) using a programming language (MATLAB) as the platform. The specific modifications to the PICEA-g algorithm include: new methodologies for initializing candidate solutions, defining goal vector boundaries and enhanced genetic operators. The new LID SAO system was tested in a typical urban residential neighborhood in western Canada, and optimal solutions for LID implementation (bioretention, rain garden, permeable pavement and green roof) were obtained. The results showed that promising hydrologic benefits of reducing peak flow rate and total volume of stormwater runoff from the catchment, can be achieved with a relatively low cost. The LID SAO system provides users with flexibility and feasibility for a variety of drainage locations, scales and objectives (e.g., water quality). Despite the growing interest, limited studies have been conducted on LID spatial allocation optimization (SAO) at neighborhood scale, and no study has applied modifications to the optimization algorithm to improve its performance. In this study, such a new LID SAO system was proposed, which integrated a hydrological computing engine (SWMM) with an optimization algorithm (PICEA-g) using a programming language (MATLAB) as the platform. The specific modifications to the PICEA-g algorithm include: new methodologies for initializing candidate solutions, defining goal vector boundaries and enhanced genetic operators. The new LID SAO system was tested in a typical urban residential neighborhood in western Canada, and optimal solutions for LID implementation (bioretention, rain garden, permeable pavement and green roof) were obtained. The results showed that promising hydrologic benefits of reducing peak flow rate and total volume of stormwater runoff from the catchment, can be achieved with a relatively low cost. The LID SAO system provides users with flexibility and feasibility for a variety of drainage locations, scales and objectives (e.g., water quality).Despite the growing interest, limited studies have been conducted on LID spatial allocation optimization (SAO) at neighborhood scale, and no study has applied modifications to the optimization algorithm to improve its performance. In this study, such a new LID SAO system was proposed, which integrated a hydrological computing engine (SWMM) with an optimization algorithm (PICEA-g) using a programming language (MATLAB) as the platform. The specific modifications to the PICEA-g algorithm include: new methodologies for initializing candidate solutions, defining goal vector boundaries and enhanced genetic operators. The new LID SAO system was tested in a typical urban residential neighborhood in western Canada, and optimal solutions for LID implementation (bioretention, rain garden, permeable pavement and green roof) were obtained. The results showed that promising hydrologic benefits of reducing peak flow rate and total volume of stormwater runoff from the catchment, can be achieved with a relatively low cost. The LID SAO system provides users with flexibility and feasibility for a variety of drainage locations, scales and objectives (e.g., water quality). Despite the growing interest, limited studies have been conducted on LID spatial allocation optimization (SAO) at neighborhood scale, and no study has applied modifications to the optimization algorithm to improve its performance. In this study, such a new LID SAO system was proposed, which integrated a hydrological computing engine (SWMM) with an optimization algorithm (PICEA-g) using a programming language (MATLAB) as the platform. The specific modifications to the PICEA-g algorithm include: new methodologies for initializing candidate solutions, defining goal vector boundaries and enhanced genetic operators. The new LID SAO system was tested in a typical urban residential neighborhood in western Canada, and optimal solutions for LID implementation (bioretention, rain garden, permeable pavement and green roof) were obtained. The results showed that promising hydrologic benefits of reducing peak flow rate and total volume of stormwater runoff from the catchment, can be achieved with a relatively low cost. The LID SAO system provides users with flexibility and feasibility for a variety of drainage locations, scales and objectives (e.g., water quality). [Display omitted] •The proposed new LID spatial allocation optimization system couples SWMM & MATLAB.•PICEA-g algorithm was modified to further enhance performance of the system.•The system was demonstrated in neighborhood for cost effective hydrologic benefits.•The LID SAO system has flexibility and feasibility for alternative applications. |
| ArticleNumber | 154843 |
| Author | Guo, Zhiyong van Duin, Bert Yu, Yang Zhou, Yongchao Zhang, Wenming |
| Author_xml | – sequence: 1 givenname: Yang surname: Yu fullname: Yu, Yang organization: Dept of Civil and Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada – sequence: 2 givenname: Yongchao surname: Zhou fullname: Zhou, Yongchao organization: The Institute of Municipal Engineering, Zhejiang University, Hangzhou 310058, China – sequence: 3 givenname: Zhiyong surname: Guo fullname: Guo, Zhiyong organization: College of New Energy and Environment, Jilin University, Changchun, 130012, China – sequence: 4 givenname: Bert surname: van Duin fullname: van Duin, Bert organization: Dept of Civil and Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada – sequence: 5 givenname: Wenming surname: Zhang fullname: Zhang, Wenming email: wenming@ualberta.ca organization: Dept of Civil and Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35351503$$D View this record in MEDLINE/PubMed |
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| Keywords | Cost-benefit SWMM PICEA-g MATLAB LID Spatial allocation optimization (SAO) |
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| SubjectTerms | algorithms Canada Cost-benefit drainage environment gardens green roofs Hydrology LID MATLAB Models, Theoretical pavements Picea PICEA-g Rain residential areas Spatial allocation optimization (SAO) stormwater SWMM Water Movements Water Quality watersheds |
| Title | A new LID spatial allocation optimization system at neighborhood scale: Integrated SWMM with PICEA-g using MATLAB as the platform |
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