{"title":"Performance Evaluation of Low Impact Development Practices Using Linear Regression","authors":"M. Eric, James Li, D. Joksimovic","doi":"10.9734/BJECC/2015/11578","DOIUrl":null,"url":null,"abstract":"Aims: To develop a modelling methodology for evaluating the cumulative stormwater performance of Low Impact D evelopment technologies on a watershed basis to address stormwater impacts of urban development. Stu dy Design : A method is presented to perform hydrological modelling on large watersheds. Hydrological modelling simulations and linear regression analyses of a small sample of randomly selected lots were performed to generate results which were extrapolated to the entire watershed. Place and Duration of Study: Department of Civil Engineering, Ryerson University, between September 2010 and September 2012. Methodology: Urban hydrological response units were developed by using the K - means cluster analysis procedure to group 6926 lot parcels amenable to the residential rain barrel Low Impact D evelopment practice into clusters. Two versions of a Microsoft Excel macro were d eveloped to run simulations for thousands of lots s imultaneously before and after Low Impact D evelopment implementation to determine the total runoff produced by all lots for both cases. The results of computer modelling all lots were compared with the res ults from developing calculation methods to be used after computer modelling subsets of lots. Two calculation methods based on clustering lots to form urban hydrological response units were developed. A random sample of 5 % of all lots was then extracted from 6616 lots a menable to the porous pavement Low Impact D evelopment. Stepwise linear regression and linear regression were performed on the ran dom sample for each case of no Low I m pact D evelopment and with Low Impact D evelopment. Regression equations we re used to extrapolate results from the sample to Original Research Article","PeriodicalId":373103,"journal":{"name":"British Journal of Environment and Climate Change","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"British Journal of Environment and Climate Change","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.9734/BJECC/2015/11578","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Aims: To develop a modelling methodology for evaluating the cumulative stormwater performance of Low Impact D evelopment technologies on a watershed basis to address stormwater impacts of urban development. Stu dy Design : A method is presented to perform hydrological modelling on large watersheds. Hydrological modelling simulations and linear regression analyses of a small sample of randomly selected lots were performed to generate results which were extrapolated to the entire watershed. Place and Duration of Study: Department of Civil Engineering, Ryerson University, between September 2010 and September 2012. Methodology: Urban hydrological response units were developed by using the K - means cluster analysis procedure to group 6926 lot parcels amenable to the residential rain barrel Low Impact D evelopment practice into clusters. Two versions of a Microsoft Excel macro were d eveloped to run simulations for thousands of lots s imultaneously before and after Low Impact D evelopment implementation to determine the total runoff produced by all lots for both cases. The results of computer modelling all lots were compared with the res ults from developing calculation methods to be used after computer modelling subsets of lots. Two calculation methods based on clustering lots to form urban hydrological response units were developed. A random sample of 5 % of all lots was then extracted from 6616 lots a menable to the porous pavement Low Impact D evelopment. Stepwise linear regression and linear regression were performed on the ran dom sample for each case of no Low I m pact D evelopment and with Low Impact D evelopment. Regression equations we re used to extrapolate results from the sample to Original Research Article