The optimization of Low Impact Development placement considering life cycle cost using Genetic Algorithm

被引:37
作者
Huang, Jeanne Jinhui [1 ]
Xiao, Meng [1 ]
Li, Yu [2 ]
Yan, Ran [1 ]
Zhang, Qian [3 ]
Sun, Youyue [1 ]
Zhao, Tongtong [1 ]
机构
[1] Nankai Univ, Coll Environm Sci & Engn, Tianjin 300350, Peoples R China
[2] Nankai Univ, Shenzhen Res Inst, Shenzhen 518057, Peoples R China
[3] Tianjin Agr Univ, Coll Water Conservancy Engn, Tianjin 300000, Peoples R China
基金
中国国家自然科学基金; 中国博士后科学基金; 国家重点研发计划;
关键词
Storm water management model; Low impact development; Genetic algorithm; Life cycle cost; CLIMATE-CHANGE; URBANIZATION;
D O I
10.1016/j.jenvman.2022.114700
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Low Impact Development (LID) is an effective measure in controlling the urban runoff and mitigating the non -point source pollution. The determination of LID facilities and layouts for a sub-catchment is important for designing stormwater management system. However, there are remain large uncertainty and challenge exist in determination of LID facilities when consider budget, land use, soil surface and groundwater as well as local climate etc. To address this issue, this study employed Genetic Algorithm (GA) for optimization of the selection and layout of LID. The urban runoff was simulated using Environmental Protection Agency (EPA) Storm Water Management Model (SWMM). The LID planning was encoded as 0 and 1 in GA algorithm. The multiple objectives which include runoff reduction, area of LID and life cycle cost were selected as optimization targets. To test the model performance, the Airport Economic Zone in Tianjin, China was chosen as the study area. The results demonstrate that the combination of LID approaches are most effective measures on runoff reduction through long-term simulation (10 years' rainfall events). The impact of different weights of land area and cost on LID selection were evaluated when considering life cycle cost. Bio-Retention is preferred when considering the area of LID and Green Roof is recommended when the cost is prioritized. The present research proved GA is feasible for LID planning in urban area. The proposed method can help the decision-makers to determine the LID plan more scientific based on SWMM model and GA.
引用
收藏
页数:9
相关论文
共 56 条
[1]  
[Anonymous], 2006, ENV MAN LIF CYCL ASS, V2nd, DOI DOI 10.1016/J.SCITOTENV.2017.03.242
[2]  
[Anonymous], 2018, China Statistical Yearbook
[3]   Green roofs and facades: A comprehensive review [J].
Besir, Ahmet B. ;
Cuce, Erdem .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2018, 82 :915-939
[4]   Hydrologically Informed Machine Learning for Rainfall-Runoff Modeling: A Genetic Programming-Based Toolkit for Automatic Model Induction [J].
Chadalawada, Jayashree ;
Herath, H. M. V. V. ;
Babovic, Vladan .
WATER RESOURCES RESEARCH, 2020, 56 (04)
[5]   Application of random number generators in genetic algorithms to improve rainfall-runoff modelling [J].
Chlumecky, Martin ;
Buchtele, Josef ;
Richta, Karel .
JOURNAL OF HYDROLOGY, 2017, 553 :350-355
[6]   Incorporating genetic algorithm to optimise initial condition of pedestrian evacuation based on agent aggressiveness [J].
Cui, Geng ;
Yanagisawa, Daichi ;
Nishinari, Katsuhiro .
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2021, 583
[7]   Towards efficient Low Impact Development: A multi-scale simulation-optimization approach for nutrient removal at the urban watershed [J].
Dong, Feifei ;
Zhang, Zhenzhen ;
Liu, Chun ;
Zou, Rui ;
Liu, Yong ;
Guo, Huaicheng .
JOURNAL OF CLEANER PRODUCTION, 2020, 269
[8]  
Fritz M, 2017, THEOR PRACT URB SUST, P1, DOI 10.1007/978-3-319-56091-5_1
[9]  
Fu C, APPL SOFT COMPUT, V110, P2021
[10]   Extrapolation-enhanced model for travel decision making: An ensemble machine learning approach considering behavioral theory [J].
Gao, Kun ;
Yang, Ying ;
Zhang, Tianshu ;
Li, Aoyong ;
Qu, Xiaobo .
KNOWLEDGE-BASED SYSTEMS, 2021, 218