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
[11]  
Goldberg D. E., 1989, GENETIC ALGORITHMS S
[12]   A generalized machine learning approach for dissolved oxygen estimation at multiple spatiotemporal scales using remote sensing [J].
Guo, Hongwei ;
Huang, Jinhui Jeanne ;
Zhu, Xiaotong ;
Wang, Bo ;
Tian, Shang ;
Xu, Wang ;
Mai, Youquan .
ENVIRONMENTAL POLLUTION, 2021, 288
[13]  
Herath H.M.V.V, 2022, WATER RESOUR MANAG
[14]   Genetic programming for hydrological applications: to model or forecast that is the question [J].
Herath, Herath Mudiyanselage Viraj Vidura ;
Chadalawada, Jayashree ;
Babovic, Vladan .
JOURNAL OF HYDROINFORMATICS, 2021, 23 (04) :740-763
[15]  
Holand John H., 1975, Adaptation in natural and artificial systems
[16]   Optimal spatial priority scheme of urban LID-BMPs under different investment periods [J].
Hou, Jingwei ;
Zhu, Moyan ;
Wang, Yanjuan ;
Sun, Shiqin .
LANDSCAPE AND URBAN PLANNING, 2020, 202
[17]   Spatial Optimization of Low-Impact Development Facilities Based on a p-Median Model and an Ant Colony Optimization [J].
Hou, Jingwei ;
Ho, Bo ;
Sun, Shiqin .
JOURNAL OF HYDROLOGIC ENGINEERING, 2019, 24 (12)
[18]   Flood mitigation performance of low impact development technologies under different storms for retrofitting an urbanized area [J].
Hu, Maochuan ;
Zhang, Xingqi ;
Li, Yu ;
Yang, Hong ;
Tanaka, Kenji .
JOURNAL OF CLEANER PRODUCTION, 2019, 222 :373-380
[19]   Flood Mitigation by Permeable Pavements in Chinese Sponge City Construction [J].
Hu, Maochuan ;
Zhang, Xingqi ;
Siu, Yim Ling ;
Li, Yu ;
Tanaka, Kenji ;
Yang, Hong ;
Xu, Youpeng .
WATER, 2018, 10 (02)
[20]   Evaluating the effect of urban flooding reduction strategies in response to design rainfall and low impact development [J].
Hua, Pei ;
Yang, Wenyu ;
Qi, Xiaochen ;
Jiang, Shanshan ;
Xie, Jiaqiang ;
Gu, Xianyong ;
Li, Honghao ;
Zhang, Jin ;
Krebs, Peter .
JOURNAL OF CLEANER PRODUCTION, 2020, 242