Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Low Impact Development Placement Investigation Using a Multi-Objective Evolutionary Optimization Algorithm
Low Impact Development (LID) controls, such as green roofs and pervious pavements, are alternative stormwater management strategies designed to better mimic the pre-development hydrologic flow regime and mitigate the impacts of urbanization. Many studies focused on monitoring and understanding the hydraulic behavior of individual LIDs, while the collective behavior of retrofitting urban watersheds with LID controls is less understood. This study applies a Multi-Objective Evolutionary Algorithm (MOEA) connected to a hydrologic model to find near-optimal solutions in terms of LID locations in urban watersheds. This methodology identifies the tradeoffs between the costs of retrofitting urban areas with LIDs and the environmental benefits provided by such measures, such as surface runoff volume and peak flow reduction. The benefit provided by LIDs is also evaluated through the use of the Hydrologic Footprint Residence (HFR), a new stormwater metric that better quantifies hydrologic alteration than peak flow alone. The HFR is a spatial and temporal metric that represents the dynamics of inundated areas and residence time of flood waves throughout downstream segments. The proposed methodology can help urban stormwater decision makers improve the sustainability of urban watersheds as it provides guidelines of LIDs placement under cost constraints.
Low Impact Development Placement Investigation Using a Multi-Objective Evolutionary Optimization Algorithm
Low Impact Development (LID) controls, such as green roofs and pervious pavements, are alternative stormwater management strategies designed to better mimic the pre-development hydrologic flow regime and mitigate the impacts of urbanization. Many studies focused on monitoring and understanding the hydraulic behavior of individual LIDs, while the collective behavior of retrofitting urban watersheds with LID controls is less understood. This study applies a Multi-Objective Evolutionary Algorithm (MOEA) connected to a hydrologic model to find near-optimal solutions in terms of LID locations in urban watersheds. This methodology identifies the tradeoffs between the costs of retrofitting urban areas with LIDs and the environmental benefits provided by such measures, such as surface runoff volume and peak flow reduction. The benefit provided by LIDs is also evaluated through the use of the Hydrologic Footprint Residence (HFR), a new stormwater metric that better quantifies hydrologic alteration than peak flow alone. The HFR is a spatial and temporal metric that represents the dynamics of inundated areas and residence time of flood waves throughout downstream segments. The proposed methodology can help urban stormwater decision makers improve the sustainability of urban watersheds as it provides guidelines of LIDs placement under cost constraints.
Low Impact Development Placement Investigation Using a Multi-Objective Evolutionary Optimization Algorithm
Giacomoni, Marcio H. (Autor:in)
World Environmental and Water Resources Congress 2015 ; 2015 ; Austin, TX
15.05.2015
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
| British Library Online Contents | 2017