City-scale optimal location planning of Green Infrastructure using piece-wise linear interpolation and exact optimization methods

Torres, María Nariné; Fontecha, John E.; Walteros, Jose L.; Zhu, Zhenduo; Ahmed, Zia; Rodríguez, Juan ́Pablo; Rabideau, Alan J.

doi:10.1016/j.jhydrol.2021.126540

Cited by 19 publications

(5 citation statements)

References 45 publications

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“…A multi-objective optimization model was built using the python language and solved using the Gurobi Optimizer [77]. The model is a modification of the Two Stage Mixed Integer Linear Program (TS-MILP) developed by Torres et al [35] for rainwater harvesting and run-off management. In the original model, the objectives are the reduction of runoff and the reuse of the intercepted water for irrigation purposes.…”

Section: Phase 4-optimizationmentioning

confidence: 99%

“…Scenario A represents the selection without budget restriction, scenario B is 5% of the total budget, scenario C is 10%, and scenario D is 20%. For more details on the optimization model, refer to Torres et al [35].…”

Section: Phase 4-optimizationmentioning

confidence: 99%

“…MCDA has been widely used for ecosystem services assessment during the urban planning process [30,31]. Various studies have focused on the GI distribution according to ecosystem services potential and physical and social characteristics of particular case studies [32][33][34][35][36][37]. These studies developed decision-support tools, spatial models, and ecosystem services assessments to guide the urban planning process at a fixed scale (municipal or local).…”

Section: Introductionmentioning

confidence: 99%

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A SUDS Planning Decision Support Tool to Maximize Ecosystem Services

et al. 2022

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In the past years, alternative drainage approaches have emerged, such as Sustainable Urban Drainage Systems (SUDS), to prevent run-off and flooding impacts on the most vulnerable zones of the cities. These systems not only provide the benefit of water regulation but also promote other types of ecosystem services. Several studies have developed optimization tools to assist SUDS selection, location, and design. However, they do not consider a comprehensive set of ecosystem services (e.g., provision, regulation, cultural, and support services). This research proposes a flexible and adaptable methodology to incorporate SUDS in different stages of urban projects using a multi-objective optimization technique to minimize run-off, maximize ecosystem services and minimize cost. The methodology comprises four phases: (1) the preliminary analysis of ecosystem services potentially generated by each SUDS type, (2) the priority and opportunity index quantification, (3) the physical feasibility analysis, and (4) the multi-objective optimization tool implementation. The methodology was successfully applied to three different urban areas of Bogotá city (Colombia). Results evidence that the interaction of the budget constraints and the available area restrict the potential benefits of SUDS implementation. These results are helpful to support different urban planning stages.

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Section: Phase 4-optimizationmentioning

confidence: 99%

Section: Phase 4-optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A SUDS Planning Decision Support Tool to Maximize Ecosystem Services

et al. 2022

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“…Torres M.N. also has five papers, presumably contributing significantly to the discourse in heuristic optimization [13].…”

Section: Introductionmentioning

confidence: 99%

Integrating Minimum Spanning Tree and MILP in Urban Planning: A Novel Algorithmic Perspective

Pavon,

Torres,

Inga

2024

Buildings

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This paper presents a novel eight-step iterative algorithm for optimizing the layout of a neighborhood, focusing on the efficient allocation of houses to strategically placed facilities, herein referred to as ’points of interest’. The methodology integrates a mixed integer linear programming (MILP) approach with a heuristic algorithm to address a variant of the facility location problem combined with network design considerations. The algorithm begins by defining a set of geographic coordinates to represent houses within a predefined area. It then identifies key points of interest, forming the basis for subsequent connectivity and allocation analyses. The methodology’s core involves applying the Greedy algorithm to assign houses to the nearest points of interest, subject to capacity constraints. The method is followed by computing a Minimum Spanning Tree (MST) among these points to ensure efficient overall connectivity. The proposed algorithm’s iterative design is a key attribute. The most promising result of this approach is its ability to minimize the distance between houses and points of interest while optimizing the network’s total length. This dual optimization ensures a balanced distribution of houses and an efficient layout, making it particularly suitable for urban planning and infrastructure development. The paper’s findings demonstrate the algorithm’s effectiveness in creating a practical and efficient neighborhood layout, highlighting its potential application in large-scale urban planning and development projects.

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“…Second, although peak flow reduction and base flow increase are also observed in catchment studies (Bhaskar et al., 2016; Jarden et al., 2016; Page et al., 2015; Pennino et al., 2016; Shuster & Rhea, 2013), the cumulative effects of GI are nonlinear and only become observable when there is sufficient density of GI within the study catchment (Jefferson et al., 2017). Third, the watershed‐scale research needed for studying the effects of GI in the field is costly to fund and difficult to coordinate, often requiring both financial and political support from property owners, city residents, and regulatory agencies (Torres et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

Integrating the Spatial Configurations of Green and Gray Infrastructure in Urban Stormwater Networks

Chen,

Davitt‐Liu,

Erickson

et al. 2023

Water Resources Research

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Green infrastructure (GI) practices improve stormwater quality and reduce urban flooding, but as urban hydrology is highly controlled by its associated gray infrastructure (e.g., stormwater pipe network), GI's watershed‐scale performance depends on its siting within its associated watershed. Although many stormwater practitioners have begun considering GI's spatial configuration within a larger watershed, few approaches allow for flexible scenario exploration, which can untangle GI's interaction with gray infrastructure network and assess its effects on watershed hydrology. To address the gap in integrated gray‐green infrastructure planning, we used an exploratory model to examine gray‐green infrastructure performance using synthetic stormwater networks with varying degrees of flow path meandering, informed by analysis on stormwater networks from the Minneapolis‐St. Paul Metropolitan Area, MN, USA. Superimposed with different coverage and placements of GI (e.g., bioretention cells), these gray‐green stormwater networks are then subjected to different rainfall intensities within Environmental Protection Agency's Storm Water Management Model to simulate their hydrological benefits (e.g., peak flow reduction, flood reduction). Although only limited choices of green and gray infrastructure were explored, the results show that the gray infrastructure's spatial configuration can introduce tradeoffs between increased peak flow and increased flooding, and further interacts with GI coverage and placement to reduce peak flow and flooding at low rainfall intensity. However, as rainfall intensifies, GI ceases to reduce peak flow. For integrated gray‐green infrastructure planning, our results suggest that physical constraints of the stormwater networks and the range of rainfall intensities must be considered when implementing GI.

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City-scale optimal location planning of Green Infrastructure using piece-wise linear interpolation and exact optimization methods

Cited by 19 publications

References 45 publications

A SUDS Planning Decision Support Tool to Maximize Ecosystem Services

A SUDS Planning Decision Support Tool to Maximize Ecosystem Services

Integrating Minimum Spanning Tree and MILP in Urban Planning: A Novel Algorithmic Perspective

Integrating the Spatial Configurations of Green and Gray Infrastructure in Urban Stormwater Networks

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