Coupled SWMM-MOEA/D for multi-objective optimization of low impact development in urban stormwater systems

• Hybrid SWMM-MOEA/D model enhances urban runoff management. • TOPSIS analysis ranks LID scenarios based on effectiveness and cost. • The coupled model aids decision-makers in sustainable urban stormwater planning. The escalating challenge of unsustainable urban development worldwide has precipitated changes in land usage, contributing to increased impermeability of the urban landscape. This phenomenon exacerbates urban runoff, a critical environmental concern. In response, Low Impact Development (LID) techniques, recognized for their environmental efficacy, have emerged as pivotal in mitigating urban runoff. However, transforming the hydrological dynamics of urban watersheds into a more sustainable state necessitates substantial financial commitments from relevant authorities. Consequently, strategic LID planning becomes essential to maximize effectiveness while minimizing costs. This research introduces a novel, hybrid modeling strategy that integrates the Storm Water Management Model (SWMM) with the Multi-Objective Evolutionary Algorithm by Decomposition (MOEA/D) optimization algorithm. This approach aims to concurrently minimize runoff volume, peak flow rate, and implementation expenses. Focusing on a segment of Tehran Municipality’s urban stormwater system in District 11, the study evaluates four distinct LID scenarios. These scenarios encompass various configurations of Rain Barrels (RB), Bioretention Cells (BC), Green Roofs (GR), and Porous Pavements (PP). Utilizing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method for comparative analysis, the study results identify the most efficacious scenario, S2_1, including RB and BC, which achieves a 19.34% reduction in runoff volume and a 46.53% decrease in peak flow rate, all at the implementation cost of 123,169 USD. A close second, scenario S3_1 incorporating RB and PP, demonstrates a 17% and 46.55% reduction in runoff volume and peak flow at an expenditure of 107,017 USD, respectively. The proposed SWMM-MOEA/D model, in conjunction with TOPSIS, presents a valuable tool for LID planning and optimization, offering decision-makers and relevant entities a pragmatic approach to address the challenges of urban runoff management.

Authors

Institutions

Publication Details

Journal
Journal of Hydrology
Published
2025-03-09
DOI
https://doi.org/10.1016/j.jhydrol.2025.133044
Citations
12
Primary Topic
Urban Stormwater Management Solutions
Type
article
Field-Weighted Citation Impact
9.59
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Coupled SWMM-MOEA/D for multi-objective optimization of low impact development in urban stormwater systems

Abbas Roozbahani, Amirhossein Nazari, Hossien Hossieni, Saeed Banihashemi et al.
12 citations
Journal of Hydrology
Urban Stormwater Management Solutions
9.59
article

Coupled SWMM-MOEA/D for multi-objective optimization of low impact development in urban stormwater systems

Abbas Roozbahani, Amirhossein Nazari, Hossien Hossieni, Saeed Banihashemi, Kazem Javan, Mariam Darestani
article en
12 citations

Abstract

• Hybrid SWMM-MOEA/D model enhances urban runoff management. • TOPSIS analysis ranks LID scenarios based on effectiveness and cost. • The coupled model aids decision-makers in sustainable urban stormwater planning. The escalating challenge of unsustainable urban development worldwide has precipitated changes in land usage, contributing to increased impermeability of the urban landscape. This phenomenon exacerbates urban runoff, a critical environmental concern. In response, Low Impact Development (LID) techniques, recognized for their environmental efficacy, have emerged as pivotal in mitigating urban runoff. However, transforming the hydrological dynamics of urban watersheds into a more sustainable state necessitates substantial financial commitments from relevant authorities. Consequently, strategic LID planning becomes essential to maximize effectiveness while minimizing costs. This research introduces a novel, hybrid modeling strategy that integrates the Storm Water Management Model (SWMM) with the Multi-Objective Evolutionary Algorithm by Decomposition (MOEA/D) optimization algorithm. This approach aims to concurrently minimize runoff volume, peak flow rate, and implementation expenses. Focusing on a segment of Tehran Municipality’s urban stormwater system in District 11, the study evaluates four distinct LID scenarios. These scenarios encompass various configurations of Rain Barrels (RB), Bioretention Cells (BC), Green Roofs (GR), and Porous Pavements (PP). Utilizing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method for comparative analysis, the study results identify the most efficacious scenario, S2_1, including RB and BC, which achieves a 19.34% reduction in runoff volume and a 46.53% decrease in peak flow rate, all at the implementation cost of 123,169 USD. A close second, scenario S3_1 incorporating RB and PP, demonstrates a 17% and 46.55% reduction in runoff volume and peak flow at an expenditure of 107,017 USD, respectively. The proposed SWMM-MOEA/D model, in conjunction with TOPSIS, presents a valuable tool for LID planning and optimization, offering decision-makers and relevant entities a pragmatic approach to address the challenges of urban runoff management.

Journal of HydrologyVol. 656
University of Technology Sydney (AU), University of Sistan and Baluchestan (IR), University of Tehran (IR), Norwegian University of Life Sciences (NO), Western Sydney University (AU)
Sustainable cities and communities
Openalex Percentile: Top 2%
Urban Stormwater Management Solutions
9.59
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.