SWMMLIDopt : a tool for optimization of low-impact development (LID) measures using the SWMM model
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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en
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17
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Journal of Hydroinformatics, Volume 26, issue 11, pp. 2781-2797
Abstract
The present study introduces the SWMMLIDopt tool that aims to optimize the implementation of low-impact development (LID) measures for stormwater management. The developed tool utilizes the capabilities of the stormwater management model (SWMM) and provides a user-friendly interface for conducting multi-objective optimization of the selection and placement of LID measures in urban catchments. The features and capabilities of the developed optimization tool are demonstrated on two urban catchments in Northern Europe, Norway and Finland. The catchments exhibit different catchment sizes and characteristics, complexities and constraints demonstrating the flexibility of the tool. The selection and placement of LID measures within the study catchments were optimized according to two criteria: (1) minimizing the peak flow and (2) minimizing the cost. The generated Pareto-fronts for the study catchments allow for the evaluation of optimal scenarios based on the trade-offs between the two selected criteria. The results obtained using SWMMLIDopt demonstrate its efficacy in optimizing LID measures in urban stormwater management that will aid practitioners in designing cost-effective and climate resilient infrastructure.Description
SOFTWARE AVAILABILITY STATEMENT The source codes and tools developed during this research are freely and publicly accessible under the GNU General Public License. They are hosted on GitHub at https://github.com/ElhadiMohsenAbdalla/SWMMLIDOPT and are also available through the EviBAN toolbox for adaptive stormwater management at https://www.sintef.no/projectweb/eviban/toolbox/.
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Abdalla, E M H, Sivertsen, E, Tamm, O, Werner, A, Koivusalo, H & Helness, H 2024, 'SWMMLIDopt : a tool for optimization of low-impact development (LID) measures using the SWMM model', Journal of Hydroinformatics, vol. 26, no. 11, pp. 2781-2797. https://doi.org/10.2166/hydro.2024.123