The Application and Potential of Multi-Objective Optimization Algorithms in Decision-Making for LID Facilities Layout

• Published in: Water resources management Vol. 38; no. 14; pp. 5403 - 5417
• Main Authors: Xie, Yuanyuan, Wang, Haiyan, Wang, Kaiyi, Ge, Xiaoyu, Ying, Xin
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.11.2024; Springer Nature B.V
• Subjects: Algorithms; Atmospheric Sciences; bibliometric analysis; Bibliometrics; Civil Engineering; Collaboration; Computer science; data visualization; Decision making; Earth and Environmental Science; Earth Sciences; Environment; Environmental risk; Facilities management; Floods; Genetic algorithms; Geotechnical Engineering & Applied Earth Sciences; Green infrastructure; Hydrogeology; Hydrology/Water Resources; Keywords; Landscape architecture; Legal documents; Linear programming; Multiple objective analysis; Optimization; Optimization algorithms; Planning; Plant layout; Runoff; Scientific visualization; Storm runoff; Stormwater; Stormwater management; Stormwater runoff; Trends; Urban environments; Urban runoff; Water management; Water quality; Water resources; Algorithm Optimization; Multi-objective Optimization; Genetic Algorithms; Layout Decision; Low-impact Development
• ISSN: 0920-4741; 1573-1650
• DOI: 10.1007/s11269-024-03926-5

Low-impact development (LID) practices are critical for mitigating urban stormwater runoff and alleviating flood risks. The strategic placement of LID facilities is paramount to optimizing their efficacy within urban landscapes. This study conducts a comprehensive bibliometric analysis of LID-related literature over the past decade, utilizing data visualization tools to elucidate key disciplines, publication trends, and the prevalence of various optimization algorithms. We delve into the application of multi-objective optimization (MOO) algorithms in LID facility layout, examining their practical applications, theoretical underpinnings, and case studies. The paper also scrutinizes the strengths and limitations of these algorithms, proposing future research trajectories that leverage MOO to enhance LID’s role in urban stormwater management.