Rhodium-SWMM: An open-source tool for green infrastructure placement under deep uncertainty

Green Infrastructure (GI) measures are increasingly used for climate adaptation in urban areas, but it remains a challenge to evaluate their effectiveness and strategically allocate investment. Planning GI is subject to deep uncertainties and requires navigating tradeoffs between multiple objectives...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Environmental modelling & software : with environment data news Ročník 163; s. 105671
Hlavní autoři: Tebyanian, Nastaran, Fischbach, Jordan, Lempert, Robert, Knopman, Debra, Wu, Hong, Iulo, Lisa, Keller, Klaus
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.05.2023
Témata:
climate
computer software
green infrastructure
Green infrastructure planning
Multi-objective optimization
rhodium
Rhodium-SWMM
Robust decision making
stormwater management
system optimization
Uncertainty
United States Environmental Protection Agency
Robust decision making
Uncertainty
Green infrastructure planning
Multi-objective optimization
Rhodium-SWMM
ISSN:1364-8152, 1873-6726
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Green Infrastructure (GI) measures are increasingly used for climate adaptation in urban areas, but it remains a challenge to evaluate their effectiveness and strategically allocate investment. Planning GI is subject to deep uncertainties and requires navigating tradeoffs between multiple objectives. Many-Objective Robust Decision Making (MORDM) can be useful in addressing these modeling challenges. Thus far, MORDM has been used sparsely for GI planning. To help mainstream MORDM applications in GI planning, we developed an open-source Python library: Rhodium-SWMM. Rhodium-SWMM connects the USEPA's Stormwater Management Model (SWMM) to Rhodium, a Python library for MORDM. Rhodium-SWMM provides a generalizable and flexible interface for taking SWMM input files and setting up a multi-objective optimization problem with the ability to define a wide range of parameters in the SWMM input file as uncertainties or levers. This opens opportunities to more conveniently analyze new research questions in multi-scale GI placement under deep uncertainty. •We develop an open-source python library: Rhodium-SWMM.•Rhodium-SWMM facilitates green infrastructure planning under deep uncertainty.•We illustrate the software use using a green infrastructure planning example problem.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1364-8152
1873-6726
DOI:10.1016/j.envsoft.2023.105671