Rhodium-SWMM: An open-source tool for green infrastructure placement under deep uncertainty. (May 2023)

• Record Type: Journal Article
• Authors: Tebyanian, Nastaran; Fischbach, Jordan; Lempert, Robert; Knopman, Debra; Wu, Hong; Iulo, Lisa; Keller, Klaus
• Is Part Of: Environmental modelling & software. Volume 163(2023)
• Publication Date: 2023-05
• Subjects: Rhodium-SWMM -- Robust decision making -- Green infrastructure planning -- Uncertainty -- Multi-objective optimization; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2023.105671
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: 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. Highlights: 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.