How can learning-by-doing improve decisions in stormwater management? A Bayesian-based optimization model for planning urban green infrastructure investments. (March 2019)

• Record Type: Journal Article
• Authors: Hung, Fengwei; Hobbs, Benjamin F.
• Is Part Of: Environmental modelling & software. Volume 113(2019)
• Page Start: 59
• Page End: 72
• Publication Date: 2019-03
• Subjects: Adaptive management -- Green infrastructure -- Learning -- Stormwater management -- Stochastic programming; 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.2018.12.005
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Urban stormwater management is shifting its attention from traditional centralized engineering solutions to a distributed and greener approach, namely Green Infrastructure (GI). However, uncertainties concerning GI's efficacy for reducing runoff and pollutants are a barrier to the adoption of GI. One strategy to deal with the uncertainty is to implement GI adaptively, in which stormwater managers can learn and adjust their plans over time to avoid undesired outcomes. We propose a new class of GI planning methods based on two-stage stochastic programming and Bayesian learning, which accounts for projected information gains and decision makers' objectives and willingness to accept risk. In the hypothetical example, the model identifies four categories of investment strategies and quantifies their benefits and costs: all-in, greedy investment plus deferral, mixed investments plus deferral, and learn-and-adjust. Which strategy is optimal depends on the user's risk attitudes, and the alternatives' costs and risks. Highlights: A new methodology for adaptive stormwater management under uncertainty is proposed. Risk aversion and future learning are incorporated in the proposed method. Prior distributions are updated by user-defined learning curve functions. The proposed method is modeled and solved as a Mixed Integer Linear Program. Four alternative strategies for GI deployment are identified in the hypothetical example.