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Title: A Bayesian decision model for optimum investment and design of low-impact development in urban stormwater infrastructure and management
Authors: Wang, Mo
Zhang, Yu
Zhang, Dongqing
Zheng, Yingsheng
Zhou, Shiqi
Tan, Soon Keat
Keywords: Engineering::Civil engineering
Issue Date: 2021
Source: Wang, M., Zhang, Y., Zhang, D., Zheng, Y., Zhou, S. & Tan, S. K. (2021). A Bayesian decision model for optimum investment and design of low-impact development in urban stormwater infrastructure and management. Frontiers in Environmental Science, 9, 713831-.
Journal: Frontiers in Environmental Science
Abstract: Uncertainties concerning low-impact development (LID) practices over its service life are challenges in the adoption of LID. One strategy to deal with uncertainty is to provide an adaptive framework which could be used to support decision-makers in the latter decision on investments and designs dynamically. The authors propose a Bayesian-based decision-making framework and procedure for investing in LID practices as part of an urban stormwater management strategy. In this framework, the investment could be made at various stages of the service life of the LID, and performed with deliberate decision to invest more or suspend the investment, pending the needs and observed performance, resources available, anticipated climate changes, technological advancement, and users’ needs and expectations. Variance learning (VL) and mean-variance learning (MVL) models were included in this decision tool to support handling of uncertainty and adjusting investment plans to maximize the returns while minimizing the undesirable outcomes. The authors found that a risk-neutral investor tends to harbor greater expectations while bearing a higher level of risks than risk-averse investor in the VL model. Constructed wetlands which have a higher prior mean performance are more favorable during the initial stage of LID practices. Risk-averse decision-makers, however, could choose porous pavement with stable performance in the VL model and leverage on potential technological advancement in the MVL model.
ISSN: 2296-665X
DOI: 10.3389/fenvs.2021.713831
Schools: School of Civil and Environmental Engineering 
Rights: © 2021 Wang, Zhang, Zhang, Zheng, Zhou and Tan. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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