Implications Of Model Uncertainty For Investment Decisions To Manage Intermittent Sewer Overflows

WATER RESEARCH(2021)

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摘要
Uncertainty in urban drainage modelling studies presents challenges to decision makers with limited investment resources attempting to achieve regulatory compliance for intermittent discharges from Combined Sewer Overflows. This paper presents the development of a new decision-making approach to address two key challenges encountered when attempting to manage sewer overflows, these are (i) the implications of different risk preferences of individuals for investment decisions; and (ii) how to utilize information on uncertainties in system performance predictions due to input or parameter uncertainty while comparing decision alternatives. The developed decision-making approach uses a multi-objective decision formulation to analyse the trade-off between investment and predicted system performance under uncertainty while accounting for risk preferences of the individual decision maker. The proposed uncertainty based decision-making approach is able to incorporate any threshold-based regulatory criteria for intermittent sewer overflows and is illustrated using a case study catchment in Luxembourg. The results from this case study highlight the significant impact of individuals' risk preferences on the level of investment recommended to comply with threshold-based regulatory criteria. It was demonstrated that differing levels of risk-averseness can result in a substantial increase in investment cost for solutions that are regulatory compliant. This paper demonstrates the need for water companies to rigorously define a corporate risk preference strategy to ensure consistent investment decisions across their operations; otherwise, individual preferences may cause significant over-investment to meet the same regulatory goals.(c) 2021 Elsevier Ltd. All rights reserved.
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关键词
Buffered probability of exceedance, Intermittent sewer overflows, Investment cost, Risk preference, Decision making under modelling&amp, nbsp, uncertainty
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