Guided adaptive optimal decision making approach for uncertainty based   watershed scale load reduction

doi:10.1016/j.watres.2011.06.038

CORC > 北京大学 > 环境科学与工程学院

	Guided adaptive optimal decision making approach for uncertainty based watershed scale load reduction
	Liu, Yong ; Zou, Rui ; Riverson, John ; Yang, Pingjian ; Guo, Huaicheng
刊名	水研究
	2011
关键词	Adaptive management Guided adaptive optimal approach Risk explicit interval linear programming Load reduction Lake Qionghai Watershed LINEAR-PROGRAMMING APPROACH MANAGEMENT FISHERIES
DOI	10.1016/j.watres.2011.06.038
英文摘要	Previous optimization-based watershed decision making approaches suffer two major limitations. First of all, these approaches generally do not provide a systematic way to prioritize the implementation schemes with consideration of uncertainties in the watershed systems and the optimization models. Furthermore, with adaptive management, both the decision environment and the uncertainty space evolve (1) during the implementation processes and (2) as new data become available. No efficient method exists to guide optimal adaptive decision making, particularly at a watershed scale. This paper presents a guided adaptive optimal (GAO) decision making approach to overcome the limitations of the previous methods for more efficient and reliable decision making at the watershed scale. The GAO approach is built upon a modeling framework that explicitly addresses system optimality and uncertainty in a time variable manner, hence mimicking the real-world decision environment where information availability and uncertainty evolve with time. The GAO approach consists of multiple components, including the risk explicit interval linear programming (REILP) modeling framework, the systematic method for prioritizing implementation schemes, and an iterative process for adapting the core optimization model for updated optimal solutions. The proposed approach was illustrated through a case study dealing with the uncertainty based optimal adaptive environmental management of the Lake Qionghai Watershed in China. The results demonstrated that the proposed GAO approach is able to (1) efficiently incorporate uncertainty into the formulation and solution of the optimization model, and (2) prioritize implementation schemes based on the risk and return tradeoff. As a result the GAO produces more reliable and efficient management outcomes than traditional non-adaptive optimization approaches. (C) 2011 Elsevier Ltd. All rights reserved.; http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000295386800019&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701 ; Engineering, Environmental; Environmental Sciences; Water Resources; SCI(E); EI; PubMed; SSCI; 5; ARTICLE; 16; 4885-4895; 45
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/158865]
专题	环境科学与工程学院
推荐引用方式 GB/T 7714	Liu, Yong,Zou, Rui,Riverson, John,et al. Guided adaptive optimal decision making approach for uncertainty based watershed scale load reduction[J]. 水研究,2011.
APA	Liu, Yong,Zou, Rui,Riverson, John,Yang, Pingjian,&Guo, Huaicheng.(2011).Guided adaptive optimal decision making approach for uncertainty based watershed scale load reduction.水研究.
MLA	Liu, Yong,et al."Guided adaptive optimal decision making approach for uncertainty based watershed scale load reduction".水研究 (2011).