个人信息Personal Information
教授
博士生导师
硕士生导师
任职 : 副校长、党委常委
性别:男
毕业院校:大连理工大学
学位:博士
所在单位:建设工程学院
学科:水文学及水资源. 人工智能. 计算机应用技术. 软件工程
办公地点:综合实验4号楼 411室
联系方式:0411-84708900
电子邮箱:czhang@dlut.edu.cn
Imprecise probabilistic estimation of design floods with epistemic uncertainties
点击次数:
论文类型:期刊论文
发表时间:2016-06-01
发表刊物:WATER RESOURCES RESEARCH
收录刊物:SCIE、EI
卷号:52
期号:6
页面范围:4823-4844
ISSN号:0043-1397
关键字:decision making; Dempster-Shafer theory; frequency analysis; hydraulic design; imprecise probability; uncertainty
摘要:An imprecise probabilistic framework for design flood estimation is proposed on the basis of the Dempster-Shafer theory to handle different epistemic uncertainties from data, probability distribution functions, and probability distribution parameters. These uncertainties are incorporated in cost-benefit analysis to generate the lower and upper bounds of the total cost for flood control, thus presenting improved information for decision making on design floods. Within the total cost bounds, a new robustness criterion is proposed to select a design flood that can tolerate higher levels of uncertainty. A variance decomposition approach is used to quantify individual and interactive impacts of the uncertainty sources on total cost. Results from three case studies, with 127, 104, and 54 year flood data sets, respectively, show that the imprecise probabilistic approach effectively combines aleatory and epistemic uncertainties from the various sources and provides upper and lower bounds of the total cost. Between the total cost and the robustness of design floods, a clear trade-off which is beyond the information that can be provided by the conventional minimum cost criterion is identified. The interactions among data, distributions, and parameters have a much higher contribution than parameters to the estimate of the total cost. It is found that the contributions of the various uncertainty sources and their interactions vary with different flood magnitude, but remain roughly the same with different return periods. This study demonstrates that the proposed methodology can effectively incorporate epistemic uncertainties in cost-benefit analysis of design floods.