期刊
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
卷 27, 期 3, 页码 693-704出版社
SPRINGER
DOI: 10.1007/s00477-012-0632-7
关键词
Risk assessment; Agricultural irrigation water; Full-infinite programming; Decision making; Uncertainty
类别
资金
- Natural Sciences Foundation of China [51190095]
- Open Research Fund Program of State Key Laboratory of Hydro-science and Engineering [sklhse-2012-A-03]
- Program for Changjiang Scholars and Innovative Research Team in University [IRT1127]
- Program for New Century Excellent Talents in University [NCET-10-0376]
In recent years, water shortages and unreliable water supplies have been considered as major barriers to agricultural irrigation water management in China, which are threatening human health, impairing prospects for agriculture and jeopardizing survival of ecosystems. Therefore, effective and efficient risk assessment of agricultural irrigation water management is desired. In this study, an inexact full-infinite two-stage stochastic programming (IFTSP) method is developed. It incorporates the concepts of interval-parameter programming and full-infinite programming within a two-stage stochastic programming framework. IFTSP can explicitly address uncertainties presented as crisp intervals, probability distributions and functional intervals. The developed model is then applied to Zhangweinan river basin for demonstrating its applicability. Results from the case study indicate that compromise solutions have been obtained. They provide the desired agricultural irrigation water-supply schemes, which are related to a variety of tradeoffs between conflicting economic benefits and associated penalties attributed to the violation of predefined policies. The solutions can be used for generating decision alternatives and thus help decision makers to identify desired agricultural irrigation targets with maximized system benefit and minimized system-failure risk. Decision makers can adjust the existing agricultural irrigation patterns, and coordinate the conflict interactions among economic benefit, system efficiency, and agricultural irrigation under uncertainty.
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