Balancing cost-risk in management optimization of water resource systems under uncertainty     

《Physics and Chemistry of the Earth》2012

When a scarce water resource is distributed between different users by a Water Resource Management Authority (WRMA), the replenishment of this resource as well as the meeting of users’ demand is subject to considerable uncertainty. Cost optimization and risk management models can assist the WRMA in its decision about striking the balance between the level of target delivery to the users and the level of risk that this delivery will not be met. Addressing the problem as a multi-period dynamic network optimization, the proposed approach is also based on further developments in stochastic programming for scenario optimization. This approach tries to obtain a “robust” decision policy that minimizes the risk of wrong decisions when managing scarce water resources. In the paper we also illustrate two application examples for water resources management problems.  相似文献   

Two-stage decision-making under uncertainty and stochasticity: Bayesian Programming     

Kenneth W. Harrison 《Advances in water resources》2007

This paper develops a new method for decision-making under uncertainty. The method, Bayesian Programming (BP), addresses a class of two-stage decision problems with features that are common in environmental and water resources. BP is applicable to two-stage combinatorial problems characterized by uncertainty in unobservable parameters, only some of which is resolved upon observation of the outcome of the first-stage decision. The framework also naturally accommodates stochastic behavior, which has the effect of impeding uncertainty resolution. With the incorporation of systematic methods for decision search and Monte Carlo methods for Bayesian analysis, BP addresses limitations of other decision-analytic approaches for this class of problems, including conventional decision tree analysis and stochastic programming. The methodology is demonstrated with an illustrative problem of water quality pollution control. Its effectiveness for this problem is compared to alternative approaches, including a single-stage model in which expected costs are minimized and a deterministic model in which uncertain parameters are replaced by their mean values. A new term, the expected value of including uncertainty resolution, or EVIUR, is introduced and evaluated for the illustrative problem. It is a measure of the worth of incorporating the experimental value of decisions into an optimal decision-making framework. For the illustrative problem, the two-stage adaptive management framework extracted up to approximately 50% of the gains of perfect information. The strength and limitations of the method are discussed and conclusions are presented.  相似文献   

Test application of Bayesian Programming: Adaptive water quality management under uncertainty     

Kenneth W. Harrison 《Advances in water resources》2007

A new method, Bayesian Programming (BP), developed by Harrison [Harrison KW. Multi-stage decision-making under uncertainty and stochasticity: Bayesian Programming. Adv Water Resour, submitted for publication] is tested on a case study involving optimal adaptive management of a river basin. The case study considers anew the process of permitting pulp mills on the Athabasca River in Alberta, Canada. The problem has characteristics common to many environmental management problems. There is uncertainty in the water quality response to pollutant loadings that will not be completely resolved with monitoring and the resolution of this uncertainty is impeded by the stochastic behavior of the water quality system. A two-stage adaptive management process is optimized with BP. Based on monitoring data collected after implementation of the first-stage decision, the uncertainties are updated prior to the second decision stage using Bayesian analysis. The worth of this two-stage adaptive management approach to this problem and the worth of monitoring are evaluated. Conclusions are drawn on the general practicality of BP for adaptive management. Potential strategies are outlined for extending the BP approach to secure further benefits of adaptive management.  相似文献   

A Monte-Carlo game theoretic approach for Multi-Criteria Decision Making under uncertainty   总被引：2，自引：0，他引：2  

Kaveh Madani  Jay R. Lund 《Advances in water resources》2011,34(5):607-616

Game theory provides a useful framework for studying Multi-Criteria Decision Making problems. This paper suggests modeling Multi-Criteria Decision Making problems as strategic games and solving them using non-cooperative game theory concepts. The suggested method can be used to prescribe non-dominated solutions and also can be used as a method to predict the outcome of a decision making problem. Non-cooperative stability definitions for solving the games allow consideration of non-cooperative behaviors, often neglected by other methods which assume perfect cooperation among decision makers. To deal with the uncertainty in input variables a Monte-Carlo Game Theory (MCGT) approach is suggested which maps the stochastic problem into many deterministic strategic games. The games are solved using non-cooperative stability definitions and the results include possible effects of uncertainty in input variables on outcomes. The method can handle multi-criteria multi-decision-maker problems with uncertainty. The suggested method does not require criteria weighting, developing a compound decision objective, and accurate quantitative (cardinal) information as it simplifies the decision analysis by solving problems based on qualitative (ordinal) information, reducing the computational burden substantially. The MCGT method is applied to analyze California’s Sacramento-San Joaquin Delta problem. The suggested method provides insights, identifies non-dominated alternatives, and predicts likely decision outcomes.  相似文献   

Water resources management under dual uncertainties: a factorial fuzzy two-stage stochastic programming approach     

Yang Zhou  Gordon Huang  Shuo Wang  Yuanyuan Zhai  Xiaying Xin 《Stochastic Environmental Research and Risk Assessment (SERRA)》2016,30(3):795-811

In this study we propose a factorial fuzzy two-stage stochastic programming (FFTSP) approach to support water resources management under dual uncertainties. The dual uncertainties in terms of fuzziness in modeling parameters and variability of α-cut levels are taken into account. As different α-cut levels are assigned to each fuzzy parameter (instead of an identical α-cut level), the effects of α-cut levels on fuzzy parameters can be considered. Factorial analysis method is integrated with fuzzy vertex method to tackle the interactive effects of fuzzy parameters within a two-stage stochastic programming framework. The effects of the interactions among fuzzy parameters under various α-cut level combinations can be examined. The FFTSP approach is applied to a water resources management case to demonstrate its applicability. The results show that this approach can not only give various optimized solutions according to decision makers’ confidence levels but also provide in-depth analyses for the effects of fuzzy parameters and their interactions on the solutions. In addition, the results show that the effects of diverse α-cut combinations should not be disregarded because the results may differ under some specific α-cut combinations. The dual sequential factorial analyses embedded in the FFTSP approach guarantee most variations in a system can be analyzed. Therefore water managers are able to gain sufficient knowledge to make robust decisions under uncertainty.  相似文献   

Risk aversion based interval stochastic programming approach for agricultural water management under uncertainty   总被引：1，自引：1，他引：0  

Q.?Q.?Li  Y.?P.?LiEmail author  G.?H.?Huang  C.?X.?Wang 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(3):715-732

In this study, a risk aversion based interval stochastic programming (RAIS) method is proposed through integrating interval multistage stochastic programming and conditional value at risk (CVaR) measure for tackling uncertainties expressed as probability distributions and intervals within a multistage context. The RAIS method can reflect dynamic features of the system conditions through transactions at discrete points in time over the planning horizon. Using the CVaR measure, RAIS can effectively reflect system risk resulted from random parameters. When random events are occurred, the adjustable alternatives can be achieved by setting desired targets according to the CVaR, which could make the revised decisions to minimize the economic penalties. Then, the RAIS method is applied to planning agricultural water management in the Zhangweinan River Basin that is plagued by drought due to serious water scarcity. A set of decision alternatives with different combinations of risk levels employed to the objective function and constraints are generated for planning water resources allocation. The results can not only help decision makers examine potential interactions between risks under uncertainty, but also help generate desired policies for agricultural water management with a maximized payoff and a minimized loss.  相似文献   

Multi-criteria decision making for seismic intensity measure selection considering uncertainty     

Jing Qian  You Dong 《地震工程与结构动力学》2020,49(11):1095-1114

Seismic intensity measure (IM) selection is associated with consideration of multiple criteria, and there are uncertainties within the selection process. In this paper, a novel multi-criteria decision making (MCDM) approach by incorporating stochastic multi-criteria acceptability analysis (SMAA) with technique for order preference by similarity to ideal solution (TOPSIS) is proposed to solve the stochastic decision making problem of IM selection. TOPSIS provides an alternative rank function, and the SMAA is used to address the uncertainties within the IM selection. The performance criteria (e.g., efficiency, proficiency, practicality, sufficiency, and correlation) are evaluated for the investigated structural components, and the decision matrix is formulated based on the criteria of each IM alternative. Furthermore, the importance of the component to system reliability is quantified in a probabilistic manner using nonlinear time history analysis and serves as the weighting factors in MCDM stage. The holistic acceptability indices indicating the overall acceptability levels of IM alternatives are computed by the proposed approach. Additionally, the effects of different IMs (e.g., average spectral acceleration, peak ground velocity, and spectral acceleration) on probabilistic seismic loss and resilience are investigated to further support the IM selection. The proposed approach is illustrated on a highway bridge, and the results are presented.  相似文献   

Two-stage fuzzy chance-constrained programming: application to water resources management under dual uncertainties   总被引：7，自引：7，他引：0  

P. Guo  G. H. Huang 《Stochastic Environmental Research and Risk Assessment (SERRA)》2009,23(3):349-359

In this study, a two-stage fuzzy chance-constrained programming (TFCCP) approach is developed for water resources management under dual uncertainties. The concept of distribution with fuzzy probability (DFP) is presented as an extended form for expressing uncertainties. It is expressed as dual uncertainties with both stochastic and fuzzy characteristics. As an improvement upon the conventional inexact linear programming for handling uncertainties in the objective function and constraints, TFCCP has advantages in uncertainty reflection and policy analysis, especially when the input parameters are provided as fuzzy sets, probability distributions and DFPs. TFCCP integrates the two-stage stochastic programming (TSP) and fuzzy chance-constrained programming within a general optimization framework. TFCCP incorporates the pre-regulated water resources management policies directly into its optimization process to analyze various policy scenarios; each scenario has different economic penalty when the promised amounts are not delivered. TFCCP is applied to a water resources management system with three users. Solutions from TFCCP provide desired water allocation patterns, which maximize both the system’s benefits and feasibility. The results indicate that reasonable solutions were generated for objective function values and decision variables, thus a number of decision alternatives can be generated under different levels of stream flows, α-cut levels and fuzzy dominance indices.  相似文献   

Factorial two-stage stochastic programming for water resources management   总被引：3，自引：3，他引：0  

Y. Zhou  G. H. Huang 《Stochastic Environmental Research and Risk Assessment (SERRA)》2011,25(1):67-78

This study presents a factorial two-stage stochastic programming (FTSP) approach for supporting water resource management under uncertainty. FTSP is developed through the integration of factorial analysis and two-stage stochastic programming (TSP) methods into a general modeling framework. It can handle uncertainties expressed as probability distributions and interval numbers. This approach has two advantages in comparison to conventional inexact TSP methods. Firstly, FTSP inherits merits of conventional inexact two-stage optimization approaches. Secondly, it can provide detailed effects of uncertain parameters and their interactions on the system performance. The developed FTSP method is applied to a hypothetical case study of water resources systems analysis. The results indicate that significant factors and their interactions can be identified. They can be further analyzed for generating water allocation decision alternatives in municipal, industrial and agricultural sectors. Reasonable water allocation schemes can thus be formulated based on the resulting information of detailed effects from various impact factors and their interactions. Consequently, maximized net system benefit can be achieved.  相似文献   

Policy implications of climate change impacts on water resources management     

《Water Policy》1998,1(2):159-175

Policy makers and water resources managers should be aware of the evolving information on climate change impacts as an activity that is preparatory, but not central, to sound decision making on current water resources management actions. Policies that ensure effective contemporary water management will form the core of a “no regrets” strategy that will contemporaneously serve adaptation to climate change and uncertainty. Hence, an “adaptive management” approach rather than an “anticipatory strategy” is warranted for most water management actions. An effective water management system depends, to a large extent, on a well-functioning institutional framework and the treatment of water as an economic and social good, both of which are a prerequisite for adaptation to contemporary climate variability. It will also serve as the foundation for responding to uncertain climate change scenarios.  相似文献   

Risk-based environmental decision-making using fuzzy analytic hierarchy process (F-AHP)   总被引：1，自引：4，他引：1  

Solomon Tesfamariam  Rehan Sadiq 《Stochastic Environmental Research and Risk Assessment (SERRA)》2006,21(1):35-50

Environmental risk management is an integral part of risk analyses. The selection of different mitigating or preventive alternatives often involve competing and conflicting criteria, which requires sophisticated multi-criteria decision-making (MCDM) methods. Analytic hierarchy process (AHP) is one of the most commonly used MCDM methods, which integrates subjective and personal preferences in performing analyses. AHP works on a premise that decision-making of complex problems can be handled by structuring the complex problem into a simple and comprehensible hierarchical structure. However, AHP involves human subjectivity, which introduces vagueness type uncertainty and necessitates the use of decision-making under uncertainty. In this paper, vagueness type uncertainty is considered using fuzzy-based techniques. The traditional AHP is modified to fuzzy AHP using fuzzy arithmetic operations. The concept of risk attitude and associated confidence of a decision maker on the estimates of pairwise comparisons are also discussed. The methodology of the proposed technique is built on a hypothetical example and its efficacy is demonstrated through an application dealing with the selection of drilling fluid/mud for offshore oil and gas operations.  相似文献   

Risk assessment of agricultural irrigation water under interval functions   总被引：2，自引：2，他引：0  

Y. Zhu  Y. P. Li  G. H. Huang  L. Guo 《Stochastic Environmental Research and Risk Assessment (SERRA)》2013,27(3):693-704

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.  相似文献   

Optimization of the industrial structure facing sustainable development in resource-based city subjected to water resources under uncertainty   总被引：2，自引：2，他引：0  

J. J. Gu  P. Guo  G. H. Huang  N. Shen 《Stochastic Environmental Research and Risk Assessment (SERRA)》2013,27(3):659-673

An inexact stochastic fuzzy programming (ISFP) approach has been developed for the optimization of the industrial structure in resource-based city subjected to water resources under uncertainty in present study. The ISFP method incorporates the techniques of inexact stochastic programming and inexact fuzzy chance-constrained programming, where the uncertainties are expressed as interval, fuzzy sets, and probability distribution, respectively. Moreover, it can also examine the risk of violating fuzzy tolerance constraints. The developed method is subsequently employed in a realistic case for industrial development in the Jinchang city, Gansu province, China. The result can help to analyze whether the water resources carrying capacity of Jinchang can meet the need of local economic development plan under uncertainty and help decision maker to optimize the industry structure under water resource constraints to meet the maximum economic efficiency.  相似文献   

Multiple criteria decision making and decision support systems for flood risk management   总被引：1，自引：4，他引：1  

Jason K. Levy 《Stochastic Environmental Research and Risk Assessment (SERRA)》2005,19(6):438-447

Multiple criteria decision making (MCDM) is a collection of methodologies to compare, select, or rank multiple alternatives that typically involve incommensurate attributes. MCDM is well-suited for eliciting and modeling the flood preferences of stakeholders and for improving the coordination among flood agencies, organizations and affected citizens. A flood decision support system (DSS) architecture is put forth that integrates the latest advances in MCDM, remote sensing, GIS, hydrologic models, and real-time flood information systems. The analytic network process (ANP) is discussed with application to short-term flood management options for the middle reaches of the Yangtze River. It is shown that DSS and MCDM can improve flood risk planning and management under uncertainty by providing data displays, analytical results, and model output to summarize critical flood information.  相似文献   

Development of an inexact two-stage stochastic model with downside risk control for water quality management and decision analysis under uncertainty   总被引：3，自引：3，他引：0  

Y. L. Xie  G. H. Huang 《Stochastic Environmental Research and Risk Assessment (SERRA)》2014,28(6):1555-1575

Water quality management along rivers involves making water-allocation plans, establishing water quality goals, and controlling pollutant discharges, which is complicated itself but further challenged by existence of uncertainties. In this study, an inexact two-stage stochastic downside risk-aversion programming (ITSDP) model is developed for supporting regional water resources allocation and water quality management problems under uncertainties. The ITSDP method is a hybrid of interval-parameter programming, two-stage stochastic programming, and downside risk measure to tackle uncertainties described in terms of interval values and probability distributions. A water quality simulation model was provided for reflecting the relationship between the water resources allocation, wastewater discharge, and environmental responses. The proposed approach was applied to a hypothetical case for a shared stream water quality management with one municipal, three industrial and two agricultural sectors. A number of scenarios corresponding to different river inflows and risk levels were examined. The results demonstrated that the model could effectively communicate the interval-format and random uncertainties, and risk-aversion into optimization process, and generate a trade-off between the system economy and stability. They could be helpful for seeking cost-effective management strategies under uncertainties, and gaining an in-depth insight into the water quality management system characteristics, and make cost-effective decisions.  相似文献   

Analyzing urban water supply through an acceptability-index-based interval approach     

X.S. Qin  Y. Xu 《Advances in water resources》2011,34(7):873-886

Incorporation of uncertainties within an urban water supply management system has been a challenging topic for many years. In this study, an acceptability-index-based two-step interval programming (AITIP) model was developed for supporting urban water supply analysis under uncertainty. AITIP improved upon the traditional two-step interval programming (TIP) through incorporating the acceptability level of constraints violation into the optimization framework. A four-layer urban water supply system, including water sources, treatment facilities, reservoirs, and consuming zones, was used to demonstrate the applicability of proposed method. The results indicated that an AITIP model was valuable to help understand the effects of uncertainties related to cost, constraints and decision maker’s judgment in the water supply network, and capable of assisting urban water managers gain an in-depth insight into the tradeoffs between system cost and constraints-violation risk. Compared with TIP, the solutions from AITIP were of lower degree of uncertainty, making it more reliable to identify effective water supply patterns by adjusting decision variable values within their solution intervals. The study is useful in helping urban water managers to identify cost-effective management schemes in light of uncertainties in hydrology, environment, and decisions. The proposed optimization approach is expected to be applicable for a wide variety of water resources management problems.  相似文献   

An interval-parameter multi-stage stochastic programming model for water resources management under uncertainty   总被引：1，自引：0，他引：1  

Y.P. Li  G.H. Huang  S.L. Nie 《Advances in water resources》2006

In this study, an interval-parameter multi-stage stochastic linear programming (IMSLP) method has been developed for water resources decision making under uncertainty. The IMSLP is a hybrid methodology of inexact optimization and multi-stage stochastic programming. It has three major advantages in comparison to the other optimization techniques. Firstly, it extends upon the existing multi-stage stochastic programming method by allowing uncertainties expressed as probability density functions and discrete intervals to be effectively incorporated within the optimization framework. Secondly, penalties are exercised with recourse against any infeasibility, which permits in-depth analyses of various policy scenarios that are associated with different levels of economic consequences when the promised water-allocation targets are violated. Thirdly, it cannot only handle uncertainties through constructing a set of scenarios that is representative for the universe of possible outcomes, but also reflect dynamic features of the system conditions through transactions at discrete points in time over the planning horizon. The developed IMSLP method is applied to a hypothetical case study of water resources management. The results are helpful for water resources managers in not only making decisions of water allocation but also gaining insight into the tradeoffs between environmental and economic objectives.  相似文献   

A stochastic optimization framework for the restoration of an over-exploited aquifer     

N. Mylopoulos  P. Sidiropoulos 《水文科学杂志》2016,61(9):1691-1706

ABSTRACT

This study investigates the impact of hydraulic conductivity uncertainty on the sustainable management of the aquifer of Lake Karla, Greece, using the stochastic optimization approach. The lack of surface water resources in combination with the sharp increase in irrigation needs in the basin over the last 30 years have led to an unprecedented degradation of the aquifer. In addition, the lack of data regarding hydraulic conductivity in a heterogeneous aquifer leads to hydrogeologic uncertainty. This uncertainty has to be taken into consideration when developing the optimization procedure in order to achieve the aquifer’s sustainable management. Multiple Monte Carlo realizations of this spatially-distributed parameter are generated and groundwater flow is simulated for each one of them. The main goal of the sustainable management of the ‘depleted’ aquifer of Lake Karla is two-fold: to determine the optimum volume of renewable groundwater that can be extracted, while, at the same time, restoring its water table to a historic high level. A stochastic optimization problem is therefore formulated, based on the application of the optimization method for each of the aquifer’s multiple stochastic realizations in a future period. In order to carry out this stochastic optimization procedure, a modelling system consisting of a series of interlinked models was developed. The results show that the proposed stochastic optimization framework can be a very useful tool for estimating the impact of hydraulic conductivity uncertainty on the management strategies of a depleted aquifer restoration. They also prove that the optimization process is affected more by hydraulic conductivity uncertainty than the simulation process.