A risk-based interactive multi-stage stochastic programming approach for water resources planning under dual uncertainties

Wang, Y. Y.; Huang, Guohe; Wang, Shuo; Li, W.; Guan, Panbo

doi:10.1016/j.advwatres.2016.05.011

Cited by 26 publications

(10 citation statements)

References 38 publications

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“…A chance constraint programming model includes groups of constraints, which do not need to be satisfied in all possible future scenarios. Decision-makers specify the probabilities with which constraints are satisfied individually [74,75,108,110,135,171,172,203,234] or jointly [72,73,119,137,228,231,244]. Closely related to this approach, Dong et al [44] propose a stochastic programming model with two objectives: maximising system benefits and maximising the probability that the constraints are satisfied.…”

Section: Water Allocationmentioning

confidence: 99%

Review of Mathematical Programming Applications in Water Resource Management Under Uncertainty

Archibald

Marshall

2018

Environ Model Assess

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Economic development, variation in weather patterns and natural disasters focus attention on the management of water resources. This paper reviews the literature on the development of mathematical programming models for water resource management under uncertainty between 2010 and 2017. A systematic search of the academic literature identified 448 journal articles on water resource management for examination. Bibliometric analysis is employed to investigate the methods that researchers are currently using to address this problem and to identify recent trends in research in the area. The research reveals that stochastic dynamic programming and multistage stochastic programming are the methods most commonly applied. Water resource allocation, climate change, water quality and agricultural irrigation are amongst the most frequently discussed topics in the literature. A more detailed examination of the literature on each of these topics is included. The findings suggest that there is a need for mathematical programming models of large-scale water systems that deal with uncertainty and multiobjectives in an effective and computationally efficient way.

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Section: Water Allocationmentioning

confidence: 99%

Review of Mathematical Programming Applications in Water Resource Management Under Uncertainty

Archibald

Marshall

2018

Environ Model Assess

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“…Global water resource issues are becoming increasingly serious owing to rapid economic development, population growth, and urban expansion [1]. Disparities between increased demand and limited water supply are, thus, intensifying and putting great pressure on sustainable water resource management [2,3]. When water shortages occur, residents' living standards, economic development, and the ecological environment may be affected [4,5].…”

Section: Motivationmentioning

confidence: 99%

A Scenario-Based Optimization Model for Planning Sustainable Water-Resources Process Management under Uncertainty

Miao

Zuo

et al. 2019

Processes

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Discrepancies between water demand and supply are intensifying and creating a need for sustainable water resource process management associated with rapid economic development, population growth, and urban expansion. In this study, a scenario-based interval fuzzy-credibility constrained programming (SIFCP) method is developed for planning a water resource management system (WRMS) that can handle uncertain information by using interval values, fuzzy sets, and scenario analysis. The SIFCP-WRMS model is then applied to plan the middle route of the South-to-North Water Diversion Project (SNWDP) in Henan Province, China. Solutions of different water distribution proportion scenarios and varied credibility levels are considered. Results reveal that different water-distribution proportion scenarios and uncertainties used in the SIFCP-WRMS model can lead to changed water allocations, sewage discharges, chemical oxygen demand (COD) emissions, and system benefits. Results also indicate that the variation of scenarios (i.e., from S2 to S3) can result in a change of 9% over the planning horizon for water allocation in the industrial sector. Findings can help decision-makers resolve conflicts among economic objective, water resource demand, and sewage discharge, as well as COD emissions.

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“…CCP has traditionally been employed in dam reservoirs capacity optimization and storage-yield-reliability analysis (Mousavi et al, 2014), water allocations (Liu et al, 2016;Wang et al, 2016a), agricultural water management (Guo et al, 2014), and watershed-scale agriculture nonpoint source pollution management (Wang et al, 2016b). Simulation-based CCP has also been implemented, where Monte Carlo simulation and evolutionary optimization are coupled in agricultural water quality management (Xu & Qin, 2010) and groundwater remediation problems (He et al, 2008).…”

Section: Water Resources Researchmentioning

confidence: 99%

Copula‐Based Chance‐Constrained Hydro‐Economic Optimization Model for Optimal Design of Reservoir‐Irrigation District Systems under Multiple Interdependent Sources of Uncertainty

Alizadeh

Mousavi

Ponnambalam

2018

Water Resources Research

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This paper presents a new explicit stochastic hydro‐economic optimization model for reservoir‐irrigation district systems design affected by multiple interdependent sources of uncertainties. The model solution determines both optimal design and long‐term operation policies of the systems while accounting for uncertainties of reservoir inflow, rainfall, crop yield, crop price, and production costs. The problem is formulated as a chance‐constrained program in which the dependence structure of discrete and continuous random coefficients is accounted for using copula. In addition to design variables such as the sizing of reservoir capacity and the irrigable area, optimal reservoir operation policies and irrigation management strategies are determined. The model performance and significance is tested in the case study of Chamshir hydrosystem in Iran, consisting of Chamshir Dam and downstream irrigation districts. Various scenarios assessing baseline conditions, risk attitude, and the impacts of change in climatic inputs and upstream conditions are also evaluated. The model results are used to quantify the interrelationships and trade‐offs among optimal values of design parameters, optimal irrigation policy, decision‐makers' risk‐attitude, uncertainty levels of agro‐economic factors, and target reliability levels of meeting crop yield and water requirement. The results demonstrate that smaller sizes of the reservoir and the irrigation district and stress‐avoidance irrigation policies, rather than deficit‐irrigation policies, are preferred when the decision maker is risk‐averse and uncertainties in agro‐economic factors and reliability levels are large. Additionally, we show how the optimal cropping pattern and irrigation strategies are affected by climate change‐induced rainfall variations and the alteration of conditions upstream of the project.

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A risk-based interactive multi-stage stochastic programming approach for water resources planning under dual uncertainties

Cited by 26 publications

References 38 publications

Review of Mathematical Programming Applications in Water Resource Management Under Uncertainty

Review of Mathematical Programming Applications in Water Resource Management Under Uncertainty

A Scenario-Based Optimization Model for Planning Sustainable Water-Resources Process Management under Uncertainty

Copula‐Based Chance‐Constrained Hydro‐Economic Optimization Model for Optimal Design of Reservoir‐Irrigation District Systems under Multiple Interdependent Sources of Uncertainty

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