A Copula-based interval linear programming model for water resources allocation under uncertainty

Yue, Wencong; Yu, Shujie; Xu, Meng; Rong, Qiangqiang; Xu, Chao; Su, Meirong

doi:10.1016/j.jenvman.2022.115318

Cited by 17 publications

(4 citation statements)

References 58 publications

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“…For the optimization solution, there are two types of existing optimization algorithms. Among them, traditional optimization algorithms have strong local search ability, resulting in a local optimum, so their application scope is greatly limited, including linear programming [10][11][12], nonlinear programming [13][14][15], dynamic programming [16][17][18][19][20][21], etc. Intelligent optimization algorithms are constructed by intuition or experience, including the genetic algorithm [22][23][24], the ant colony algorithm [25][26][27][28], the artificial neural network algorithm [29,30], and some new algorithms, such as the artificial immune algorithm [31], the differential evolution algorithm [32], the whale optimization algorithm [33], etc.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimal Operation Decision for Parallel Reservoirs Based on NSGA-II-TOPSIS-GCA Algorithm: A Case Study in the Upper Reach of Hanjiang River

Wei,

Peng,

et al. 2024

Applied Sciences

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The parallel reservoirs in the upper reach of the Hanjiang River are key projects for watershed management, development, and protection. The optimal operation of parallel reservoirs is a multiple-stage, multiple-objective, and multiple-decision attributes complex decision problem. Taking Jiaoyan–Shimen parallel reservoirs as an example, a method of multi-objective optimal operation decision of parallel reservoirs (MOODPR) was proposed. The multi-objective optimal operation model (MOOM) was constructed. The new algorithm coupling NSGA-II, TOPSIS, and GCA was used to solve the MOODPR problem. The method of MOODPR was formed by coupling problem identification, model construction, an optimization solution, and scheme evaluation. The results show that (1) combining the Euclidean distance with the grey correlation degree to construct a new hybrid closeness degree makes the multi-attribute decision making method more scientific and feasible. (2) The NSGA-II-TOPSIS-GCA algorithm is applied to obtain decision schemes, which provide decision support for management. (3) It can be seen from the Pareto chart that for the Jiaoyan–Shimen parallel reservoirs, the comprehensive water supply was negatively related to ecology. (4) The comprehensive water supply and ecological AAPFD value in the extraordinarily dry year was 4.212 × 108 m3 and 4.953. The number of maximum continuous water shortage periods was 4 and 6. The maximum ten-day water shortage was 4.46 × 107 m3 and 2.3 × 106 m3. The research results provide technical support and reference value to multi-objective optimal operation decisions for parallel reservoirs in the upper reach of the Hanjiang River.

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Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimal Operation Decision for Parallel Reservoirs Based on NSGA-II-TOPSIS-GCA Algorithm: A Case Study in the Upper Reach of Hanjiang River

Wei,

Peng,

et al. 2024

Applied Sciences

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“…For defense resource allocation, Dong et al (2021) proposes an optimization model that incorporates forecast information and the subject's risk tolerance to obtain the optimal risk control scheme. In terms of agricultural water management, considering the heterogeneous risk tolerance of decisionmakers for water scarcity, Yue et al (2022) proposed an interval linear programming model to identify the nonstationary disturbance of water allocation in irrigation areas. Delorit and Block (2020) explored the influence of variable hydrology and farmers' heterogeneous risk attitudes on the cooperative behavior of WOT.…”

Section: Introductionmentioning

confidence: 99%

Assessing the option water‐saving willingness of irrigation areas considering farmers’ risk tolerance

Cai

Yao

2023

Risk Analysis

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Water option trading could facilitate water conservation in irrigation areas to achieve optimal allocation of agricultural water resources. However, the risk associated with water‐saving decisions increases due to the uncertainties of tradeable water and water‐saving benefits, which makes farmers in the irrigation area with heterogeneous risk tolerances exhibit varied option water‐saving willingness (OWSW) in response to the water option contract. Thus, this article provides a novel framework for prior assessing the OWSW in the irrigated area that considers farmers’ heterogeneous risk tolerance and proposes the optimal contractual water demand to stimulate the OWSW. First, a multiobjective optimal allocation model for cropping water is constructed to predict tradeable water, and then risk trust, risk‐return perception and reference are integrated into water‐saving return analysis for proposing a willingness calculation model involving forecast information. Finally, the influence of heterogeneous risk tolerance on farmers’ water‐saving path choices and the irrigation area's OWSW is analyzed with three sets of comparative data from 2014 to 2021. Results indicate that the intensity and stability of OWSW in water‐scarce irrigation areas increase as farmers’ risk tolerance rises, but the enhancement utility exhibits a diminishing marginal trend. When both prediction accuracy and farmers’ risk tolerance are low, contracts with relatively adventurous and differentiated water demands are more likely to stimulate OWSW. This study provides insights into activating water options trading and stimulating water conservation in agriculture from a risk management perspective.

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“…The optimization theories and methods can be divided into three categories: optimization methods based on mathematical theory, optimization methods based on evolutionary theory, and hybrid optimization methods [20]. The optimization methods based on mathematical theory include linear programming (LP) [21,22], nonlinear programming (NP) [23,24], dynamic programming (DP) [25][26][27], and large-scale system decomposition-coordination (LSSDC) [28]. The optimization methods based on evolutionary theory have developed rapidly in recent years, and include the non-dominated sorting genetic algorithm II (NSGA-II) [29][30][31], ant colony optimization (ACO) [32,33], the artificial bee colony algorithm (ABCA) [34], particle swarm optimization (PSO) [35][36][37], the artificial neural network (ANN) [38,39], and the simulated annealing algorithm (SAA) [40].…”

Section: Introductionmentioning

confidence: 99%

Multi-Stakeholder Coordinated Operation of Reservoir Considering Irrigation and Ecology

Wei

Lu³

et al. 2022

Water

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In traditional ecological operation, it is difficult to coordinate the balance among the interests of stakeholders, and stakeholders find it difficult to accept the operation scheme. To address these problems, this study proposed a method of multi-stakeholder coordinated operation of reservoir (MSCOR). By comprehensively considering the interest demands of stakeholders, the multi-stakeholder interval coordination mechanism (MSICM) for reservoir operation was established. The multi-stakeholder coordinated operation model (MSCOM) was constructed. The multi-stakeholder solution algorithm based on the MSICM, the non-dominated sorting genetic algorithm II, and the approach of successive elimination of alternative schemes based on the k-order and p-degree of efficiency (MSIC-NSGA-II-SEABODE) were applied to solve the MSCOR problem. The coordination mechanism, model construction, multi-stakeholder optimization, and multi-attribute decision making were coupled to establish a multi-stakeholder coordinated operation method, comprising the whole process of mechanism–modeling–optimization–decision making. Taking Baojixia Reservoir as an example, the performance of the coordinated operation method was compared with that of the traditional optimal operation method, and the relationship between the irrigation benefits and ecological benefits of the reservoir was explored. The results show that: (1) On the premise of the same satisfaction degree of basic irrigation interests, the ecological AAPFD value of coordinated operation decreased by 0.184, 0.469, and 0.886 in a normal year, dry year, and extraordinary dry year, respectively. The effect of coordinated operation on balancing various stakeholders was more obvious with the decrease in water inflow. (2) The MSICM ensures that the multi-stakeholder operation of the reservoir conforms to the principles of comprehensiveness, balance, and sustainability. (3) The coordination scheme obtained by the MSIC-NSGA-II-SEABODE algorithm is more reasonable and feasible. The research results provide a new idea and method to address the MSCOR problem.

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A Copula-based interval linear programming model for water resources allocation under uncertainty

Cited by 17 publications

References 58 publications

Multi-Objective Optimal Operation Decision for Parallel Reservoirs Based on NSGA-II-TOPSIS-GCA Algorithm: A Case Study in the Upper Reach of Hanjiang River

Multi-Objective Optimal Operation Decision for Parallel Reservoirs Based on NSGA-II-TOPSIS-GCA Algorithm: A Case Study in the Upper Reach of Hanjiang River

Assessing the option water‐saving willingness of irrigation areas considering farmers’ risk tolerance

Multi-Stakeholder Coordinated Operation of Reservoir Considering Irrigation and Ecology

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