A Fair Approach for Multi-Objective Water Resources Allocation

2019 ◽  
Vol 33 (10) ◽  
pp. 3633-3653 ◽  
Author(s):  
Jing Tian ◽  
Shenglian Guo ◽  
Dedi Liu ◽  
Zhengke Pan ◽  
Xingjun Hong
2015 ◽  
Vol 15 (4) ◽  
pp. 817-824 ◽  
Author(s):  
Jing Peng ◽  
Ximin Yuan ◽  
Lan Qi ◽  
Qiliang Li

Water resources supply and demand has become a serious problem. Water resources allocation is usually a multi-objective problem, and has been of concern for many researchers. In the north of China, the lack of water resources in the Huai River Basin has handicapped the development of the economy, especially badly in the low-flow period. So it is necessary to study water resources allocation in this area. In this paper, a multi-objective dynamic water resources allocation model has been developed. The developed model took the overall satisfaction of water users in a time interval as the objective function, applied an improved simplex method to solve the calculation, considered the overall users' satisfaction variation with time, and followed the principle that the variation of the system satisfaction within adjacent periods of time must be minimal. The established model was then applied to the Huai River, for the present situation (2010), short-term (2020) and long-term (2030) planning timeframes. From the calculation results, the overall satisfaction in late May and mid September in 2030 was 0.65 and 0.70. After using the model allocation optimization, the overall satisfaction was improved, increasing to 0.78 and 0.79, respectively, thus achieving the dynamic balance optimization of water resources allocation in time and space. This model can provide useful decision support in water resources allocation, when it is used to alleviate water shortages occurring in the low-flow period.


2020 ◽  
Vol 12 (4) ◽  
pp. 1337 ◽  
Author(s):  
Junfei Chen ◽  
Cong Yu ◽  
Miao Cai ◽  
Huimin Wang ◽  
Pei Zhou

With the rapid increase of water demand in urban life, ecology and production sectors, the problem of water resources allocation has become increasingly prominent. It has hindered the sustainable development of urban areas. Based on the supply of various water sources and the water demand of different water users, a multi-objective optimal allocation model for urban water resources was proposed. The model was solved using the algorithm of particle swarm optimization (PSO). The algorithm has a fast convergence and is both simple and efficient. In this paper, the conflict over Kunming’s water resources allocation was taken as an example. The PSO algorithm was used to obtain optimized water resources allocation plans in the year 2020 and 2030, under the circumstances of a dry year (inflow guarantee rate p = 0.825) and an unusually dry year (inflow guarantee rate p = 0.885), respectively. The results showed that those allocation plans can lower the future potential water shortage rates of Kunming. At the same time, the interests of different sectors can all be satisfied. Therefore, conflicts over urban water use can be effectively alleviated.


Water ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1421
Author(s):  
Jisi Fu ◽  
Ping-An Zhong ◽  
Bin Xu ◽  
Feilin Zhu ◽  
Juan Chen ◽  
...  

Transboundary water resources allocation is an effective measure to resolve water-related conflicts. Aiming at the problem of water conflicts, we constructed water resources allocation models based on game theory and multi-objective optimization, and revealed the differences between the two models. We compare the Pareto front solved by the AR-MOEA method and the NSGA-II method, and analyzed the difference between the Nash–Harsanyi Leader–Follower game model and the multi-objective optimization model. The Huaihe River basin was selected as a case study. The results show that: (1) The AR-MOEA method is better than the NSGA-II method in terms of the diversity metric (Δ); (2) the solution of the asymmetric Nash–Harsanyi Leader–Follower game model is a non-dominated solution, and the asymmetric game model can obtain the same water resources allocation scheme of the multi-objective optimal allocation model under a specific preference structure; (3) after the multi-objective optimization model obtains the Pareto front, it still needs to construct the preference information of the Pareto front for a second time to make the optimal solution of a multi-objective decision, while the game model can directly obtain the water resources allocation scheme at one time by participating in the negotiation. The results expand the solution method of water resources allocation models and provide support for rational water resources allocation.


1981 ◽  
Vol 14 (2) ◽  
pp. 3901-3906
Author(s):  
Y. Hagihara ◽  
K. Hagihara ◽  
Y. Nakagawa ◽  
H. Watanabe

2015 ◽  
Vol 29 (7) ◽  
pp. 2303-2321 ◽  
Author(s):  
Hojjat Mianabadi ◽  
Erik Mostert ◽  
Saket Pande ◽  
Nick van de Giesen

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