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An Inexact Two-Stage Stochastic Dependent-Chance Programming Model for Water Resources Management
| Content Provider | Semantic Scholar |
|---|---|
| Author | Ha, Minghu Zhang Song, Jianwei |
| Copyright Year | 2019 |
| Abstract | In order to optimize the allocation of water resources, an inexact two-stage stochastic dependent-chance programming model, which integrates stochastic dependent-chance programming, two-stage programming and interval programming, is given. Compared with the existing other water resources management models, this model reflects the dynamic characteristics and randomness of the water resource management system, emphasizes the importance of water users, and maximizes the probability of achieving the required economic goals set by the water manager. In order to solve the model with the data of multiply distributed stochastic boundaries, a hybrid algorithm, which incorporates stochastic simulation, back propagation neural network, and genetic algorithm, is proposed. Finally, the model is applied to a case study of Handan City’s water resources management in 2024 and 2025, through which the optimized water-allocation in Handan City is realized. © 2019 World Academic Press, UK. All rights reserved. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.worldacademicunion.com/journal/jus/jusVol13No1paper02.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
