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<p>In this paper, we first establish the model of optimal allocation of water resources within the region and convert it into a dynamic planning problem. Then, under drought conditions, the models of optimal allocation of intra-regional water resources among industries and different crops are established, respectively, and the water production function model is established using sensitivity coefficients. Under the condition of satisfying the water quantity opportunity constraints and with the objective of ensuring obtaining a higher system return, the water resources optimal allocation model based on interval multi-stage stochastic opportunity constraint planning is established by combining multi-stage planning, interval planning and stochastic opportunity constraint planning. A hybrid intelligent algorithm combining stochastic simulation, multilayer neural networks, and genetic algorithms is used to solve the model. A case study of optimal water resource allocation is used to verify the feasibility and validity of the proposed model and algorithm. Under the traditional water resource allocation scheme, the total water supply and water shortage of Nandan County are 0.97 and 0.58 billion m<sup>3</sup>, and the water shortage rate is 37.55%. Under the optimized water resources allocation scheme of this paper, the water shortage of Nandan County is 0.05 billion m³, and the water shortage rate is 6.09%. The model of this paper’s allocation scheme can effectively meet the water demand.</p>
</abstract>

In this paper, we first establish the model of optimal allocation of water resources within the region and convert it into a dynamic planning problem. Then, under drought conditions, the models of optimal allocation of intra-regional water resources among industries and different crops are established, respectively, and the water production function model is established using sensitivity coefficients. Under the condition of satisfying the water quantity opportunity constraints and with the objective of ensuring obtaining a higher system return, the water resources optimal allocation model based on interval multi-stage stochastic opportunity constraint planning is established by combining multi-stage planning, interval planning and stochastic opportunity constraint planning. A hybrid intelligent algorithm combining stochastic simulation, multilayer neural networks, and genetic algorithms is used to solve the model. A case study of optimal water resource allocation is used to verify the feasibility and validity of the proposed model and algorithm. Under the traditional water resource allocation scheme, the total water supply and water shortage of Nandan County are 0.97 and 0.58 billion m3, and the water shortage rate is 37.55%. Under the optimized water resources allocation scheme of this paper, the water shortage of Nandan County is 0.05 billion m³, and the water shortage rate is 6.09%. The model of this paper’s allocation scheme can effectively meet the water demand.

Construction of an intelligent optimization model for water resource allocation under drought conditions

Xi’an University of Science and Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In this paper, we first establish the model of optimal allocation of water resources within the region and convert it into a dynamic planning problem. Then,...

Construction of an intelligent optimization model for water resource allocation under drought conditions

Online veröffentlicht: 22. Mai 2024

Seitenbereich: -

Eingereicht: 21. Jan. 2024

Akzeptiert: 03. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1162

Schlüsselwörter
Dynamic planning, Opportunity-constrained planning, Water resource allocation, Multilayer neural network, Water production function

© 2024 Jian Fu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Construction of an intelligent optimization model for water resource allocation under drought conditions

Online veröffentlicht: 22. Mai 2024

Seitenbereich: -

Eingereicht: 21. Jan. 2024

Akzeptiert: 03. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1162

SchlüsselwörterDynamic planning, Opportunity-constrained planning, Water resource allocation, Multilayer neural network, Water production function

© 2024 Jian Fu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Dynamic planning, Opportunity-constrained planning, Water resource allocation, Multilayer neural network, Water production function