Application of Game Theory in the Reallocation of Agricultural Water to Improve Water Use Efficiency

Document Type : Research Paper

Authors

• Mohammadhashem Sarafha ¹
• Sheyda Kakavand ¹
• Hashemi Masoumeh ²
• hamed mazandarani zadeh ³

¹ M.Sc. in Water Resource Management, Imam Khomeini International University, Qazvin, Iran.

² Ph.D. in Water Resource Management, Imam Khomeini International University, Qazvin, Iran.

³ associate professor, water eng. group, Imam Khomeini International University، Qazvin

Abstract

Keywords

• game theory
• genetic algorithm
• irrigation network
• water reallocation

Main Subjects

• منابع آب

Introduction

The Iranian agriculture has been consistently threatened by frequent droughts, sudden groundwater level declines, and decreased surface flow. The Qazvin plain, It covers an estimated area of about 4,400–4,500 km² and plays a key role in supplying diverse agricultural products at the national level, has been subject to severe water shortages due to both climate-based and growing pressures by domestic, industrial, and environmental uses. Based on recent studies, it has been pointed out that risk concerns of farmers due to price fluctuation and unstableness of income have been a source of over-extraction of groundwater resources. Particularly, economic modeling of crop prices through artificial neural networks and inverse demand functions has pointed out that market uncertainty boosts rigid cropping behavior and additional pressures on aquifer systems. Since farmers’ behavior during water shortage is very much determined by private economic incentives, cooperative game theory offers a sturdy analytical tool to eliminate conflict and distribute equitable reallocation of water. This is particularly relevant in semi-arid regions like Qazvin, where conflicts over water use are frequent and require equitable, transparent mechanisms to ensure stakeholder cooperation.

Purpose

This study aims to develop an integrated framework for optimizing the reallocation of water resources in the Qazvin irrigation network using a combination of genetic algorithms and cooperative game theory (Shapley value). The goal is to increase overall economic returns while ensuring fairness and motivating user participation in collective water management.

Method

We used a mixed-methods quantitative design. Using data from the 2015-2016 agricultural year, the net income of each secondary canal was first calculated, assuming the non-cooperative scenario was occurring. Then, under the cooperative scenario full cooperation by all stakeholders was assumed, we used a genetic algorithm to discover the optimal flow of 122 million cubic meters (MCM) of water across 11 secondary canals over a time period of 12 months. Finally, we applied the Shapley value to distribute surplus benefits of cooperation. Whatever each stakeholder "made" at a minimum would not be less than their current income

Results

The results showed the direct cooperative redistribution income led to a total income of 104% higher income than the current situation, however under the sequence of the optimized allocation some canals (for example, L6 and L8) had income that was lower than their current allocation. However, under the socially sustainable sequence of using the Shapley value for a fair redistribution meant that all users would have an income not less than their current income, with the surplus of each stakeholder distributed based on their marginal contribution. This led to fair benefit sharing, along with eliminating any significant incentives to conflict, and meant that the potential for realization in the 'real world' was a legitimate possibility.

Conclusions

This study supports the use of optimization techniques in conjunction with cooperative game theory as a way of resolving resource conflicts. By integrating cooperative game theory with optimization techniques, cooperative solutions can emerge for shared resources, enhancing total economic production and associated fairness to stimulate voluntary participation. It can also serve as a template for water allocation for example, and other semi-arid agricultural regions with similar socio-environmental contexts.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Authorship contribution

Conceptualization, Mazandarani Zadeh H.; methodology, Mazandarani Zadeh H.; software, Sarafha M.; validation, Sarafha M., Mazandarani Zadeh H; formal analysis, Sarafha M., Mazandarani Zadeh H.; investigation, Sarafha M. and Mazandarani Zadeh H.; resources, Sarafha M.; data curation, Sarafha M.; writing—original draft preparation, Hashemi M. and kakavand Sh.; writing—review and editing, Mazandarani Zadeh H; kakavand Sh and Hashemi M. All authors have read and agreed to the published version of the manuscript.

Declaration of Generative AI and AI-assisted technologies in the writing process

The authors did not use artificial intelligence during the preparation of this work and take full responsibility for the content of the publication.

Data availability statement

Data available on request from the authors.

Acknowledgements

The authors would like to thank anonymous referees for their constructive comments.

Ethical considerations

The authors avoided data fabrication, falsification, and plagiarism, and any form of misconduct.

Conflict of interest

The authors declare no conflict of interest.