Presenting A Model for Managing Agricultural, Industrial, And Household Water Consumption in Iran by Combining System Dynamics and Genetic Algorithm

<strong>Introduction:</strong> In this study, the country's water resources management system has been modeled using a system dynamics approach. After simulation, optimal water resources, social power of governance, government financial resources, and the rate of water migration have been determined based on the decision variables of household, agricultural, and industrial water tariffs. The optimal combined results suggest an 11%, 200%, and 200% increase in water tariffs for household, agricultural, and industrial sectors, respectively, over the next 5 years. <strong>Methods:</strong> Initially, this study modeled and simulated the variables and feedback loops involved in water resources management using system dynamics tools. For optimization, two methods, genetic algorithm and the Simulation-optimization capability in the Vensim software, were employed. In the genetic algorithm, an initial population of 30 individuals and crossover and mutation probabilities of 83% and 17% were considered. The selection criterion for each chromosome is based on the roulette wheel method. <strong>Findings: </strong>The results indicate that optimization using the genetic algorithm results in an 89% increase in water resources behind dams compared to the baseline conditions, whereas the Vensim software output shows an 84% improvement. Overall, the genetic algorithm improves the objective function by 2% compared to the Vensim software's optimization output. Additionally, after optimization, the best tariff policy for enhancing the objective function is a further increase in agricultural and industrial water tariffs compared to household tariffs.