Simultaneously Management of Surface and Groundwater Resources and Increasing Farmers' Resilience to Water Scarcity by Predicting the Price of Agricultural Products and using GA (Case Study of Irrigation and Drainage Network of Qazvin Plain)

Document Type : Research Paper


1 Department of Water Science and Engineering, Faculty of Agriculture and Natural Resources, Imam Khomeini International University, Qazvin, Iran.

2 associated professor, water eng. group, Imam Khomeini International University، Qazvin


Taleghan Dam is the main supplier of required water to the agricultural sector of Qazvin plain. The amount of water allocated from Taleghan Dam to this plain has decreased for various reasons, including increasing the allocation of drinking water to Tehran. The reduction of allocated water and the fluctuation prices of agricultural products due to the time lag between the farmer's decision to cultivate and offer it to the market, make farmers to be uncertain to their future earnings. In order to deal with the uncertainty of the prices of agricultural products and their livelihood, despite the reduction of allocated water, farmers have started to discharge the groundwater by stabilizing the cultivated area and combining the cultivation pattern. In this study, in order to increase farmers' resilience and preserve groundwater resources, water distribution pattern with price prediction and simultaneous water cultivation and distribution pattern with price prediction has been optimized using genetic algorithm. For predicting the price of agricultural products with guaranteed purchase such as wheat, barley, sugar beet and rapeseed the ANN model was ued. For predicting the price of maize, tomato, alfalfa, peas, beans, potatoes, corn and lentils, the reverse demand function method was used. The price elasticity of demand for maize, tomato, alfalfa, peas, beans, potatoes, corn and lentils were estimated -0.508,-1.111,-0.954,-0.914,-0.374,-0.529,-0.363 and -0.332, respectively. MAE and RSME indeces indicated the ability of reverse demand function and ANN in price forecasting. The results also showed that the use of water distribution optimization models with price forecasting will increase revenue by 25% and the simultaneous optimization model of water cultivation and distribution model with price forecasting will increase network revenue by 160% compared to the current situation.


Main Subjects

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