Scheduling and optimal delivery of water in irrigation networks by combining the AquaCrop model and genetic algorithm

Document Type : Research/Original/Regular Article

Authors

1 Ph.D. Student, Department of Water Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran

2 Associate Professor, Department of Water Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran

3 Professor, Department of Water Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran

4 Associate Professor, Department of Water Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran

5 Ph.D. Student, Department of Applied Mathematics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad, Iran

Abstract

Introduction
Delivery discharge to each outlet, the number of outlets grouped in a block, and the sequence of water delivery to the outlets are the three main components of water delivery in irrigation networks. Therefore, scheduling the distribution and delivery of water in the network means a determination of these three components. In this research, an optimal water delivery model was developed. The proposed approach was applied to the M canal of the Moghan Irrigation Network of Iran. By choosing a certain number of blocks, the outlets located in the distribution canal are distributed inside each block in such a way that all the outlets are placed inside the blocks and no block is left without outlets. In the following, the way of scheduling water delivery to each of the outlets located in each block and the flow rate delivered to them is such that the flow rate of the canal, the time required to complete the irrigation scheduling, and also the difference in the volume of water delivered to each outlet are minimized. The limitations of the model are: a) the total flow entering the outlets that collect water at the same time should not exceed the capacity of the canal. b) the total water intake time of the outlets inside each block should not exceed the irrigation interval. In this research, the allocation of water and the cultivated area in the optimal water allocation model, which is linked to the crop model, were used to optimize water delivery.
 
Materials and Methods
Delivery discharge to each outlet, the number of outlets grouped in a block, and the sequence of water delivery to the outlets are the three main components of water delivery in irrigation networks. Therefore, scheduling the distribution and delivery of water in the network means the determination of these three components. In this research, an optimal water delivery model was developed. The proposed approach was applied to the Canal M of the Moghan Irrigation Network of Iran. By choosing a certain number of blocks, the outlets located in the distribution canal are distributed inside each block in such a way that all the outlets are placed inside the blocks and no block is left without outlets. In the following, the way of scheduling water delivery to each of the outlets located in each block and the flow rate delivered to them is such that the flow rate of the canal, the time required to complete the irrigation scheduling,g and also the difference in the volume of water delivered to each outlet are minimized. The limitations of the model are: a) the total flow entering the outlets that collect water at the same time should not exceed the capacity of the canal. b) the total water intake time of the outlets inside each block should not exceed the irrigation interval. In this research, the allocation of water and the cultivated area in the optimal water allocation model, which is linked to the crop model, were used to optimize water delivery.
 
Results and Discussion
By using the volume of water allocated to each crop and cultivated area, the water demand for each outlet in different time steps was obtained. Following determining the water demand of each outlet and also by knowing the physical characteristics of the canal, optimal water delivery modeling was done in the canal for each time step by using the optimal water delivery model and genetic algorithm. Water delivery factors are presented for the 17th time step (peak demand period). The results show that the optimal flow does not exceed the minimum and maximum flow of each off-take. Also, the shorter total duration of the exploitation process than the irrigation interval indicates compliance with the relevant condition in the model. The hydrograph of the inflow into Canal M showed that the maximum flow of the inflow into the canal is less than the capacity of the canal, which indicates compliance with the relevant condition in the model. The hydrograph of the inflow to the canal provides the number of settings of the main off-take of the distribution canal. During the completion of the water delivery program, the main off-take of the distribution canal, which receives water from the main Canal A, is adjusted a total of 23 times by the network operator.
 
Conclusion
In this research, the optimization model of water distribution and delivery in distribution canals was developed using a genetic algorithm in MATLAB software. With the aim of optimal allocation and delivery of water at different levels of the irrigation network and by combining the crop model, the results and output of the models were combined with each other so that the results of optimal water scheduling are more appropriate with the real conditions and water requirements of the network. For the canal in the peak demand period, the maximum and minimum canal capacity were calculated to be 2.573 and 0.590 m3.s-1, respectively, the maximum time to complete the irrigation program was 232 h, and the number of settings of the main off-take was calculated as 23 settings. The obtained results indicate that the developed models are useful for scheduling the optimal allocation and delivery of water and with its help different goals can be optimized simultaneously.

Keywords

Main Subjects

References

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Water and Soil Management and Modelling
Volume 4, Issue 4
2024
Pages 255-268

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History

  • Receive Date: 14 November 2023
  • Revise Date: 17 December 2023
  • Accept Date: 17 December 2023

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