Optimal dispatching of water resources in artesian irrigation district of Ningxia based on genetic algorithm-generative adversarial neural network model

doi:10.14088/j.cnki.issn0439-8114.2021.15.036

Abstract

Abstract: In view of the phenomena of large crop water consumption, concentrated water use, and low irrigation efficiency in Ningxia artesian irrigation area. According to the actual water use in Ningxia, focusing on the water inlet and outlet of the channel, the premise is to meet the basic irrigation water of farmland, and the goal is to maximize irrigation efficiency. Using machine learning methods to build genetic algorithm-generative adversarial neural network model in Ningxia artesian irrigation districts, and verify and apply them in more than 30 kilometers of channels and irrigation areas of the Qinhan Canal Management Office in Ningxia. The results show that the model deeply excavates the water usage rules of each water intake on the basis of learning traditional scheduling schemes, establishes efficient water intake joint scheduling irrigation methods, saves 315 109~1 050 362 m³ of irrigation water per month, and significantly improves the efficiency of water resource utilization in Ningxia.

Key words: irrigation water resources, optimal schedule, genetic algorithm, generative adversarial neural network, machine learning

CLC Number:

TV213.9

DONG Chen-chao. Optimal dispatching of water resources in artesian irrigation district of Ningxia based on genetic algorithm-generative adversarial neural network model[J]. HUBEI AGRICULTURAL SCIENCES, 2021, 60(15): 174-180.

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