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Dynamic Simulation Model of Channel Leakage Based on Multiple Regression

Author

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  • Jianqin Ma

    (Department of Agricultural Hydraulic Engineering, Faculty of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450000, China)

  • Jiangshan Yang

    (Department of Agricultural Hydraulic Engineering, Faculty of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450000, China)

  • Xiuping Hao

    (Department of Agricultural Hydraulic Engineering, Faculty of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450000, China)

  • Bifeng Cui

    (Department of Agricultural Hydraulic Engineering, Faculty of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450000, China)

  • Shuoguo Yang

    (Department of Agricultural Hydraulic Engineering, Faculty of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450000, China)

Abstract

Aiming at the problem that the existing channel leakage calculation methods generally ignore the dynamic changes of influencing factors, which leads to a large calculation error, this study attempts to utilize the machine learning method to accurately calculate the channel leakage loss under the dynamic changes in the influencing factors. By using the machine learning method to analyze the impact of dynamic changes in the flow rate and soil moisture content over time on the channel leakage loss in the water transmission process and quantify the impact of the selected factors on the leakage loss, a dynamic simulation model of the multi-parameter channel leakage loss was constructed, and a test was carried out in the irrigation area to verify the accuracy of the model. The test results are as follows: the actual leakage loss of the U1 channel is 1094.03 m 3 , the simulated value of the model is the 1005.24 m 3 , and the error between the simulated value and the measured value is 8.12%; the total leakage of the U2 channel is 1111.24 m 3 , the simulated value of the model is 1021.1 m 3 , and the error between the simulated value and the measured value is 6.31%. The experimental results show that the use of machine learning to construct a dynamic simulation model of channel leakage loss under the comprehensive consideration of the dynamic change in influencing factors over time has a better effect, and the calculation accuracy is high.

Suggested Citation

  • Jianqin Ma & Jiangshan Yang & Xiuping Hao & Bifeng Cui & Shuoguo Yang, 2023. "Dynamic Simulation Model of Channel Leakage Based on Multiple Regression," Sustainability, MDPI, vol. 15(20), pages 1-13, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:20:p:14904-:d:1260499
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    References listed on IDEAS

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    1. Santhi, C. & Pundarikanthan, N. V., 2000. "A new planning model for canal scheduling of rotational irrigation," Agricultural Water Management, Elsevier, vol. 43(3), pages 327-343, April.
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    Cited by:

    1. Jianqin Ma & Yijian Chen & Xiuping Hao & Bifeng Cui & Jiangshan Yang, 2024. "Study on Real-Time Water Demand Prediction of Winter Wheat–Summer Corn Based on Convolutional Neural Network–Informer Combined Modeling," Sustainability, MDPI, vol. 16(9), pages 1-14, April.
    2. Jianqin Ma & Lansong Liu & Bifeng Cui & Xiuping Hao & Qinxue He & Jiangshan Yang & Xiaolong Xu, 2024. "Research on the Coupling Effect of Water and Fertilizer on Maize under Multi-Objective Conditions and Its Application Scenarios," Sustainability, MDPI, vol. 16(13), pages 1-15, June.
    3. Jianqin Ma & Xiaolong Xu & Bifeng Cui & Xiuping Hao & Jiangshan Yang & Shuoguo Yang & Lansong Liu, 2024. "Multivariate Regression-Based Dynamic Simulation Modeling of Cumulative Carbon Emissions from Fields," Sustainability, MDPI, vol. 16(22), pages 1-13, November.

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