Dynamic Simulation Model of Channel Leakage Based on Multiple Regression

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 m3, the simulated value of the model is the 1005.24 m3, and the error between the simulated value and the measured value is 8.12%; the total leakage of the U2 channel is 1111.24 m3, the simulated value of the model is 1021.1 m3, 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.

Automated summary

This study utilizes machine learning to analyze the impact of dynamic changes in flow rate and soil moisture content on channel leakage loss in water transmission process and constructs a dynamic simulation model. The experimental results show that machine learning can improve the calculation accuracy of channel leakage loss.