ORIGINAL RESEARCH article
Front. Water
Sec. Water and Artificial Intelligence
Volume 7 - 2025 | doi: 10.3389/frwa.2025.1590124
Research on Mobile Ultrasonic Stratified Flow Velocity Measurement Based on Machine Learning Algorithms
Provisionally accepted
- 1Xinjiang Institute of Water Resources and Hydropower, Urumqi, China
- 2Nanjing Research Institute of Hydrology and Water Conservation Automation, Nanjing, China
- 3College of Management and Economics, Tianjin University, Tianjin, China
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A mobile ultrasonic stratified flow velocity measurement device, which utilizes a pair of ultrasonic transducers, was characterized by its low power consumption and the ability to measure multi-layer flow velocities in channels, offering advantages in water measurement applications in agricultural irrigation areas. The study combined experimental research with computational modeling to investigate the impact of ultrasonic propagation time in downstream and upstream flows and the effect of measurement position on flow velocity prediction. Traditional time-difference methods were found to be less effective for calculating water flow velocities using the proposed mobile ultrasonic transducers. By employing the AdaBoost algorithm with decision tree algorithms as weak classifiers, the relative error of the testing set was less than 5% in 85.71% of cases, achieving an R² value of 0.951, an RMSE value of 0.071, an MSE value of 0.795, and an MAE value of 0.654. The experimental results demonstrated that the use of the AdaBoost algorithm from machine learning for the mobile ultrasonic stratified flow velocity measurement device was feasible and effective.
Keywords: Water measurement, Irrigation area, Flow velocity, machine learning, Ultrasonic transit time method
Received: 08 Mar 2025; Accepted: 25 Apr 2025.
Copyright: © 2025 PU, Liu, Ba, Chen, Yu and Cui. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Jiawen Yu, College of Management and Economics, Tianjin University, Tianjin, China
Chunliang Cui, Xinjiang Institute of Water Resources and Hydropower, Urumqi, China
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