Paper Info
Title
Video-Sensing Characterization For Hydrodynamic Features: Particle Tracking-Based Algorithm Supported By A Machine Learning Approach
Abstract
The efficient and reliable monitoring of the flow of water in open channels provides useful information for preventing water slow-downs due to the deposition of materials within the bed of the channel, which might lead to critical floods. A reliable monitoring system can thus help to protect properties and, in the most critical cases, save lives. A sensing system capable of monitoring the flow conditions and the possible geo-environmental constraints within a channel can operate using still images or video imaging. The latter approach better supports the above two features, but the acquisition of still images can display a better accuracy. To increase the accuracy of the video imaging approach, we propose an improved particle tracking algorithm for flow hydrodynamics supported by a machine learning approach based on a convolutional neural network-evolutionary fuzzy integral (CNN-EFI), with a sub-comparison performed by multi-layer perceptron (MLP). Both algorithms have been applied to process the video signals captured from a CMOS camera, which monitors the water flow of a channel that collects rain water from an upstream area to discharge it into the sea. The channel plays a key role in avoiding upstream floods that might pose a serious threat to the neighboring infrastructures and population. This combined approach displays reliable results in the field of environmental and hydrodynamic safety.
Year
DOI
Venue
2021
10.3390/s21124197
SENSORS
Keywords
DocType
Volume
sensors, sensing systems, hydrodynamic monitoring, flow measurement and classification, machine learning, particle tracking
Journal
21
Issue
ISSN
Citations 
12
1424-8220
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Aimé Lay-Ekuakille17121.29
John Djungha Okitadiowo200.34
Moïse Avoci Ugwiri300.34