This is an early access version, the complete PDF, HTML, and XML versions will be available soon. Open AccessArticle Hybrid IoT-VIoT System for Real-Time Water-Level Monitoring Using Computer Vision 1 Department of Information Systems, M.Kh. Dulaty Taraz University, Taraz 080001, Kazakhstan 2 Department of Systems Analysis and Management, L.N. Gumilyov Eurasian National University, Astana 010000, Kazakhstan 3 Department of Water Resources, Kazakh National University of Water Management and Irrigation, Taraz 080000, Kazakhstan * Authors to whom correspondence should be addressed. Computers 2026, 15(6), 373; https://doi.org/10.3390/computers15060373 (registering DOI) Submission received: 22 April 2026 / Revised: 4 June 2026 / Accepted: 4 June 2026 / Published: 7 June 2026 Efficient water resource management is critically important for arid regions such as southern Kazakhstan. This paper presents a hybrid Internet of Things (IoT) and Vision-based Internet of Things (VIoT) architecture for real-time monitoring of water levels in irrigation channels. The proposed system integrates an ultrasonic water-level sensor, an IP camera with edge-based computer vision processing on a Raspberry Pi, wireless communication, an autonomous solar power supply, and discharge estimation using Manning’s equation. The VIoT subsystem applies image processing techniques, including gauge calibration, Canny edge detection, and pixel-to-metric conversion, to automatically estimate water level from captured video frames. Water-level measurements obtained from IoT sensors and video-based analysis are combined through synchronised data fusion to improve monitoring accuracy and reliability. The hybrid approach leverages the complementary strengths of IoT and VIoT by combining continuous quantitative sensing with visual verification capabilities. Field experiments conducted on the Merke River in the Zhambyl region of Kazakhstan over a 14-day observation period demonstrated stable real-time operation with RMSE = 0.311 cm, MAE = 0.279 cm, and Pearson r = 0.99 between the ultrasonic sensor and the vision-based estimates. Sensitivity analysis indicated that water level is the most influential parameter in Manning-based discharge estimation, confirming the importance of accurate level detection. The proposed system improves reliability by cross-checking independent data sources, making it applicable to monitoring water levels in agricultural regions. Keywords: internet of things; visual internet of things; water-level monitoring; intelligent irrigation; hybrid system Share and Cite Tungatarova, A.; Borankulova, G.; Murzakhmetov, A.; Yeraliyeva, B.; Dulatbayeva, S.; Bekbolatov, S.; Turarova, B. Hybrid IoT-VIoT System for Real-Time Water-Level Monitoring Using Computer Vision. Computers 2026, 15, 373. https://doi.org/10.3390/computers15060373 Tungatarova A, Borankulova G, Murzakhmetov A, Yeraliyeva B, Dulatbayeva S, Bekbolatov S, Turarova B. Hybrid IoT-VIoT System for Real-Time Water-Level Monitoring Using Computer Vision. Computers. 2026; 15(6):373. https://doi.org/10.3390/computers15060373 Tungatarova, Aigul, Gaukhar Borankulova, Aslanbek Murzakhmetov, Bakhyt Yeraliyeva, Saltanat Dulatbayeva, Samat Bekbolatov, and Balzhan Turarova. 2026. "Hybrid IoT-VIoT System for Real-Time Water-Level Monitoring Using Computer Vision" Computers 15, no. 6: 373. https://doi.org/10.3390/computers15060373 Tungatarova, A., Borankulova, G., Murzakhmetov, A., Yeraliyeva, B., Dulatbayeva, S., Bekbolatov, S., & Turarova, B. (2026). Hybrid IoT-VIoT System for Real-Time Water-Level Monitoring Using Computer Vision. Computers, 15(6), 373. https://doi.org/10.3390/computers15060373 Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details . Article Metrics Article metric data becomes available approximately 24 hours after publication online.
