Abstract
Water scarcity is one of the global's most urgent problems, affecting billions of human beings and endangering sustainable development for the duration of the globe. It faces more advantageous fees of water wastage in each city and rural settings because of rapid urbanization and commercial growth, coupled with inefficient management practices in water use. traditional water tracking depends on manual inspection or pricey sensor-based solutions, which can be normally insufficient for complete insurance, now not to mention real-time intervention. The offered research introduces the Water Waste Detective machine, included with synthetic Intelligence, laptop imaginative and prescient, and internet of things technologies, capable of real-time detection of water wastage. it works thru the YOLOv5 and YOLOv8 deep studying fashions that become aware of actual-time leaks, overflows, and unattended faucets from stay video feeds. Upon detection, it sends automated notifications to the customers thru alerts enabled by means of IoT. this may guarantee instantaneous corrective actions that lessen water loss. It specializes in developing a exceedingly correct, scalable, and lower priced answer that may easily be deployed at family, commercial, and municipal pipeline levels. It also contributes a nicely-annotated dataset of water-wastage eventualities to the educational and industrial research network for destiny research. This take a look at indicates the capacity of the device to ensure responsible water usage, with out losing the assets, and for this reason to meet the UN Sustainable development goal 6: smooth Water and Sanitation. In widespread, this examine suggests how AI-primarily based strategies can rework water management practices, imparting practical answers to contemporary real-global demanding situations whilst placing a foundation for destiny research and technological innovation inside the management of natural sources.
Keywords
Water wastage, artificial Intelligence, computer imaginative and prescient, IoT, YOLOv5, YOLOv8, Deep getting to know, actual-time tracking, Sustainable Water management, Environmental Sustainability
Citations
IRE Journals:
Nalini Sirohi, Abha Sharma, Prathvi, Anubhav Sharma "Waste Water Detection: AI and Computer Vision" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 626-632 https://doi.org/10.64388/IREV9I10-1716080
IEEE:
Nalini Sirohi, Abha Sharma, Prathvi, Anubhav Sharma
"Waste Water Detection: AI and Computer Vision" Iconic Research And Engineering Journals, 9(10) https://doi.org/10.64388/IREV9I10-1716080
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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