Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Efficient and intelligent water management is vital for modern urban and rural infrastructures. This project
proposes a Machine Learning-based Water Supply Monitoring
Network that ensures transparency, minimizes water loss, and
enhances consumer utility tracking. The system integrates flow
sensors and turbidity sensors across the distribution pipeline
from the main storage tank to individual client endpoints. A
Long Short-Term Memory (LSTM) based machine learning
model is used to analyze sensor data, detect anomalies such
as leakages, forecast future water demand, and monitor water
quality. An Android app allows users to track daily water usage,
receive alerts, and pay bills digitally, while an administrative
dashboard enables authorities to monitor real-time data for
informed decision-making. This smart water distribution system
promotes sustainability, reduces wastage, and enhances the efficiency and accountability of water resource management.
"SmartLeakNet:Wireless Leak Monitor for Irrigation", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 3, page no.a512-a518, March-2026, Available :http://www.ijrti.org/papers/IJRTI2603065.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator