Cite this article

@article{vol4issue1iotbasedwaterqualitymonitoringwithrealtimeanomalydetection,
  title = {IoT-Based Water Quality Monitoring with Real-Time  Anomaly Detection},
  author = {Rahman, Nur Aisyah},
  journal = {AI Tech International Journal},
  volume = {4},
  number = {1},
  pages = {53--62},
  doi = {https://doi.org/10.64180/oct.techai.260107},
  issn = {3079-4749}
}

TY  - JOUR
AU  - Rahman, Nur Aisyah
TI  - IoT-Based Water Quality Monitoring with Real-Time  Anomaly Detection
JO  - AI Tech International Journal
VL  - 4
IS  - 1
SP  - 53
EP  - 62
DO  - https://doi.org/10.64180/oct.techai.260107
AB  - <p style="text-align:justify;">Water quality monitoring is essential for ensuring public health, environmental sustainability, and the availability of safe drinking water. Traditional water quality assessment techniques often rely on manual sample collection and laboratory-based testing, which can be time-consuming, expensive, and insufficient for identifying sudden changes in water conditions. The rapid development of the Internet of Things (IoT), wireless sensor networks, cloud computing, and artificial intelligence has created new opportunities for continuous and intelligent water quality monitoring. This paper presents an IoT based water quality monitoring framework integrated with real-time anomaly detection to provide accurate, efficient, and automated assessment of water conditions. The proposed system utilizes multiple sensors to measure critical water quality parameters, including pH, turbidity, temperature, electrical conductivity, dissolved oxygen, and total dissolved solids (TDS).</p>
KW  - Internet of Things  (IoT)
KW  - Water  Monitoring
KW  - Quality  Real-Time  Anomaly Detection
KW  - Machine  Learning
KW  - Smart  Management. 
SN  - 3079-4749
UR  - https://techaijournal.com/article/vol-4-issue-1-iot-based-water-quality-monitoring-with-real-time-anomaly-detection
ER  -