AI and IoT-assisted Water Quality Monitoring and Prediction

This research provides a strong and all-inclusive solution by utilizing the capabilities of Internet of Things and Artificial Intelligence (AI) technologies for water quality prediction. The system uses an Arduino that is integrated with Temperature, Turbidity, pH, total dissolved solids, and LCD sensors to record water quality data in real time. Cloud connectivity is provided via ESP8266, which allows the collected data to be sent to the cloud in an efficient manner. Here, the prediction of water quality levels is made possible by a Python script that uses AI techniques to compare the real-time data with an existing dataset. Over time, the prediction model adjusts and improves, guaranteeing accuracy. Following visualization, the data is shown in real-time on ThingSpeak, offering a simple and approachable platform for tracking trends in water quality. This integrated system is a sustainable method of using technology for environmental monitoring and prediction, while also improving the effectiveness of water resource management.

Authors

Institutions

Publication Details

Published
2024-11-06
DOI
https://doi.org/10.1109/iceca63461.2024.10801021
Citations
2
Primary Topic
Water Quality Monitoring Technologies
Type
article
Field-Weighted Citation Impact
0.40
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AI and IoT-assisted Water Quality Monitoring and Prediction

V Harsha Vardhan, V. Rama Krishna, Vishnu Vardhan Kanuri, C Jenila et al.
2 citations
Water Quality Monitoring Technologies
0.40
article

AI and IoT-assisted Water Quality Monitoring and Prediction

V Harsha Vardhan, V. Rama Krishna, Vishnu Vardhan Kanuri, C Jenila, Santhan Reddy, Jeyaprakash Kadambarajan
article en
2 citations

Abstract

This research provides a strong and all-inclusive solution by utilizing the capabilities of Internet of Things and Artificial Intelligence (AI) technologies for water quality prediction. The system uses an Arduino that is integrated with Temperature, Turbidity, pH, total dissolved solids, and LCD sensors to record water quality data in real time. Cloud connectivity is provided via ESP8266, which allows the collected data to be sent to the cloud in an efficient manner. Here, the prediction of water quality levels is made possible by a Python script that uses AI techniques to compare the real-time data with an existing dataset. Over time, the prediction model adjusts and improves, guaranteeing accuracy. Following visualization, the data is shown in real-time on ThingSpeak, offering a simple and approachable platform for tracking trends in water quality. This integrated system is a sustainable method of using technology for environmental monitoring and prediction, while also improving the effectiveness of water resource management.

Kalasalingam Academy of Research and Education (IN)
Clean water and sanitation
Openalex Percentile: Top 36%
Water Quality Monitoring Technologies
0.40
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

AI and IoT-assisted Water Quality Monitoring and Prediction — V Harsha Vardhan, V. Rama Krishna, et al. · (2024) | TGRS Research Map | TGRS