Water Quality Assessment Through Predictive Modeling Employing Machine Learning Methods
Clicks: 1
ID: 312987
2024
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
0.0
/100
1 views
0 readers
AI Quality Assessment
Not analyzed
Readership in this journal
Ranked #419 of 705 articles by views in Journal of Computing & Biomedical Informatics
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 705 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Water quality declines pose serious problems that need creative methods to ensure proper monitoring. In order to gather and evaluate water quality data in real time, this study presents the Water Quality Measurement Application, which integrates cutting-edge sensors with artificial intelligence (AI). By creating a tool that is easily accessed by environmentalists and scientists, the goal is to enhance existing techniques that depend on labor-intensive and less accurate manual sampling and analysis. The accuracy of current sensor-based systems is frequently restricted, and they are unable to accurately forecast problems with water quality. To get around this, machine learning (ML) techniques are used in the application to assess and forecast water quality situations while IoT sensors are integrated for continuous data collecting. Safely moving data to a cloud platform is made possible by the Blynk IoT framework, which guarantees accessibility and security. When it came to identifying water quality characteristics, the Random Forest (RF) approach outperformed other machine learning models, including K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Multinomial Naive Bayes (MNB). When compared to conventional techniques, this breakthrough yields forecasts that are more trustworthy. Subsequent efforts will concentrate on improving the application by extending the scope of observable metrics and adding user input. Accuracy improvement is another goal of ongoing research into ML algorithms. By providing a more sophisticated, automated method of comprehending and regulating water health, this creative solution benefits environmental professionals as well as labs.
| Reference Key |
imported_1777058276_69ebc1e423283
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Mehak Afzal, Shujaat Ali, Hafiz Burhan Ul Haq, Rabia Younis, Hamid Ali, Amna Kosar, Hafiz Muneeb Akhtar |
| Journal | Journal of Computing & Biomedical Informatics |
| Year | 2024 |
| DOI |
DOI not found
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.