| Title |
AI for clean water: efficient water quality prediction leveraging machine learning |
| ID_Doc |
43312 |
| Authors |
Ansari, AT; Nigar, N; Faisal, HM; Shahzad, MK |
| Title |
AI for clean water: efficient water quality prediction leveraging machine learning |
| Year |
2024 |
| Published |
Water Practice And Technology, 19, 5 |
| DOI |
10.2166/wpt.2024.120 |
| Abstract |
Water is one of the most critical resources for maintaining life. Although it makes upto 70% of the earth's surface but only a small amount of it is usable. Since water is used for a variety of functions, its quality must be determined before usage. The rapid increase of the world's population has also had a significant influence on the environment, particularly on water quality. The quality of water has been deteriorating in recent years due to various pollutants. To control the water pollution, modeling and predicting the water quality has become a crucial need. In this work, we propose a machine learning (ML)-based model to predict and classify the water quality. The results from six different ML models are analyzed for accuracy, precision, recall, and F1 score as performance measures. The proposed approach is validated using benchmark dataset. The results show that Decision Tree ML model has a distinct superiority on other classifiers in terms of performance indicators like accuracy of 97.53%, precision of 87.66%, recall of 74.59%, and F1-score of 80.60%. This will help the aquatic system for better water quality analysis. |
| Author Keywords |
water quality prediction; machine learning; smart city |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Emerging Sources Citation Index (ESCI) |
| EID |
WOS:001217740800001 |
| WoS Category |
Water Resources |
| Research Area |
Water Resources |
| PDF |
https://iwaponline.com/wpt/article-pdf/doi/10.2166/wpt.2024.120/1417999/wpt2024120.pdf
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