Predicting and Analyzing Water Quality using Machine Learning: A Comprehensive Model
The deteriorating quality of natural water resources like lakes, streams and estuaries, is one of the direst and most worrisome issues faced by humanity. The effects of un-clean water are far-reaching, impacting every aspect of life. Therefore, management of water resources is very crucial in order...
| Main Authors: | , |
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| Format: | Proceeding |
| Language: | English |
| Published: |
2016
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/12651/ http://ir.unimas.my/id/eprint/12651/1/Yafra%20Khan.pdf |
| _version_ | 1848837243244904448 |
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| author | Khan, Yafra Chai, Soo See |
| author_facet | Khan, Yafra Chai, Soo See |
| author_sort | Khan, Yafra |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | The deteriorating quality of natural water resources
like lakes, streams and estuaries, is one of the direst and most worrisome issues faced by humanity. The effects of un-clean water are far-reaching, impacting every aspect of life. Therefore, management of water resources is very crucial in order to optimize the quality of water. The effects of water contamination can be tackled efficiently if data is analyzed and water quality is predicted beforehand. This issue has been addressed in many previous researches, however, more work needs to be done in terms of effectiveness, reliability, accuracy as well as usability of
the current water quality management methodologies. The goal
of this study is to develop a water quality prediction model with the help of water quality factors using Artificial Neural Network (ANN) and time-series analysis. This research uses the water quality historical data of the year of 2014, with 6-minutes time interval. Data is obtained from the United States Geological Survey (USGS) online resource called National Water Information System (NWIS). For this paper, the data includes the measurements of 4 parameters which affect and influence water quality. For the purpose of evaluating the performance of model, the performance evaluation measures used are Mean-Squared Error (MSE), Root Mean-Squared Error (RMSE) and Regression Analysis. Previous works about Water Quality prediction have also been analyzed and future improvements have been proposed in this paper. |
| first_indexed | 2025-11-15T06:36:34Z |
| format | Proceeding |
| id | unimas-12651 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:36:34Z |
| publishDate | 2016 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-126512022-01-04T00:28:47Z http://ir.unimas.my/id/eprint/12651/ Predicting and Analyzing Water Quality using Machine Learning: A Comprehensive Model Khan, Yafra Chai, Soo See TD Environmental technology. Sanitary engineering The deteriorating quality of natural water resources like lakes, streams and estuaries, is one of the direst and most worrisome issues faced by humanity. The effects of un-clean water are far-reaching, impacting every aspect of life. Therefore, management of water resources is very crucial in order to optimize the quality of water. The effects of water contamination can be tackled efficiently if data is analyzed and water quality is predicted beforehand. This issue has been addressed in many previous researches, however, more work needs to be done in terms of effectiveness, reliability, accuracy as well as usability of the current water quality management methodologies. The goal of this study is to develop a water quality prediction model with the help of water quality factors using Artificial Neural Network (ANN) and time-series analysis. This research uses the water quality historical data of the year of 2014, with 6-minutes time interval. Data is obtained from the United States Geological Survey (USGS) online resource called National Water Information System (NWIS). For this paper, the data includes the measurements of 4 parameters which affect and influence water quality. For the purpose of evaluating the performance of model, the performance evaluation measures used are Mean-Squared Error (MSE), Root Mean-Squared Error (RMSE) and Regression Analysis. Previous works about Water Quality prediction have also been analyzed and future improvements have been proposed in this paper. 2016 Proceeding PeerReviewed text en http://ir.unimas.my/id/eprint/12651/1/Yafra%20Khan.pdf Khan, Yafra and Chai, Soo See (2016) Predicting and Analyzing Water Quality using Machine Learning: A Comprehensive Model. In: Long Island Systems, Applications and Technology Conference (LISAT), 29-29 April 2016, Farmingdale, NY, USA. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7494106&tag=1 |
| spellingShingle | TD Environmental technology. Sanitary engineering Khan, Yafra Chai, Soo See Predicting and Analyzing Water Quality using Machine Learning: A Comprehensive Model |
| title | Predicting and Analyzing Water Quality using Machine Learning: A Comprehensive Model |
| title_full | Predicting and Analyzing Water Quality using Machine Learning: A Comprehensive Model |
| title_fullStr | Predicting and Analyzing Water Quality using Machine Learning: A Comprehensive Model |
| title_full_unstemmed | Predicting and Analyzing Water Quality using Machine Learning: A Comprehensive Model |
| title_short | Predicting and Analyzing Water Quality using Machine Learning: A Comprehensive Model |
| title_sort | predicting and analyzing water quality using machine learning: a comprehensive model |
| topic | TD Environmental technology. Sanitary engineering |
| url | http://ir.unimas.my/id/eprint/12651/ http://ir.unimas.my/id/eprint/12651/ http://ir.unimas.my/id/eprint/12651/1/Yafra%20Khan.pdf |