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Whelan, Stephanie (2022) Predicting River Water Quality Parameters using Supervised Machine Learning Techniques: UK. Masters thesis, Dublin, National College of Ireland.
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Abstract
The quality of our water is essential to human health and to our ecosystem. Pollution in water can cause humans to become ill and wildlife to die. Rivers have become one of the most used natural water sources globally, yet in the last decade river pollution has grown due to human activities and climate change increasing the importance of a reliable, fast and affordable way to monitor river water quality. In this study, five supervised machine learning models were applied to a river water quality dataset that were collected from a river and its tributaries located in South East England. They include Decision Trees, Random Forest, Extreme Gradient Boosting, Support Vector Machines and Multiple Linear Regression. Four popular river quality parameters were predicted, they are Dissolved Sodium, Dissolved Nitrate, Gran Alkalinity and Electrical Conductivity. The best performing algorithm was found to be Random Forest when predicting all parameters with an R-Squared value of between 87% and 98%. The results found in this study can help to support the monitoring of river water quality in a fast and inexpensive way and improve the existing testing system in place.
| Item Type: | Thesis (Masters) |
|---|---|
| Subjects: | G Geography. Anthropology. Recreation > GE Environmental Sciences Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > TD Environmental technology. Sanitary engineering Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning |
| Divisions: | School of Computing > Master of Science in Data Analytics |
| Depositing User: | Tamara Malone |
| Date Deposited: | 14 Mar 2023 15:19 |
| Last Modified: | 14 Mar 2023 15:19 |
| URI: | https://norma.ncirl.ie/id/eprint/6341 |
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