Groundwater Quality Predictive Analysis using Machine Learning Techniques: Ireland

Monroy Ochoa, Leslie Rebeca (2024) Groundwater Quality Predictive Analysis using Machine Learning Techniques: Ireland. Masters thesis, Dublin, National College of Ireland.

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Abstract

One of the most important water resources is groundwater, essential for drinking water, agriculture, industry, and environmental sustainability. Ensuring its quality is very crucial for public and ecosystem health. This study applied four supervised machine learning models—Decision Tree, Random Forest, Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM)—to predict key groundwater quality parameters: Alkalinity, Dissolved Oxygen, Conductivity, and Nitrate, using data from monitoring stations across Ireland. Among the models, Random Forest and XGBoost demonstrated superior performance, with Random Forest achieving the highest Accuracy (0.9599), closely followed by XGBoost (0.9562). These results highlighted the potential of machine learning to enhance groundwater monitoring, offering a more efficient, cost-effective, and accurate approach for the analysis of environmental data compared to conventional methods.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Singh, Jaswinder UNSPECIFIED
Uncontrolled Keywords:	Groundwater Quality; Ireland; Machine Learning; Predictive Models
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 H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Agriculture Industry D History General and Old World > DA Great Britain > Ireland Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	20 Aug 2025 11:20
Last Modified:	20 Aug 2025 11:20
URI:	https://norma.ncirl.ie/id/eprint/8591

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