Speaker
Description
Water quality monitoring is critical for sustainable water resource management, particularly in water-stressed regions such as South Africa. However, the availability, accessibility, and consistency of water quality remain a major challenge, which may be contaminated by waste from different sources like mines, industries and agricultural activities. Some areas in South Africa face serious water scarcity, where consumers are compelled to buy water, and those who cannot afford it use water from rivers or dams, which may be contaminated by waste from various sources, such as mines, industries. This study employs machine learning models including Linear Regression, Decision Trees, and Random Forests to predict water quality. The dataset contains water quality parameters such as pH, turbidity, dissolved oxygen, electrical conductivity, and nutrient concentrations. For electrical conductivity Linear Regression shown the highest predictive performance (R² = 0.625), indicating predominantly linear relationship. Random Forest and Decision Tree models showed moderate performance (R² ≈ 0.56), suggesting limited nonlinear interactions within the dataset. The findings will support the integration of conventional machine learning approaches to enhance water quality prediction, water monitoring, and resource management.
| Apply for student award at which level: | PhD |
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| Consent on use of personal information: Abstract Submission | Yes, I ACCEPT |