A Hybrid framework approach for SMART water quality monitoring and predictive analysis using internet of things, and random forest algorithm
- Author
- Matope, Frank
- Title
- A Hybrid framework approach for SMART water quality monitoring and predictive analysis using internet of things, and random forest algorithm
- Abstract
- This research presents a hybrid framework for SMART water quality monitoring and predictive analysis, integrating Internet of Things (IoT) technologies with the Random Forest algorithm. As water quality degradation poses significant risks to public health, agriculture, and ecosystems, timely and accurate monitoring is essential. The proposed framework employs IoT sensors to collect real-time data on critical water quality parameters such as pH, turbidity, and dissolved oxygen. These data streams are processed through a Random Forest algorithm to predict contamination events and detect anomalies, enabling proactive resource management. The study evaluates the system's performance through case studies and simulations, demonstrating high accuracy in predictions and efficient data processing. By merging IoT and machine learning, this framework addresses existing gaps in conventional monitoring methods, providing a scalable solution that supports sustainable water management and aligns with global health and environmental objectives.
- Date
- June 2025
- Publisher
- BUSE
- Keywords
- Random Forest Algorithm
- Hybrid Framework Approach
- Supervisor
- Mr. D. Hove
- Item sets
- Department of Computer Science
- Media
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Matope, Frank.pdf