An Embedded and Machine Learning Based Early Flood Monitoring and Warning System, the Case of River Manafwa
| dc.contributor.author | Daniel Lukyamuzi Wavamunno | |
| dc.date.accessioned | 2025-06-12T12:35:26Z | |
| dc.date.available | 2025-06-12T12:35:26Z | |
| dc.date.issued | 2025-05-06 | |
| dc.description | Undergraduate | |
| dc.description.abstract | Flooding remains a serious threat in many parts of Uganda, especially in regions with limited access to early warning systems. This project introduces a practical solution that combines embedded hardware and machine learning to monitor and predict flood events in real time. Using a flow sensor and an ultrasonic sensor connected to an ESP32 device, the system captures data on water movement and levels. These readings are automatically logged to Google Sheets, allowing for easy data management and access. A backend built with FastAPI processes this information, using a trained Random Forest algorithm to forecast potential flood risks. The results, along with past records, are displayed on an interactive dashboard developed in React. By merging simple electronics with predictive analytics, the system provides an affordable and adaptable tool to support timely flood response efforts in vulnerable areas. | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12311/2703 | |
| dc.language.iso | en | |
| dc.publisher | Uganda Christian University | |
| dc.title | An Embedded and Machine Learning Based Early Flood Monitoring and Warning System, the Case of River Manafwa | |
| dc.type | Thesis |