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A Multi-Modal Wireless Sensor System for River Monitoring: A Case for Kikuletwa River Floods in Tanzania
Lawrence Mdegela1,2, Yorick De Bock1, Esteban Municio3
1Department of Computer Science, University of Antwerp-imec IDLab, Sint-Pietersvliet 7, 2000 Antwerpen, Belgium.
Sensors (Basel, Switzerland)
|April 28, 2023
Summary
A new sensor system collects crucial river and weather data in Tanzania, improving flood prediction accuracy in under-monitored areas. This enhances early warning systems for vulnerable communities.
Area of Science:
- Hydrology
- Environmental Science
- Sensor Technology
Background:
- Flood prediction is difficult in poorly gauged basins, especially in developing nations, due to scarce data.
- Insufficient river monitoring hinders the development of effective flood prediction models and early warning systems.
Purpose of the Study:
- To introduce a multi-modal, sensor-based, near-real-time river monitoring system for the Kikuletwa River in Tanzania.
- To create a multi-feature dataset to improve flood prediction accuracy and anomaly detection in data-scarce regions.
Main Methods:
- Deployed a sensor system collecting six parameters: current/previous hour/day rainfall, river level, wind speed, and direction.
- Gathered data at multiple locations to broaden ground truth for river characteristics and weather conditions.
Main Results:
- Generated a comprehensive dataset for the Kikuletwa River, enhancing local weather station data.
- The system provides crucial data for establishing reliable river thresholds for anomaly detection.
Conclusions:
- The developed monitoring system and dataset significantly improve flood prediction capabilities in data-scarce environments.
- The data supports the development of advanced AI/ML forecasting models and has applications beyond flood warning systems.
