Kumpee Teeravech
Geoinformatics Program (GI)
Faculty of Computer Science and Information Technology (CSIT)
Rambhai Barni Rajabhat University (RBRU)
2025-07-01

Rua-Rai-Sai, or RRS, is a system implemented in Thailand designed to monitor natural hazards—such as floods or landslides—and issue timely warnings to local communities. The system leverages technology (often implied sensors or geospatial tools) to detect risk zones in real time, enabling early warning and potentially life-saving interventions (Teeravech et al, 2023).

Originally, RRS was created by Yongkumyart (2009) as part of a Royal Thai Navy (RTN) research initiative. Its early applications were primarily military, focusing on alert systems for forts and base stations. Over time, however, the system was adapted for civilian use, particularly in disaster risk reduction. Since 2016, Rambhai Barni Rajabhat University (RBRU) has collaborated with the RTN to further refine RRS for disaster monitoring and community-based warning systems (Yongkumyart, 2016; Teeravech & Yongkumyart, 2023).

Different versions of RRS have since emerged. One of the most common relies on electromagnetic sensors to detect ground movement in landslide-prone regions such as Nan and Uttaradit provinces. Later enhancements introduced three-dimensional web-based visualization, GNSS modules for geolocation, and wireless communication for rapid alerts. In 2022, researchers advanced RRS during the Rapid Prototype Development (RPD-2022) Hackathon, building prototypes with ESP32 and Sony Spresense boards, coupled with QZSS satellite signals.

By 2024-2025, the system expanded beyond disaster monitoring, being adapted to detect wild elephant intrusions in agricultural and community zones. This version incorporated laser sensors—more effective than magnetic switches in orchards—alongside ESP-CAM modules for real-time image capture and transmission via Wi-Fi. Researchers have also begun experimenting with artificial intelligence to analyze and recognize elephant sounds, adding another layer of intelligence to the system.

The continuous evolution of RRS demonstrates Thailand’s growing integration of advanced technologies into community-based disaster risk management. These innovations align with global best practices, where multi-hazard early warning systems are increasingly enhanced by IoT devices, remote sensing, and AI for predictive analysis.

For more information, please contact me at kumpee(at)rbru.ac.th.

Reference

• Teeravech, K., Yongkumyart, A., Srimala, W., Yarak, K., Samma, T., and Thongjing, P. (2023). Applications of Rua-Rai-Sai for Disaster Monitoring and Warning in Thailand. The 7th International Conference on Information Technology (InCIT-2023), Mae Fah Luang University, Chiangrai, 15 – 17 November, 2023, pp. 250-255. DOI: https://doi.org/10.1109/InCIT60207.2023.10412974.
• A. Yungkumyart. Rua Rai Sai (Tracking System), 2009, Unpublished.
• A. Yungkumyart. Application of a Landslide Monitoring System using Weather, Underground water pressure, and Land moving by Commonity, 2016, Unpublished.
• K. Teeravech and A. Yungkumyart, Application of RuaRai-Sai for Landslide and Flood Monitoring in Nan and Uttaradit Provinces, 2023, unpublished.