The project is led by Ajolwich Chanthaveerot, Ph.D., Associate Professor from the School of Engineering and Technology, and is designed to address recurrent landslide and flood risks affecting local communities.
Research Collaboration and Objectives
The interdisciplinary research team consists of:
Ajolwich Chanthaveerot, Ph.D. (Project Leader)
Eshrat E. Alah, Ph.D.
Korrakot Suwanrat
Jantira Rattanarat, Ph.D.
The project aims to overcome limitations of conventional warning systems, such as delayed alerts, limited accuracy, and poor performance during emergency situations.
Dual-Layer Communication for Reliable Alerts
According to Associate Professor Dr. Ajolwich, the subdistricts of Theppharat and Si Kheed in Sichon District are highly vulnerable to landslides and flooding, affecting more than 1,000 households. The research team therefore designed a system that integrates IoT sensors with AI-based data analytics to enable localized monitoring, intelligent risk assessment, and timely alerts, helping to reduce loss of life and property.
A key feature of the system is its two-layer communication architecture:
Normal Mode (Internet-Based)
Sensor data are transmitted via a gateway to an AI analytics system developed in Python, which evaluates risk levels and sends alerts through LINE and a web-based dashboard for immediate action by community authorities.Emergency Mode (Internet or Power Failure)
When internet connectivity or electricity is unavailable, the system automatically switches to LoRaWAN communication, enabling alerts to be sent over a local network. Community leaders then disseminate warnings via two-way radios, ensuring continuous communication even when primary infrastructure is down.
Solar-Powered Sensors Operating 24/7
The system deploys five sensor nodes in steep, high-risk areas. Each node is equipped with comprehensive sensors, including vibration, soil moisture, rainfall, slope inclination, GPS, and onboard data logging. All nodes operate entirely on solar power, allowing uninterrupted 24-hour monitoring even during power outages in severe weather conditions.
Real-time data from all locations are visualized on an interactive dashboard using color-coded status indicators—for example, green indicating “safe” conditions—enabling rapid, proactive, and evidence-based decision-making.
Strong Support from International Funding Partners
The project received financial support from the Asia Pacific Network Information Centre (APNIC) through the Information Society Innovation Fund Asia (ISIF Asia).
The system was successfully developed with the support of research assistants Sarun Hwangkarem, Chamil Ahlee, Ushaloy Chakma, and Ahmadaduwa Dao.
A Model for Sustainable Disaster Preparedness
Associate Professor Dr. Ajolwich concluded that this innovation significantly strengthens disaster preparedness for vulnerable communities in a sustainable manner and has strong potential to serve as a scalable model for deployment in other high-risk areas in the future.