Virtual Booth

Topic : LDTec-Ai
LDTec -Ai addresses a critical gap in landslide monitoring by introducing a smart, adaptive, and real-time early warning system powered by Hybrid Artificial Intelligence. Landslides pose a significant risk to safety, particularly in areas with unstable terrain such as school environments and residential area. LDTec-AI is an Internet of Things (IoT)-based system designed to monitor ground displacement and provide real-time alerts via mobile devices and cloud-based dashboards, enabling early preventive action. The system utilises ESP32 microcontrollers integrated with distance sensors to continuously collect and transmit data. During the prototype stage, ultrasonic sensors are used due to their accessibility and suitability for controlled demonstrations, while real-world deployment on high-risk slopes employs laser ranging sensors for improved accuracy and reliability in outdoor conditions.
The system has been successfully implemented on a landslide-prone hillside, demonstrating continuous monitoring and real-time data transmission. To further validate its robustness and adaptability, the system is planned to be tested across multiple locations with varying terrain conditions, highlighting its scalability and potential for wider application.
To enhance system intelligence and accuracy, LDTec-Ai integrates a Hybrid Artificial Intelligence approach. Fuzzy Logic is applied during data processing to interpret uncertain and imprecise sensor readings using rule-based reasoning, converting them into meaningful outputs. Subsequently, Q-Learning is utilised to optimise decision-making by learning from environmental data patterns over time, enabling the system to adapt dynamically without relying on predefined models.
Analysis of displacement data shows that sudden increases and high-frequency fluctuations indicate unstable ground conditions, serving as early warning signals of potential landslides.
In addition to its technical capabilities, LDTec-AI is designed to be cost-effective, easy to operate, and environmentally friendly, making it suitable for both educational purposes and real-world deployment.
The novelty of this study lies in the integration of Hybrid Artificial Intelligence, combining Fuzzy Logic for uncertainty handling and Q-Learning for adaptive decision-making, within a real-world IoT-based landslide monitoring system—easy to implement without prior expertise, enabling continuous learning and improved accuracy in dynamic environments.