ARDUIONO BASED IOT RAINFALL INDUCED LANDSLIDE MONITORING AND DETECTION SYTEM: A DISASTER RISK MANAGEMENT PROSPECTIVE
Abstract
Rainfall-induced landslides pose a threat to life, infrastructure, and economy in Malawi, particularly in regions such as Mulanje, Zomba, Dedza and the northern highlands just mention a few. Traditional monitoring methods such as field surveys, GIS, and remote sensing meet obstacles for large-scale, real-time detection due to terrain complexity and lack of timely data. This study addresses this gap by designing and implementing a low cost, Arduino-based Internet of Things (IoT) early warning system to monitor key environmental factors including soil moisture, slope stability, ground movement, humidity, and temperature. The system integrates with the Blynk IoT platform for real-time data transmission and monitoring. Through a quantitative, experimental approach, the prototype was field-tested in Thyolo District, with sensor data analyzed via Excel and Python to establish thresholds and evaluate performance. Results demonstrated high system accuracy, with strong correlations between slope, soil moisture, and deformation, and a low false alarm rate. This research confirms the feasibility and effectiveness of deploying a scalable, IoT-enabled early warning systems for disaster risk reduction in resource-limited settings like Malawi, offering a proactive solution for climate-resilient community safety.
Research summary
Rainfall-induced landslides pose a threat to life, infrastructure, and economy in Malawi, particularly in regions such as Mulanje, Zomba, Dedza and the northern highlands just mention a few. Traditional monitoring methods such as field surveys, GIS, and remote sensing meet obstacles for large-scale, real-time detection due to terrain complexity and lack of timely data. This study addresses this gap by designing and implementing a low cost, Arduino-based Internet of Things (IoT) early warning system to monitor key environmental factors including soil moisture, slope stability, ground movement, humidity, and temperature. This research confirms the feasibility and effectiveness of deploying a scalable, IoT-enabled early warning systems for disaster risk reduction in resource-limited settings like Malawi, offering a proactive solution for climate-resilient community safety.