| dc.description.abstract | As the one of the biggest topographical catastrophes, landslides are hurtful to the biological
environment. They are common within the eastern and western slopes of Uganda, coming about in a
essential number of fatalities and monetary incidents. Inquiries on landslides within the Mount
Elgon locale date back almost two decades. These considers have largely centered on landslide
causality, human defenselessness, and how individuals perceive landslide hazards. Scholars have
made low endeavors to utilize cutting edge advancement to monitor and caution approximately
landslide events inside the Elgon zones, and in this way robotize the forecast of landslide
susceptibility. An effective landslide prevention program is needed to address key aspects of
landslides, such as their occurrence locations and timing. What will be the estimate? Monitoring and
prediction are key for a landslide hazard evasion system.
This project will deploy a satellite store and forward system for monitoring and predicting
landslides. Factors that influence landslides include; Geomechanical properties, Rainfall, Ground
saturation, Topography, Earthquakes and many others. This project considers ground saturation and
geomechanical properties as they directly influence the stability and mechanical behavior of slopes,
contributing to the occurrence and triggering of landslides. The framework includes a network of
remote sensor nodes placed in potential landslide areas to collect information on soil moisture and
vibration parameters. Long-range transceivers connected to sensor nodes transmit sensor information
using chirp spread spectrum over the unlicensed 868 MHz band to LoRa boards with long-range
wide area network gateways aboard CubeSat satellites.
The CubeSat LoRaWAN gateway connects to cloud applications via the Things Network protocol,
thus "Store and Forward". This project utilizes the cloud application and IoT analytics platform
ThingSpeak. Once integrated with your microcontroller, Things Network automatically sends
collected sensor data to ThingSpeak. Which gathers, visualizes, and showcases real-time data
streams online. It offers iframes for easy display on a webpage which provides alerts for identifying
potential landslides. This strategy offers a cost-effective and efficient method to monitor landslides
in real-time, serving as an early warning system to prevent casualties and property damage. The
study ensures the security of the framework’s design, communication protocols, and information
handling calculations. The study’s ultimate area emphasizes the potential benefits of this strategy,
such as progressed precision, diminished wrong alerts and expanded versatility. | en_US |
