Autobonics logo
    / Projects /  Hardware & Software

    Landslide Prediction and Zone Based Alert System

    Departments:
    ECE,
    EEE,
    CS,
    IT
    Level:
    Landslide Prediction and Zone Based Alert System
    Landslide Prediction and Zone Based Alert System
    Landslide Prediction and Zone Based Alert System
    Landslide Prediction and Zone Based Alert System
    Project Landslide Prediction and Zone Based Alert System
    Project Landslide Prediction and Zone Based Alert System
    Project Landslide Prediction and Zone Based Alert System
    Project Landslide Prediction and Zone Based Alert System
    Project date: 8/19/2023

    A Landslide Prediction and Zone Based Alert System utilizing sensors and machine learning to predict landslides, categorize areas into zones, and provide timely color-coded alerts for disaster management and public safety.

    Topics:
    Topic iotTopic aiTopic appTopic 3dTopic sensorsTopic mc
    Technologies used:
    Technology pytTechnology flutterTechnology ardTechnology fusnTechnology frbs
    About this project
    Landslide is a geological hazard caused when masses of rock and debris flow down a steep slope during periods of intense rainfall and accumulation of water. The proposed system is introduced for predetermining the possibility of an upcoming landslide by converting the landslide-prone areas into different zones and providing colour code-based warnings. Soil moisture and rainfall tracking sensors are incorporated in the node so as to detect the presence of the landslide triggering factor, i,e., the rainfall. Before the landslide happens, there will be internal movements inside the soil which will be detected using highly accurate vibration tilt sensors. The networking system is based on LoRa which has several advantages like long life and low power consumption. The tree network topology is utilized for the data transmission from nodes to the server via a respective gateway. It sends information to the district disaster management agency when it crosses the threshold value. Different terrains possess different threshold values which are analyzed from the previous landslide cases with the help of machine learning techniques. The entire region is categorized into several zones and the information related to landslides will be informed to the people residing via different media. By obtaining advanced information about the forthcoming disaster, people can shift and opt for the necessary actions accordingly.