Skip to content

utkarshgupta2804/SHELTERRA

 
 

Repository files navigation

ShelTerra

Project Overview 🌍⛰️

🚨 Problem Statement In hilly regions, landslides pose a serious threat to life and infrastructure. Unstable land, triggered by rainfall, slope steepness, soil saturation, and other environmental factors, can collapse without warning. Early detection and prediction are crucial to saving lives and preventing disasters.

💡 Solution We have developed a hardware-software integrated system that analyzes land stability in real-time. Our system collects sensor data and uses a Machine Learning model trained on landslide datasets to predict the risk of a landslide.

🛠️ Tech Stack & Implementation

🔹 Hardware Components (connected through usb)

💧 Water Sensor → Checks soil moisture level (Soil Saturation)

📏 Tilt Sensor → Measures the slope angle

📡 Ultrasonic Sensor → Determines distance from the surface (for stability assessment)

🔹 Software & Backend

🧠 Machine Learning Model → Trained on landslide datasets with key factors: Rainfall, Slope Angle, Soil Saturation, Vegetation Cover Earthquake Activity, Proximity to Water, Soil Type

🖥️ Backend: Express.js (Node.js framework)

💾 Database: MongoDB (Stores real-time sensor data & predictions)

🖥️ Frontend: Next.js (React.js) for an interactive UI

🎨 Styling: Tailwind CSS for a clean and responsive design

🔐 Authentication: Clerk for secure access

🔄 Workflow

Sensors collect real-time data on slope stability. Data is stored in MongoDB and sent to the backend. Machine Learning model processes the data and predicts landslide risk. Results are displayed on a Next.js frontend with an intuitive dashboard. Users get alerts and warnings based on real-time analysis.

📌 Features

✅ Real-time sensor data collection

✅ ML modelling-based risk prediction

✅ User-friendly dashboard

✅ Secure authentication with Clerk

✅ Cloud-based MongoDB storage

google drive link:https://drive.google.com/drive/folders/1-J-QJX_FJ1uL6B3zufM59cavrkv2QM-t

rendor deployment url :

backend : https://shelterra-wugu.onrender.com

frontend : https://shelterra-1.onrender.com

ml model : https://shelterra-2.onrender.com

To browse webpage :

websearch -> https://shelterra-1.onrender.com

signin/signup to begin

search -> https://shelterra-1.onrender.com/dashboard

Enter Input -> fill the form

(‼️Predict output will show error : "Failed to process Prediction" => if sensors are not connected‼️)

Screenshot 2025-03-09 091817 Screenshot 2025-03-09 091925 Screenshot 2025-03-09 105944

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • TypeScript 59.3%
  • JavaScript 21.6%
  • Python 13.3%
  • C++ 3.3%
  • CSS 2.5%