In a village nestled against the unpredictable mountains, a young engineer named Leo creates a revolutionary AI system to predict landslides. This inspiring story blends cutting-edge technology with a high-stakes race against nature, showing how innovation can protect communities and save lives. Follow Leo's journey from building complex sensors to deploying a life-saving mobile alert system.
Project Title: AI-Based Landslide Monitoring and Early Warning System Objective: Develop a complete mobile and web application that predicts landslides using multiple environmental sensors and machine learning. The system should extract algorithm and sensor details from the uploaded PDF and implement them into the application logic. Application Structure: 1. Login Page Create the first page as a secure login screen where users log in using: * Phone number * Password Features: * Phone number validation * Secure authentication * Option for new user registration * Store login credentials securely in the backend database 2. Dashboard Page (Real-Time Monitoring) After login, redirect users to the dashboard showing real-time landslide monitoring data. Display real-time sensor values including: * Soil moisture sensor * Rainfall sensor * Temperature sensor * Ground vibration sensor * Tilt sensor * Humidity sensor The dashboard should include: * Live sensor readings * Graphs of sensor data over time * Risk level indicator (Low, Medium, High) * Map showing monitoring location * Timestamp of latest readings 3. Sensor Fusion System Implement a sensor fusion algorithm combining multiple sensor inputs to detect landslide risk. Inputs: * Rainfall intensity * Soil moisture level * Ground vibration * Slope tilt angle * Temperature Process: Normalize and combine sensor readings using a machine learning model such as: * Random Forest * Support Vector Machine * XGBoost Output: A landslide risk score between 0 and 1. 4. Alert System Develop an alert mechanism that triggers warnings when risk levels exceed a threshold. Alert types: * Mobile push notifications * SMS alerts * Dashboard warning messages * Color-coded risk indicators Risk Levels: 0–0.3 = Safe 0.3–0.7 = Warning 0.7–1.0 = High Risk 5. PDF Data Extraction Automatically analyze the uploaded PDF document and extract the following information: * Algorithms used * Sensor types * System architecture * Data flow process * Risk prediction model * Key parameters used for landslide prediction Use the extracted information to configure the machine learning model and sensor fusion logic inside the application. 6. Backend System Develop a backend API using Python. Framework: FastAPI or Flask Functions: * User authentication * Real-time sensor data storage * ML prediction API * Alert triggering * Data analytics Database: Use Firebase or MongoDB to store: * User accounts * Sensor readings * Prediction results * Alert logs 7. Real-Time Data Processing The system must continuously process incoming sensor data and update the dashboard in real time. Implementation: * WebSockets or REST API polling * Data refresh every 5 seconds 8. Mobile Application Develop the frontend using Flutter. Screens required: * Login page * Registration page * Real-time monitoring dashboard * Alerts page * Sensor analytics graphs UI requirements: * Clean modern interface * Real-time charts * Color-coded warnings * Responsive design 9. Deployment Deploy the complete system with the following components: Backend API: Deploy on Render or AWS Machine Learning Model: Deploy as a prediction API Mobile App: Build Android APK using Flutter Dashboard: Accessible through the mobile app 10. Output Provide the following deliverables: * Complete source code * Flutter mobile application * Python backend API * Machine learning model * Real-time dashboard * Alert notification system * Instructions to run and deploy the application Goal: Create a fully functional AI-powered landslide early warning system capable of monitoring environmental conditions, predicting landslide risks using sensor fusion, and alerting users in real time.
