Philippine Earthquake Prediction Dashboard

Overview

This interactive dashboard provides earthquake risk assessment for the Philippines using machine learning algorithms. The system combines DBSCAN clustering and XGBoost classification to predict significant seismic events (magnitude ≥ 4.0) based on location, depth, and regional factors.

Features

• 📍 Real-time Prediction: Enter coordinates to get immediate risk assessment
• 📊 Data Visualization: Interactive charts showing magnitude, regional, and depth distributions
• 🗺️ Regional Analysis: Breakdown of seismic activity across Luzon, Visayas, and Mindanao
• 🛡️ Safety Recommendations: Contextual advice based on prediction results
• 📈 Performance Metrics: Live display of model accuracy and statistics

Technology Stack

• Frontend: HTML5, CSS3, JavaScript with Chart.js for visualizations
• Backend: Node.js HTTP server
• ML Model: XGBoost classifier with DBSCAN clustering
• API: RESTful endpoints for data and predictions

Installation & Setup

Prerequisites

• Node.js (v14 or higher)

Quick Start

1. Clone or download the repository
2. Navigate to the project directory
3. Install dependencies:

   npm install

4. Start the server:

   npm start

5. Open your browser and go to http://localhost:8000

Alternative Start

You can also run the server directly:

node server.js

Usage

Making Predictions

1. Enter latitude and longitude coordinates
2. Input depth in kilometers
3. Click "Predict Risk Level"
4. View results with confidence percentage
5. See safety recommendations if needed

Dashboard Sections

• Prediction Panel: Input coordinates for risk assessment
• Statistics: Model performance metrics
• Visualizations: Charts showing seismic patterns
• Data Table: Recent earthquake information

API Endpoints

GET /api/prediction-stats

Returns model performance statistics:

{
  "accuracy": 99.5,
  "clusters": 2,
  "data_points": 10000,
  "significant_events": 1247
}

GET /api/recent-earthquakes

Returns recent earthquake data:

[
  {
    "date": "2025-01-15",
    "location": "Surigao Del Sur",
    "lat": 9.0,
    "lon": 125.8,
    "depth": 35.2,
    "magnitude": 4.5,
    "significant": true
  }
]

POST /api/predict

Makes a prediction based on coordinates:

{
  "latitude": 9.0,
  "longitude": 125.8,
  "depth": 35.2
}

Response:

{
  "is_significant": true,
  "confidence": 0.85,
  "cluster_id": 1,
  "location": {
    "latitude": 9.0,
    "longitude": 125.8,
    "depth": 35.2
  }
}

Machine Learning Model

Clustering

• Algorithm: DBSCAN (Density-Based Spatial Clustering)
• Parameters: eps=0.05, min_samples=5
• Purpose: Identify persistent seismic zones in the Philippines

Classification

• Algorithm: XGBoost (Extreme Gradient Boosting)
• Target: Binary classification (significant vs non-significant)
• Features: Latitude, longitude, depth, cluster assignment
• Performance: 99.5% accuracy

Key Insights

• Mindanao has the highest rate of significant earthquakes (5.47%)
• Top predictive features: Latitude, longitude, depth, regional clustering
• High-risk areas: Surigao del Sur, Davao Occidental, Eastern Mindanao

Safety Recommendations

The dashboard provides contextual safety advice based on prediction results:

For Significant Predictions:

• 🚨 Drop, Cover, and Hold - Take cover under a sturdy desk or table
• 🚪 Keep exit routes clear and know your evacuation plan
• 📱 Stay updated with PHIVOLCS alerts and local emergency services
• 📦 Prepare emergency kit with water, food, flashlight, and first aid
• 🏗️ Check building safety and retrofit if necessary

For Non-Significant Predictions:

• ✅ Remain calm - This is a minor seismic event
• 📊 Continue monitoring for updates from PHIVOLCS
• 🏠 Check for minor damages in your immediate area
• 📞 Inform local authorities if you notice any issues

Data Sources

• Primary: PHIVOLCS (Philippine Institute of Volcanology and Seismology)
• Dataset: Historical earthquake records from 2016-2025
• Records: Over 100,000 earthquake events processed

Project Structure

├── index.html          # Main dashboard page
├── server.js           # Node.js server implementation
├── package.json        # Project dependencies and scripts
└── README.md           # This documentation

Important Note

This system provides risk assessment based on historical data patterns. Actual earthquake prediction remains scientifically impossible. The dashboard should be used as a supplementary tool alongside official PHIVOLCS information and local emergency services.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• PHIVOLCS for providing earthquake data
• Machine learning community for algorithms and techniques
• Disaster preparedness organizations for safety guidelines