Global Weather Forecasting System & Climate Analysis Platform

🌍 Overview

This project is a Global Weather Forecasting System and Climate Analysis Platform that utilizes AWS cloud services and IoT-based monitoring systems to collect and analyze real-time weather data. The system is designed to provide accurate location-based forecasts and climate insights, particularly for Andhra Pradesh.

🚀 Features

• Real-Time Weather Data Collection: Utilizes IoT devices with Arduino Nano, ESP32, and sensors to gather weather parameters.
• AWS Cloud Integration:
  • EC2 for computation
  • RDS for database management
  • S3 for data storage
  • Lambda for serverless processing
• Geospatial Data Processing: Implements PostgreSQL and PostGIS for accurate location-based climate forecasts.
• Machine Learning Models: Used for climate trend analysis and forecasting.
• Data Visualization: Interactive dashboards and charts for climate insights.

🛠️ Technologies Used

• AWS (EC2, RDS, Lambda, S3)
• Arduino Nano & ESP32
• PostgreSQL & PostGIS
• Python & Machine Learning Models
• Data Visualization Tools

🔧 Installation

Prerequisites

Ensure you have Python 3.x, PostgreSQL, and necessary AWS credentials configured.

Steps to Install

# Clone the repository
git clone https://github.com/your-username/weather-forecasting-platform.git
cd weather-forecasting-platform

# Install dependencies
pip install -r requirements.txt

🚀 Usage

1. Deploy IoT Sensors: Set up Arduino Nano, ESP32, and sensors for real-time data collection.
2. Run the Data Ingestion Pipeline:

   python data_ingestion.py

3. Process and Analyze Weather Data:

   python climate_analysis.py

4. Visualize Climate Insights: Access interactive dashboards for real-time climate data.

🌦️ Data Processing & Storage

• Weather data is collected from IoT sensors and stored in AWS RDS (PostgreSQL).
• PostGIS is used for geospatial data processing.
• AWS Lambda processes real-time data streams.

📊 Machine Learning & Visualization

• Implemented ML models for climate forecasting.
• Created visualization dashboards for data representation.

📌 Challenges Faced

• Real-Time Data Collection: Ensured continuous and error-free data flow from IoT sensors.
• Scalability on AWS: Optimized services to handle large-scale climate data efficiently.
• Geospatial Data Processing: Utilized PostGIS for accurate regional forecasting.

👨‍💻 Author

Karthikeyan