🌊 GeoWindNet: Smart Seafloor Wind Farm Suitability Predictor 🌬️

What is GeoWindNet?

GeoWindNet is a cutting-edge machine learning project designed to help renewable energy experts find the perfect spots for offshore wind farms. Imagine having a smart assistant that can look at complex seafloor data and tell you exactly where you should place wind turbines – that's exactly what this project does!

🎯 Project Mission

Our goal is simple but powerful: Use artificial intelligence to make offshore wind farm placement smarter, faster, and more efficient.

🚀 Key Features

• Smart Data Analysis: Uses advanced machine learning to process complex geospatial data
• Precision Prediction: Determines seafloor suitability with high accuracy
• Flexible Framework: Easy to adapt and customize for different datasets
• Comprehensive Evaluation: Provides detailed performance metrics

🛠️ How It Works

Data Processing

1. Loads seafloor data from a CSV file
2. Preprocesses and standardizes input features
3. Transforms data into a format perfect for neural network analysis

Machine Learning Magic

• Uses a Convolutional Neural Network (CNN)
• Automatically finds the best model configuration
• Optimizes performance through intelligent hyperparameter tuning

Performance Metrics

GeoWindNet doesn't just predict – it proves its worth by measuring:

• Precision
• Recall
• F1-Score
• AUC-ROC
• Detailed Confusion Matrix

🔧 Prerequisites

Software Requirements

• Python 3.8+
• Libraries:

  pip install tensorflow keras scikit-learn pandas numpy seaborn matplotlib keras-tuner

📦 Project Structure

GeoWindNet/
│
├── data/
│   └── seafloor_data.csv     # Your input dataset
│
├── logs/                     # Execution logs
│   └── geowindnet.log
│
├── hyperparam_logs/          # Hyperparameter tuning details
│
├── geowindnet.py             # Main project script
│
└── geowindnet_final.h5       # Trained model

🚀 Quick Start Guide

1. Prepare Your Data

   • Place your seafloor dataset in ./data/seafloor_data.csv
   • Ensure it has numerical features and a binary 'suitability' column

2. Run the Project

   python geowindnet.py

3. Check Results

   • Console output shows performance metrics
   • Logs saved in logs/geowindnet.log
   • Trained model saved as geowindnet_final.h5

🔬 Customization Options

Hyperparameter Tuning

Easily adjust the search space:

tuner = kt.RandomSearch(
    build_hypermodel,
    objective='val_accuracy',
    max_trials=10,  # Increase for more exploration
    directory='hyperparam_logs',
    project_name='GeoWindNet_Tuning'
)

Model Architecture

Modify CNN layers in build_model() to suit your specific needs.

🌈 Future Roadmap

• Integrate more diverse geospatial datasets
• Expand to multi-class suitability predictions
• Develop web API for real-time predictions
• Implement advanced data augmentation techniques

🤝 Contributing

Love the project? Here's how you can help:

• Report bugs
• Suggest features
• Submit pull requests
• Share your use cases