https://github.com/manishsec/ai-floodguard
🎯 Smart flood prediction system for Punjab using AI & ML to save lives! 🤖 💡 🗺️ Real-time risk mapping with beautiful dashboards and mobile alerts! 📱 ⚡ 🔮 Powered by Python, TensorFlow, and historical weather data from IMD! 🌧️ 📊
https://github.com/manishsec/ai-floodguard
ai disaster earthquakes python
Last synced: 3 months ago
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🎯 Smart flood prediction system for Punjab using AI & ML to save lives! 🤖 💡 🗺️ Real-time risk mapping with beautiful dashboards and mobile alerts! 📱 ⚡ 🔮 Powered by Python, TensorFlow, and historical weather data from IMD! 🌧️ 📊
- Host: GitHub
- URL: https://github.com/manishsec/ai-floodguard
- Owner: Manishsec
- License: mit
- Created: 2025-03-29T10:21:49.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-03-29T10:52:03.000Z (7 months ago)
- Last Synced: 2025-06-29T21:04:25.522Z (4 months ago)
- Topics: ai, disaster, earthquakes, python
- Language: Python
- Homepage:
- Size: 1.58 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🌊 AI-Powered Flood Risk Prediction System for Chandigarh/Punjab Region
[](https://www.python.org/downloads/)
[](LICENSE)
[](https://github.com/yourusername/flood-risk-prediction)
[](https://github.com/yourusername/flood-risk-prediction)
[](https://github.com/yourusername/flood-risk-prediction/wiki)
## 📝 Description
This project implements an AI-powered flood risk prediction and visualization system for the Chandigarh/Punjab region. It uses historical weather data to predict flood risk levels and provides interactive visualizations on a map interface.
### 🌟 Key Features
- 🔍 Accurate flood risk prediction using machine learning
- 🗺️ Interactive map visualization
- 📊 Comprehensive data analysis
- 📈 Real-time risk assessment
- 🔄 Automated data preprocessing
- 📱 Mobile-friendly interface
### 📸 Project Screenshots
#### 🗺️ Risk Map Visualization (PNG)
Static visualization of flood risk zones in Chandigarh/Punjab region
#### 📊 Risk Distribution Analysis
Distribution of flood risk levels across different areas
#### 📈 Rainfall Pattern Analysis
Historical rainfall patterns and trends over time
#### 🔍 Risk Assessment Dashboard
Comprehensive dashboard showing risk metrics and predictions
#### 📱 Mobile Interface
Mobile-friendly interface for on-the-go risk assessment
> Note: These screenshots were generated using example data. The actual implementation may vary based on your specific data and requirements.
## 📑 Table of Contents
1. [🚀 Installation](#installation)
2. [📁 Project Structure](#project-structure)
3. [📊 Data](#data)
4. [🤖 Model](#model)
5. [💻 Usage](#usage)
6. [📈 Visualization](#visualization)
7. [🔮 Future Improvements](#future-improvements)
8. [📄 License](#license)
9. [🙏 Acknowledgments](#acknowledgments)
## 🚀 Installation
### ⚙️ Prerequisites
- Python 3.8 or higher
- pip (Python package installer)
- Git (for version control)
- Virtual environment tool (venv or conda)
### 🛠️ Setting up the Environment
1. Clone the repository:
```bash
git clone [](https://github.com/Cryptonomicon-Manish-dev/AI-FloodGuard.git)
cd AI-FloodGuard
```
2. Create and activate a virtual environment:
Using venv (recommended):
```bash
# Create virtual environment
python -m venv venv
# Activate virtual environment
# On Windows
venv\Scripts\activate
# On Unix or MacOS
source venv/bin/activate
# Verify activation
python --version
pip --version
```
Using conda:
```bash
# Create conda environment
conda create -n flood-risk python=3.8
# Activate conda environment
conda activate flood-risk
# Verify activation
python --version
pip --version
```
3. Install dependencies:
```bash
# Upgrade pip first
python -m pip install --upgrade pip
# Install project dependencies
pip install -r requirements.txt
# Verify installations
pip list
```
4. Generate example screenshots (optional):
```bash
# Make sure you're in the project root directory
python src/generate_screenshots.py
```
### 🔍 Environment Verification
After installation, verify your environment:
```bash
# Check Python version
python --version
# Check installed packages
pip list
# Test the environment
python -c "import folium; import pandas; import numpy; import matplotlib; import seaborn; print('All packages imported successfully!')"
```
### ⚠️ Troubleshooting
If you encounter any issues:
1. Make sure your virtual environment is activated (you should see `(venv)` or `(flood-risk)` in your terminal prompt)
2. Try reinstalling dependencies:
```bash
pip uninstall -r requirements.txt -y
pip install -r requirements.txt
```
3. If using conda, try:
```bash
conda install --file requirements.txt
```
## 📁 Project Structure
```
flood-risk-prediction/
├── data/
│ ├── raw/ # Raw data files
│ └── processed/ # Processed data files
├── models/ # Saved model files
├── src/
│ ├── data_preprocessing.py
│ ├── model.py
│ ├── visualization.py
│ ├── utils.py
│ └── generate_screenshots.py
├── screenshots/ # Generated visualization screenshots
├── notebooks/ # Jupyter notebooks for analysis
├── requirements.txt
└── README.md
```
## 📊 Data
### 📥 Data Sources
- Historical rainfall data from India Meteorological Department (IMD)
- Geographic data for Chandigarh/Punjab region
- Historical flood event records
### 📋 Data Format
The system expects the following data formats:
- Rainfall data: CSV files with columns (Date, Location, Rainfall_Amount)
- Geographic data: GeoJSON or Shape files
- Historical flood records: CSV files with flood event details
### 🔧 Data Preprocessing
The system includes preprocessing steps for:
- Handling missing values
- Outlier detection and removal
- Feature scaling
- Feature engineering
## 🤖 Model
### 🏗️ Model Architecture
The system uses a combination of:
- Random Forest Classifier for risk level prediction
- Neural Network for pattern recognition in historical data
### 📚 Model Training
The model is trained on:
- Historical rainfall data
- Geographic features
- Previous flood events
### 📊 Model Evaluation
Performance metrics include:
- Accuracy
- Precision
- Recall
- F1-score
- ROC-AUC curve
## 💻 Usage
1. Prepare your data:
```bash
python src/data_preprocessing.py --input_path data/raw --output_path data/processed
```
2. Train the model:
```bash
python src/model.py --train
```
3. Run predictions:
```bash
python src/model.py --predict --input data/processed/test_data.csv
```
4. Generate visualizations:
```bash
python src/visualization.py --risk_level high
```
## 📈 Visualization
The system provides:
- 🗺️ Interactive maps showing risk zones
- 📊 Risk level heatmaps
- 📈 Historical flood event visualization
- 🌧️ Rainfall pattern analysis
## 🔮 Future Improvements
1. 🌐 Integration with real-time weather data
2. 📱 Mobile application development
3. 🎨 Enhanced visualization features
4. 🤖 Additional machine learning models
5. 🔌 API development for external access
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🙏 Acknowledgments
- India Meteorological Department for providing weather data
- OpenStreetMap for geographic data
- Contributors and maintainers of all used libraries
## 🤝 Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## 📞 Support
If you encounter any issues or have questions, please:
1. Check the [Issues](https://github.com/yourusername/flood-risk-prediction/issues) page
2. Create a new issue if needed
3. Contact the maintainers
## 🔍 Version History
- v1.0.0 (2024-03-29)
- Initial release
- Basic flood risk prediction
- Interactive map visualization
## 📝 Notes
- The system requires a stable internet connection for map visualization
- Large datasets may require additional processing time
- Regular updates to the model are recommended for better accuracy