Climate-Risk-And-Disaster-Management

🌊 Leveraging Machine Learning for Flood Event Forecasting

This project aims to develop a robust machine learning-based system for forecasting flood events using historical rainfall data and other hydrological indicators. By leveraging predictive analytics, it helps improve early warning systems and disaster preparedness.

📌 Table of Contents

• Features
• Data Sources
• Installation
• Usage
• Model Architecture
• Results
• Contributing
• License
• Contact

🚀 Features

• Predicts flood likelihood based on rainfall and environmental data
• Supports multiple machine learning models (Random Forest, XGBoost, LSTM)
• Visualizes rainfall trends and flood risk zones
• Modular pipeline for data preprocessing, training, and evaluation

🌧️ Data Sources

• Historical rainfall data (e.g., IMD, NOAA)
• River discharge and water level data
• Satellite imagery (optional)
• Ground truth flood event records

💻 Installation

Clone the repository and install dependencies:

git clone https://github.com/yourusername/Climate-Risk-And-Disaster-Management.git
cd flood-forecasting-ml
pip install -r requirements.txt

🛠️ Usage
Run the pipeline with your dataset:
python main.py --data ./data/rainfall.csv --model random_forest

Or train a deep learning model:
python train_lstm.py --epochs 50 --data ./data/rainfall.csv


# 🌊 Leveraging Machine Learning for Flood Event Forecasting

This project aims to develop a robust machine learning-based system for forecasting flood events using historical rainfall data and other hydrological indicators. By leveraging predictive analytics, it helps improve early warning systems and disaster preparedness.


🛠️ Usage
Run the pipeline with your dataset:
python main.py --data ./data/rainfall.csv --model random_forest


Or train a deep learning model:
python train_lstm.py --epochs 50 --data ./data/rainfall.csv

🧠 Model Architecture

|Model          |Description                |Use Case                 |
_______________________________________________________________________
|Random Forest  |Ensemble tree-based model  |Fast, interpretable      | 
|XGBoost        |Gradient boosting          |High accuracy            | 
|LSTM           |Recurrent neural network   |Time-series forecasting  | 


📊 Results
- Accuracy: 92% (Random Forest)
- Precision: 89%
- Recall: 94%
- ROC-AUC: 0.96
Visualizations and confusion matrices are available in the results/ folder.

🤝 Contributing
We welcome contributions! Please fork the repo and submit a pull request. For major changes, open an issue first to discuss what you'd like to change.

📄 License
This project is licensed under the MIT License. See the LICENSE file for details.

🙋‍♀️ Contact
Created by Anahita
📧 Email: anahita.singh.31@gmail.com
🌐 LinkedIn: https://www.linkedin.com/in/anahita31/

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