🌊 Flood Risk Prediction System

Predicting flood occurrence using Machine Learning algorithms based on environmental and geographical factors.

---

📌 Project Overview

Floods are among the most destructive natural disasters, causing significant damage to human life, infrastructure, and the environment.

This project uses Machine Learning techniques to predict whether a flood is likely to occur based on various environmental and geographical parameters. Three different classification algorithms are implemented and compared to identify the best-performing model.

---

🎯 Objectives

• Predict flood occurrence using historical environmental data.
• Compare the performance of multiple Machine Learning models.
• Identify the most accurate prediction model.
• Build a simple and efficient flood prediction system.

---

📂 Dataset Features

The dataset contains the following features:

• 📍 Latitude
• 📍 Longitude
• 🌧 Rainfall (mm)
• 🌡 Temperature (°C)
• 💧 Humidity (%)
• 🌊 River Discharge (m³/s)
• 📈 Water Level (m)
• ⛰ Elevation (m)
• 🌿 Land Cover
• 🌱 Soil Type
• 👥 Population Density
• 🏗 Infrastructure
• 📚 Historical Floods

Target Variable

• Flood Occurred
  • 0 → Low Flood Risk
  • 1 → High Flood Risk

---

🛠 Technologies Used

• Python
• Google Colab
• Pandas
• NumPy
• Scikit-learn
• Matplotlib
• Seaborn

---

🤖 Machine Learning Models

The following models are implemented:

• Logistic Regression Classifier
• Decision Tree Classifier
• Random Forest Classifier

---

⚙ Project Workflow

Dataset
    │
    ▼
Data Preprocessing
    │
    ▼
Encoding Categorical Data
    │
    ▼
Train-Test Split
    │
    ▼
Model Training
    │
    ▼
Prediction
    │
    ▼
Performance Evaluation

---

📊 Model Performance

Model	Accuracy
Logistic Regression	90.15%
Decision Tree Classifier	99.40%
Random Forest Classifier	99.55%

Best Performing Model: Logistic Regression Classifier

---

📁 Project Structure

Project_1_Flood_Risk_Prediction_System_Using_Machine_Learning/
│
├── Flood_Risk_Prediction_System.ipynb
├── Flood_Risk_Prediction_System.py
├── Dataset/
│   ├── flood_risk_india.csv
├── images/
│   ├── confusion_matrix.png
│   ├── feature_importance.png
│   └── model_accuracy_comparison.png
├── best_model.pkl
├── feature_names.pkl 
├── label_encoders.pkl
├── scaler.pkl 
├── requirements.txt
├── README.md
└── LICENSE

---

🚀 Installation

Clone the repository

git clone https://github.com/Tuhin092005/Project_1_Flood_Risk_Prediction_System_Using_Machine_Learning.git

Navigate to the project

cd Project_1_Flood_Risk_Prediction_System_Using_Machine_Learning

Install required libraries

pip install -r requirements.txt

▶️ Run the project

Using Python

python Flood_Risk_Prediction_System.py

Using Google Colab Using Python

Open

Flood_Risk_Prediction_System.ipynb

Upload:

• flood_risk_india.csv

Run all cells.

---

📈 Sample Predictions

The model predicts flood risk using two different input scenarios.

🧪 Example Prediction 1 (High Flood Risk)

Input

Latitude             : 22.57
Longitude            : 88.36
Rainfall             : 250 mm
Temperature          : 30°C
Humidity             : 85%
River Discharge      : 500 m³/s
Water Level          : 8 m
Elevation            : 10 m
Land Cover           : 2
Soil Type            : 1
Population Density   : 5000
Infrastructure       : 1
Historical Floods    : 3

Expected Output

Prediction : 1

Flood Risk : HIGH

A location with heavy rainfall, high humidity, high water level, and multiple historical floods is predicted as High Flood Risk.

---

🧪 Example Prediction 2 (Low Flood Risk)

Input

Latitude             : 23.50
Longitude            : 87.50
Rainfall             : 40 mm
Temperature          : 28°C
Humidity             : 55%
River Discharge      : 80 m³/s
Water Level          : 2 m
Elevation            : 120 m
Land Cover           : 1
Soil Type            : 2
Population Density   : 300
Infrastructure       : 3
Historical Floods    : 0

Expected Output

Prediction : 0

Flood Risk : LOW

A location with low rainfall, lower humidity, higher elevation, and no historical floods is predicted as Low Flood Risk.

---

📌 Future Improvements

• Hyperparameter tuning
• Cross-validation
• Feature importance analysis
• Interactive web application using Streamlit or Flask
• Real-time weather API integration
• Interactive dashboard for visualization

---

👨‍💻 Author

Tuhin Maji

B.Tech in Computer Science & Engineering (AI & ML)

Meghnad Saha Institute of Technology (MSIT), Kolkata

---

⭐ If you found this project useful

Give this repository a ⭐ and feel free to fork it!

---

📜 License

This project is intended for educational and learning purposes.

---

⭐ Support

If you like this project,

⭐ Star this repository

🍴 Fork this repository

📢 Share it with others

Happy Coding! 🚀