Climate Crisis Prediction with ARIMA Model for Turkey

Project Overview

This research project provides a comprehensive time series analysis and prediction system for climate change indicators in Turkey. The project combines advanced statistical modeling using ARIMA (Autoregressive Integrated Moving Average) with a user-friendly web interface, enabling stakeholders to access and visualize future climate predictions for critical environmental indicators.

Research Significance

Understanding and predicting climate change patterns is crucial for environmental policy-making and mitigation strategies. This project addresses this need by providing:

Data-driven predictions for key climate indicators
Interactive visualization of historical trends and future projections
Comprehensive analysis of multiple climate variables affecting Turkey
Accessible interface for stakeholders to explore climate predictions

Methodology

Data Collection and Processing

The study utilizes historical climate data from Turkey, focusing on seven key indicators:

Carbon Dioxide emissions
Methane emissions
Nitrous Oxide emissions
Fluorinated Gases emissions
Total Greenhouse Gas emissions
Temperature changes
Forest Area percentage

Key data processing implementations include:

pythonCopy# Data preprocessing and formatting
data = pd.read_excel(file_path)
data['Year'] = pd.to_datetime(data['Year'], format='%Y')
data = data.set_index('Year')

# Variable selection for analysis
variables = [
    'Carbon Dioxide (Million metric tons of CO2 equivalent)',
    'Methane (Million metric tons of CO2 equivalent)',
    'Nitrous Oxide (Million metric tons of CO2 equivalent)',
    'Fluorinated Gases (Million metric tons of CO2 equivalent)',
    'Total GHG (Million metric tons of CO2 equivalent)',
    'Temperature (Celcius)',
    'Forest Area (%)'
]

Statistical Analysis Framework

The project employs the ARIMA model through the pmdarima library, implementing automatic parameter selection for optimal forecasting:

pythonCopystepwise_model = pm.auto_arima(train_data, 
                              start_p=1, start_q=1,
                              max_p=5, max_q=5, m=12,
                              seasonal=True, D=1,
                              d=None, trace=False,
                              error_action='ignore',
                              suppress_warnings=True,
                              stepwise=True)

Web Application Implementation

The project features a Flask-based web interface that allows users to:

Select specific climate indicators for analysis
Choose future prediction timeframes
Visualize historical data and predictions
Compare multiple indicators simultaneously

Key implementation features:

pythonCopy@app.route('/result', methods=['POST'])
def result():
    indicator = request.form['indicator']
    year = int(request.form['year'])
    
    # Process predictions for selected indicators
    results = {}
    plot_data = []
    indicators_to_predict = variables if indicator == 'Predict All Indicators' else [indicator]
    
    for ind in indicators_to_predict:
        # Model training and prediction logic
        forecast = stepwise_model.predict(n_periods=forecast_steps)
        results[ind] = format_result(forecast[-1], ind)

Visualization and Results

Web Interface

The web application features a clean, intuitive interface for users to select climate indicators and prediction timeframes.

Statistical Visualizations

Some of the visualizations look like that:

The analysis includes detailed visualizations of historical trends and future projections for each climate indicator.ARIMA model diagnostics ensure prediction reliability and model accuracy.

Technical Stack

Python for core statistical analysis
Flask for web application framework
Pandas and NumPy for data processing
pmdarima for time series modeling
Matplotlib and Plotly for visualization
Bootstrap for responsive web design

Impact and Applications

This project provides valuable insights for:

Environmental policy makers
Climate researchers
Public awareness initiatives
Urban planning and development
Environmental impact assessments

Future Directions

The project can be extended to include:

Integration of additional climate indicators
Machine learning-based prediction improvements
Regional-level analysis capabilities
Real-time data updates and predictions
Enhanced visualization features

Conclusion

This climate crisis prediction system demonstrates the practical application of statistical modeling and web development for environmental analysis. By combining robust ARIMA modeling with an accessible web interface, the project provides a valuable tool for understanding and predicting climate change patterns in Turkey.

Note: The project includes an extensive test suite and comprehensive documentation to ensure reliability and maintainability.