SHORT SUMMARY
The research focuses on predicting future air pollution levels by using population growth trends as a key determinant. Five machine learning models were implemented and evaluated: linear regression, ridge regression, random forest regression, XGBoost, and MLP regression. The study utilized two datasets: a global air pollution dataset and a metropolis population growth dataset, merged to establish correlations between population growth and air quality indices (AQI).
The performance of the models was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²) as metrics. The random forest regression model demonstrated the best performance across all metrics, while linear regression performed the worst. However, the results indicate room for improvement through optimization and the inclusion of more diverse datasets and features.
Key contributions of the study include:
- Developing a predictive framework for air pollution trends based on historical AQI and population growth data.
- Highlighting the superior performance of ensemble models like random forest in handling non-linear relationships.
- Emphasizing the necessity of optimization and additional datasets for better predictions.
Key Takeaways:
- Importance of Air Pollution Prediction:
- Predicting air pollution levels is crucial for effective air quality management and mitigating risks to human health caused by pollutants such as PM2.5, nitrogen dioxide, and sulfur dioxide.
- Model Comparison:
- Among the five tested models, random forest regression had the lowest error rates (RMSE = 43.33, MAE = 26.23) and the highest R² (0.46579), making it the most effective for the study.
- Simpler models like linear and ridge regression exhibited inferior performance due to their inability to capture complex patterns.
- Data and Preprocessing:
- The datasets were merged and cleaned, with missing values addressed, and population columns were converted for growth rate calculation. The final data was split into training and testing sets (80% training, 20% testing).
- Room for Improvement:
- The study acknowledges limitations in the datasets and emphasizes the need for further optimization and inclusion of additional features to improve predictive accuracy.
- Practical Application:
- The results can aid policymakers and environmental planners in designing strategies for air quality improvement by providing insights into future pollution trends based on demographic growth.
Who Can Benefit from This Research?
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Policymakers and Government Agencies
- Environmental Protection Agencies: Use the predictive insights to design and implement policies that mitigate air pollution, such as promoting renewable energy and controlling emissions.
- Urban Planners: Predict future air quality issues in growing urban areas and integrate these insights into sustainable urban development strategies.
- Public Health Departments: Assess the potential health impacts of air pollution trends and develop targeted interventions, such as awareness campaigns or healthcare resource allocation.
Researchers and Data Scientists
- Environmental Researchers: Use the methodologies and findings as a foundation for further studies, especially to incorporate additional datasets or explore more advanced models.
- AI and ML Practitioners: Improve machine learning models for air quality prediction by building on this research’s framework and findings.
Non-Governmental Organizations (NGOs)
- Organizations focused on climate change and environmental advocacy can use the research to highlight the impact of urbanization on air quality and advocate for cleaner practices.
Industries
- Energy and Manufacturing Sectors: Identify how population-driven pollution trends might impact regulations and adapt their practices accordingly to comply with future standards.
- Smart City Initiatives: Incorporate predictive models into smart city infrastructures, particularly for air quality monitoring systems.
General Public
- Health-Conscious Individuals: Gain awareness about the potential health risks from air pollution and take preventive measures.
- Communities in High-Risk Areas: Understand how population growth might affect their local air quality and advocate for preventive actions.
Educational Institutions
- Incorporate the findings and methodologies into curriculum topics related to environmental science, data science, and urban planning.
Technology Developers
- Companies building IoT devices for air quality monitoring can use this research to refine prediction algorithms and integrate them into their systems.