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Soft Computing Techniques for Atmospheric Pollution and Traffic Emission Prediction
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DOI：10.1016/j.envsoft.2025.106828.png)
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Abstract
En 中文
• Soft computing models are reviewed for air pollution and traffic emission prediction. • Hybrid AI models improve forecasting accuracy and adaptability in urban settings. • Comparative analysis of ANN, fuzzy logic, GA, and LSTM models is presented. • Integration with meteorological and traffic data enhances predictive performance. • Future scope includes uncertainty quantification and real-time smart city deployment.
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4.6
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511
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