A CNN-LSTM HYBRID MODEL FOR PREDICTING AIR QUALITY AND DETECTING ANOMALIES WITH GAUSSIAN APPROXIMATION

Air pollution is a global issue affecting the health of people, the sustainability of the environment, and the planning of urban areas. The present work utilises a smart air quality data monitoring analysis system that uses machine learning algorithms in forecasting and studying atmospheric pollution concentration. Multi-pollutant forecasting in Ust- Kamenogorsk utilises the collaborative use of LSTM and CNN. The Gaussian approximation is used in detecting outliers and meteorological input is added in order to support predictive precision. The LSTM-CNN blended model was utilized in predicting the concentration of different contaminants, including PM2.5, PM10, NO2, SO2, CO, and O3. The predictive accuracy of the model was average, considering its Root Mean Squared Error (RMSE) of 0.3297. Mean absolute error (MAE) was 0.2741, indicating differences in prediction ability among contaminants. However, R² score at -0.3210 suggests that the model needs to be tuned for greater predictability. Identification of outliers was done through residual analysis, which provided a 1.0 recall but poor precision of 0.0676, indicating high false positive rate. Despite its limitations, the model has the capacity to anticipate air quality in real time and detect anomalies. Future enhancements will include hyperparameter optimization, the addition of new data sources, and the refining of the anomaly detection method for greater accuracy and dependability. This contribution goes toward the development of intelligent air quality monitoring technologies to support data-driven environmental management and policy.