Enhancing Air Quality Prediction through CNN-LSTM Based Deep learning Model:

Abstract

Air pollution has transformed into a critical environmental issue which endangers human health in nations that undergo rapid industrial development such as India. Air quality prediction requires precise outcomes because its two main purposes include creating early warning systems and designing successful public policies. The research presents a deep learning approach which combines Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM) with a hybrid CNN-LSTM model to classify air quality. The models operate on an Air Quality Index (AQI) dataset which researchers processed to include essential pollutant data that shows PM2.5 and PM10 and NO and CO and SO levels. The methodology includes data preprocessing activities which involve treating missing data and conducting normalization procedures and assigning AQI levels to Low Moderate and High categories. The CNN model extracts spatial feature patterns from the data while the LSTM model uses its temporal dependencies to analyze the information. The hybrid CNN-LSTM model implements two techniques which enhance its performance in delivering precise forecasts. The hybrid model shows superior performance compared to standalone CNN and LSTM models because it achieves better classification results and maintains greater model consistency. The research team used multiple metrics to evaluate performance which included accuracy and precision and recall and F1-score and confusion matrix assessment. The system created by researchers enables precise real-time air quality forecasting which supports environmental monitoring organizations in making educated choices.