Technological Frontier on Hybrid Deep Learning Paradigm for Global Air Quality Intelligence

Abstract

This hybrid deep-learning study focuses on pollutant concentration. It illuminates convolutional neural networks (CNN) and long short-term memory in hybrid deep learning methods (LSTM). CNNs are essential to deep learning, especially image processing. They are ideal for pollution concentration analysis because they extract complex data features. LSTM is another important tool for this study. LSTMs are recurrent neural networks (RNNs) that can process and store data sequences. Time-series data analysis, common in pollution concentration research, benefits from them. Understanding deep learning and hybrid learning's impact on pollutant concentration issues. It investigates a hybrid CNN-LSTM model that combines CNN feature extraction with LSTM sequence processing. This fusion lets the model make smart predictions from input data sequences. PCA is key to this investigation. PCA dimensionality reduction finds variables with significant relationships.