A LSTM Deep Learning method with Attention Dense Mechanism for AQI prediction

Accurate prediction of PM2.5 concentrations is crucial in addressing the severe air pollution issues faced by countries like India. In this study, we propose a comprehensive algorithm that combines Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), attention mechanisms, and dense layers for PM2.5 prediction while incorporating meteorological features specific to the Indian context. To evaluate the algorithm's performance on Indian data, we collected a large dataset consisting of historical PM2.5 concentrations and associated meteorological features from monitoring stations across India. The dataset underwent preprocessing, including normalization and scaling, to ensure consistent input ranges for the models. Through experimental comparisons among the CNN, LSTM, CNN-LSTM, and CNN-LSTM-Attention-Dense algorithms using the Indian dataset, our findings demonstrated that the CNN-LSTM-AttentionDense algorithm outperformed the other algorithms, exhibiting the highest prediction accuracy for PM2.5 concentrations in the Indian context. By harnessing the spatial and temporal capabilities of CNNs and LSTMs, coupled with attention mechanisms and dense layers, our proposed algorithm effectively captured the intricate patterns and relationships between Indian meteorological conditions and PM2.5 concentrations. The integration of attention mechanisms enabled the model to focus on the meteorological features with significant impacts on air quality in the Indian context. This research contributes to the development of accurate PM2.5 prediction models tailored specifically to the unique meteorological characteristics of India. The outcomes of this work have practical implications for policymakers, environmental agencies, and individuals in India, enabling proactive decision-making and mitigation strategies to combat the adverse effects of air pollution and safeguard public health.