Attention Based-ConvLSTM-DNN Networks for Fine Dust Concentration Prediction

Abstract

Air pollution, particularly fine dust, poses a significant threat to public health and necessitates accurate prediction models for effective mitigation strategies. In this paper, we propose a so-called attention-based ConvLSTM-DNN networks for fine dust concentration prediction. Our proposed model integrates the feature extraction capabilities of a 2D Convolutional Neural Network (CNN) with the long-term memory retention of an LSTM, capturing spatial and temporal dependencies in the input data. We apply an attention mechanism to enhance the model