… combined CNN and LSTM to predict PM2.5 concentration. The rationale for this is as follows.
(1) CNN … Given this advantage, we used the CNN to extract spatial features of inputs among …

… (CNN) and long-short-term memory (LSTM) with a space-shared mechanism, named
space-shared CNN-LSTM (SCNN-LSTM… The proposed SCNN-LSTM contains multi-channel …

… the CNN-LSTM model, its feasibility and practicability to forecast the PM 2.5 concentration
… network model that integrates the CNN and LSTM architectures, and through historical data …

… Given that PM 2.5 data is one-dimension data, 1D CNN was utilized for … CNN-LSTM model
based on spatiotemporal correlation is proposed to predict the daily PM 2.5 concentration in …

… [12] was applied in the AC-LSTM model, used to capture the … at different times on PM 2.5
concentration in this paper. … CNN–LSTM model to predict urban PM 2.5 concentrations over …

… aware attentional LSTM neural network (FAALSTM) model to predict PM 2.5 [30]. Pak et
al. used the CNN and LSTM models to predict the daily average concentration of PM 2.5 in …

… In this paper, a novel PM 2.5 concentration prediction model, CBAM-CNN-Bi LSTM, is
constructed by deep learning techniques based on the principles related to spatial big data. This …

… analyze the PM 2.5 concentration of stations … (CNN-LSTM) model. To solve the complexity
and nonlinear characteristics of PM 2.5 time series data problems, we adopted the CNN-LSTM …

… 2.5 concentrations using RNN, LSTM, CNN-LSTM, and HDAQP models is designed. The
influence of meteorological factors on the PM 2.5 concentrations … of PM 2.5 concentrations can …

… In this study, the PM 2.5 concentration forecasting models based on the CNN, LSTM and
CNN-LSTM were established respectively. Since it can effectively extract the spatial …