Graph convolutional network-Long short term memory neural network- multi layer perceptron- Gaussian progress regression model: A new deep learning model for predicting ozone concertation

Ozone is one of the most important air pollutants. The high ozone concertation (OZC) affects the environment and public health. Since OZC depends on the number of different variables, it is difficult and complex to predict ozone concertation. A multilayer perceptron model is robust for predicting ozone concertation, although it has some limitations. The multi-layer perceptron (MLP) model may be unable to extract spatial and temporal features from time series data. Additionally, modelers need to developed models for handling unstructured and structured data. The purpose of this study is to combine deep learning models with MLP models to extract spatial and temporal features and improve the accuracy of predictions. We coupled deep learning models with the MLP model to overcome the limitations of the MLP model. We propose two new hybrid deep models for predicting ozone concertation. Graph convolutional network (GCN)-Long short term memory neural network (LSTM)-multi layer perceptron model (MLP)-Gaussian progress models (GPR) and convolutional neural network (CNN)-LSTM-MLP-GPR are introduced to predict hourly ozone concentration. Novelties of the papers include the development of a new model for spatial feature extraction, the introduction of a new model for temporal feature extraction, and the quantification of uncertainty. Spatial features can be extracted using CNN and GCN models. The LSTM model is a robust model for extracting temporal features. The GPR model quantifies uncertainty values. We use the models to predict ozone concentrations in three stations of Malaysia and one basin in Iran (Sefidrood basin). For the first case study (Malaysian stations), air pollution data and meteorological data were used. GCN-LSTM-MLP-GPR decreased the training MAE of CNN-LSTM-MLP-GPR, CNN-MLP-GPR, GNC-LSTM, CNN-LSTM, GCN-MLP, CNN-MLP, GCN-GPR, CNN-GPR, LSTM-GPR, MLP-GPR, GCNN, CNN, LSTM, MLP, and GPR by 1.7%, 9.2%, 14%, 15%, 18%, 21%, 22%, 27%, 30%, 32%, 34%, 35%, 37%, 42%, and 50% respectively at station 1. At station 2, the PBIAS of the CNN-LSTM-MLP-GPR and GCN-LSTM-LSTM-GPR was 2 and 4%, respectively. The testing MAE of the GCN-LSTM, CNN-LSTM, GCN-MLP, and CNN-MLP was 0.130, 0.131, 0.133, and 0.141 at station 3. In the Sefidrrod basin, the GCN-LSTM-MLP-GPR decreased the MAE of the GCN, CNN, LSTM, MLP, and GPR models by 53%, 55%, 56%, 57%, and 59%, respectively. The uncertainty analysis indicated that the un-certainty of the GCN-LSTM-MLP-GPR model was lower than that of the CNN-LSTM-MLP-GPR, CNN-MLP-GPR, GNC-LSTM, CNN-LSTM, GCN-MLP, CNN-MLP, GCN-GPR, CNN-GPR, LSTM-GPR, MLP-GPR, GCNN, CNN, LSTM, MLP, and GPR model. The GCN-LSTM-MLP-GPR model was a reliable tool for extracting data and predicting ozone concentration. The CGN-LSTM-MLP-GPR is a robust model for spatial and temporal predictions. It can be used as an early warning system to monitor air quality.