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A SPATIAL-TEMPORAL MODEL FOR AIR QUALITY PREDICTION IN HONG KONG USING AN ATTENTION BASED ENCODER-DECODER AND CNN ARCHITECTURE WITH A CORRESPONDING VISUALIZATION EFFORT
MPhil Thesis Defence Title: "A SPATIAL-TEMPORAL MODEL FOR AIR QUALITY PREDICTION IN HONG KONG USING AN ATTENTION BASED ENCODER-DECODER AND CNN ARCHITECTURE WITH A CORRESPONDING VISUALIZATION EFFORT" By Mr. Yihong Fanise ZHOU Abstract This work presents a deep learning model on the prediction of air pollution for Hong Kong city, together with a corresponding visualization system. Rapid urbanization and its increasing density have induced a serious air pollution problem. Thus, air pollution prediction has become a necessity for helping people to protect their health. In this study, we introduce a spatial-temporal deep learning model based on attention mechanism assisted encoder-decoder and 1D CNN architecture to predict the concentration of five target pollutants (i.e., O3, NO2, SO2, PM2.5, and PM10) in the next 12 hours by using past 24 hours data from the air quality monitoring stations as the input. The temporal model implemented in this study was constructed by encoder-decoder architecture, which has the advantage to handle the time-series dataset. Long Short-Term Memory (LSTM), an advanced Recurrent Neural Network (RNN) network, is used in this deep learning model as the encoder and decoder stacked unit to extract the temporal relations; and the attention mechanism is used to deepen the correlation between the encoder and decoder for enhancing the prediction accuracy. The implemented spatial model was constructed by a 1D CNN network, which has the advantage to handle image-like dataset. This deep learning model is composed of a convolutional layer, a pooling layer and a fully connection layer. It can effectively extract the spacial features to infer the fine-grained air qualities based on the sparse dataset from the predictions of temporal model and the spatial information such as POI. The hyperparameters of the model were adjusted by gradient descent method in the training process. The index of agreement (IOA) was used as an accuracy indicator in this study to evaluate the model performance. The combined spatial-temporal model was implemented with the input of hourly air quality data from 16 air quality monitoring stations in Hong Kong. With the past 24 hours data, the model can provide the next 12 hours prediction for five target pollutants. The accuracy of the prediction for every pollutant at an arbitrary point on the grid-mesh with 1km interval is good. The highest IOA can achieve 0.98. A visualization system was also established to create an user friendly interface for both normal users and domain experts. The visualization system includes generating a 2D map for input parameters and output predictions, displaying the feature information with suitable circles and colors and providing the tools for comparison and analysis. Normal users can find the visualized information they are interested in; domain experts can use the visualized tools for identification and analysis. A visualized labeling system was also introduced for domain experts in the environmental area to perform error labeling in an easy way with a user friendly interface. The labeled data could be used selectively by the training process, which is very useful to refine the processing and improve the prediction function of the machine learning model. Date: Monday, 21 March 2022 Time: 3:00pm - 5:00pm Zoom Meeting: https://hkust.zoom.us/j/96255779657?pwd=R2lHUm9NeitRb3JPdmd1R3ErQzNUUT09 Committee Members: Prof. Huamin Qu (Supervisor) Prof. Cunsheng Ding (Chairperson) Prof. Ke Yi **** ALL are Welcome ****