AIR QUALITY PREDICTION IN HANOI USING A DEEP LEARNING APPROACH

Air pollution is becoming a serious global crisis, threatening human health, disrupting the balance of the environment, negatively affecting ecosystems, and contributing to climate change. Accurate long-term air quality prediction plays a key role in building early warning systems to mitigate these negative impacts. Efforts to forecast air quality through the combination of knowledge from environmental science, statistics, and computer science have attracted much attention. Among them, deep learning and advanced machine learning have demonstrated an outstanding ability to detect complex non-linear patterns from environmental data. However, the application of deep learning to air quality prediction is still quite new. This paper proposes a deep-learning model using the LSTM (Long Short-Term Memory) network to predict air quality in Hanoi. The research results demonstrate that the proposed model is capable of predicting the air quality index with high accuracy, close to actual values ​​from monitoring data.