Hybrid CNN-LSTM Model for Air Quality Prediction: A Case Study for Gurugram

Abstract

One of the most important environmental problems brought about by rapid population growth and industrialization is air pollution. Today, air pollution is generally caused by heating, industry and motor vehicles. In addition, factors such as unplanned urbanization, topographic structure of cities, atmospheric conditions and meteorological parameters, building and population density also cause pollution to increase. Pollutants with concentrations above limit values have negative effects on humans and the environment. In order to prevent people from being negatively affected by these pollutants, it is necessary to know the pollution level and take action as soon as possible. In this study, a hybrid ConvLSTM model was developed in order to quickly and effectively predict air pollution, which has such negative effects on humans and the environment. ConvLSTM was compared with LR, RF, SVM, MLP, CNN and LSTM using approximately 4 years of air quality index data from the city of Gurugram in India. Experimental results showed that ConvLSTM was significantly more successful than the base models, with 30.645 MAE and 0.891 R2.

Keywords

• Air quality
• Deep learning
• Machine learning
• CNN
• LSTM

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