Air pollutant forecasting using deep learning

Abstract

In nearly every country, air pollution has become a serious issue. whether it is developing countries or developed countries, as urbanization and industrialization hasincreased. Governments and citizens are greatly concerned about air pollution, which has a negative impact on human health, the well-being of all life forms, and global economic development. Numerical data is used in traditional air quality forecast systems, which necessitates more computing resources for pollutant concentration measurement and yields poor results. We used a commonly used deep learning model to solve this problem. Particulate Matter 10 was the pollutant studied in this study (PM10). This study examines the methods and techniques for predicting air quality using Deep Learning. Various deep learning models have been investigated. This research incorporates a recurrent neural network (RNN), a long short-term memory (LSTM), a gated recurrent unit (GRU) and a bidirectional long short-term memory combination for forecasting. The dataset is primarily comprised of pollution and meteorological time series data from AirNet China and the United States Environmental Protection Agency. We studied various architectures and their variations in topologies and model parameters in order to decide the best architecture. The Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) were used to assess the models (MAPE). Each experiment was run for up to 1000 epochs by varying the learning rate, the number of nodes in a layer, and the total number of hidden layers. All models performed admirably in terms of prediction, according to the results. For AirNet dataset GRU based architecture produced best outcome while for EPA dataset LSTM based architecture outperformed other models.