Abstract
Growing metropolitan areas bring rapid urbanization and air pollution problems. As diseases and mortality rates increase because of the air pollution problem, it becomes a necessity to estimate the air pollution density and inform the public to protect the health. Air pollution problem displays contextual characteristics such as meteorological conditions, industrial and technological developments, traffic problem etc. that change from country to country and also from city to city. In this study, we determined PM{}_{10} as the target pollutant and designed a new deep learning based air quality forecasting model, namely DFS (Deep Flexible Sequential). Our study uses real world hourly data from Istanbul, Turkey between 2014 and 2018 to forecast the air pollution 4, 12, and 24 hours before. DFS model is a hybrid & flexible deep model including Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN). The proposed model also is capable of generalization with standard and flexible Dropout layers. Through flexible Dropout layer, the model also obtains flexibility to adapt changing window sizes in sequential modelling. Moreover, this model can be applied to other air pollution time series data problems with small modifications on parameters by taking into account the nature of the data set.
Highlights
Air pollution plays an important role in living conditions in most large cities of the world
Considering the threats posed by human health, in this study we focus on estimating PM10 density
This study aims to forecast PM10 density four, twelve and twenty-four hours before it occurs and offers a novel deep learning based forecasting approach, entitled Deep Flexible Sequential (DFS) model
Summary
Air pollution plays an important role in living conditions in most large cities of the world. There are standard approaches in order to identify specific pollutant mixtures that may include hundreds of gas compounds and particulates of complex physic-chemical compounds. These mixtures which are combinations of different pollutants in varying percentages, depend on social, economic, and technological activities at a given area. Estimation of alterations at air pollution concentration is required to secure life quality at city centers. In this respect, air quality estimation models have been developed in order to forecast air pollution before air quality declines significantly at the regional or local level. The characteristics of atmospheric pollution and their negative effects on life quality are taken into account[5,6]
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