Exploring Deep Learning Techniques for Data-driven Air Quality Modeling and Forecasting

Abstract

Air pollution has become a significant issue, especially in high-density urban areas. Since the first industrial revolution in the 19th century, human activities have badly affected the earth and the environment. The world has become inhabitable day by day due to inappropriate human activities, construction patterns, and unsustainable cities development. Moreover, due to these activities, environmental air quality has become worst day by day. World Health Organization (WHO) provided evidence that urban air pollution is becoming the main threat to human health, especially in high-density countries like China and India. Changes in air pollution are basically affected by low-frequency and high-frequency pollution in other places and their weather conditions. In that situation, changes in meteorological parameters and spatial attributes should be considered and identify the correlation between them. People are paying more attention to changes in air quality and its control. Air pollution forecasting is one of the essential preventive steps to control air pollution. Based on the pollutant forecasting results, the relevant department and policymakers get the early information on pollutant concentration in a particular location. This information can help them adjust some critical measures according to pollutant emission sources to control pollutant emissions and their adverse impact on public health. However, pollution forecasting has become challenging due to its complexities with time-space nonlinearities, weather conditions, and spatial-temporal impact of nearby locations. In order to address these issues, this research work proposes various approaches, which are summarized as follows. In the first contribution, this research work presents a neural network based Convolutional Long Short term Memory-Sparse Denoising Autoencoder (CLS) model to forecast the PM2.5 level under meteorological conditions. The CLS model identifies the vast dataset’s hidden features, performs pollutants’ temporal modeling, and reconstructs the predicted output in the dynamic fine-tuning layer to get robust prediction results in a real time environment. The proposed model has experimented with different datasets, and its results show the model’s efficiency in air quality modeling. In the second contribution, this research work developed Temporal Convolutional Denoising Autoencoder (TCDA) network, a hybrid PM2.5 prediction framework that can perform rapid extraction of complex dataset’s features, handle missing values and improve PM2.5 prediction results. The model can reconstruct the corrupted, missing values and handle the different patterns of missing values to enhance the short-term PM2.5 forecasting results. In the third contribution, this research developed a Multi-Directional Temporal Convolutional Artificial Neural Network (MTCAN) model to impute and forecast PM2.5 pollutant concentration in a single training process. The main idea of the multi-directional properties of MTCAN is to maintain the temporal correlation within the features’ measurement and meteorological and pollutant variables to impute PM2.5 missing values. The MTCAN model performs feature learning and sequential modeling simultaneously with a wide range of past observations for long-term forecasting, minimizing memory size requirement and training cost. In the fourth contribution, this research work presents a newly developed multi-step ahead pollution forecasting model with a multi-input, multi-output learning process. The proposed model can work effectively under meteorological conditions and spatial impact. The proposed model has better long-term forecasting accuracy as compared to the traditional statistical and machine learning models. The proposed Multi-Output Long Short-Term Memory (LSTM) Autoencoder (M-LSTMA) accumulates each step prediction value to perform multi-step ahead forecasting for multiple pollutants in a single training process. The results show the model’s effectiveness, where we need to know the overall air pollution level for a particular area. In the fifth contribution, this research work proposed a novel PM2.5 forecasting model named as Multi-Output Temporal Convolutional Network Autoencoder (MO TCNA), which serves both the PM2.5 and PM10 pollutants forecasting for various locations instead of performing single output and site-specific pollutant forecasting for an overall idea of pollution level for a particular region. The proposed work developed a Recursive-Multi-Input, Multi Output (R-MIMO) strategy to improve multiple pollutant forecasting accuracy for the long-term period for different sites in a region. Experimental results indicate that the proposed models are superior to baseline single-output and multi-output forecasting models, which proves their effectiveness in regional air quality modeling. The efficiency of all the proposed models has experimented with two datasets for evaluation, and the comparative results illustrate the efficiency of all the models for an effective environmental decision support system.