Air Pollution Prediction with Multi-Modal Data and Deep Neural Networks

Abstract

Air pollution is becoming a rising and serious environmental problem, especially in urban areas affected by an increasing migration rate. The large availability of sensor data enables the adoption of analytical tools to provide decision support capabilities. Employing sensors facilitates air pollution monitoring, but the lack of predictive capability limits such systems’ potential in practical scenarios. On the other hand, forecasting methods offer the opportunity to predict the future pollution in specific areas, potentially suggesting useful preventive measures. To date, many works tackled the problem of air pollution forecasting, most of which are based on sequence models. These models are trained with raw pollution data and are subsequently utilized to make predictions. This paper proposes a novel approach evaluating four different architectures that utilize camera images to estimate the air pollution in those areas. These images are further enhanced with weather data to boost the classification accuracy. The proposed approach exploits generative adversarial networks combined with data augmentation techniques to mitigate the class imbalance problem. The experiments show that the proposed method achieves robust accuracy of up to 0.88, which is comparable to sequence models and conventional models that utilize air pollution data. This is a remarkable result considering that the historic air pollution data is directly related to the output—future air pollution data, whereas the proposed architecture uses camera images to recognize the air pollution—which is an inherently much more difficult problem.

A Dual-Model Architecture with Grouping-Attention-Fusion for Remote Sensing Scene Classification

“…Benefiting from the development of high-performance computers and the availability of large-scale training data [36,37], deep learning-based approaches [38][39][40][41] have attracted more and more attention. Among the typical deep architectures, CNN provides a strong ability of feature extraction and yield significant performance improvement on scene classification.…”

“…Zhao et al [51] developed a multitask learning framework that improved the discrimination ability of the model features by taking advantage of different tasks. Kalajdjieski et al [41] applied a series of deep CNNs together with other sensor data for the classification of air pollution.…”

Multi-Horizon Air Pollution Forecasting with Deep Neural Networks

“…The same approach can be used in cities and other areas. Taking preventive measures could decrease the influence on the air quality upfront and prevent or minimize the citizens' exposure to air with hazardous pollution [15,16].…”

Unsupervised Adversarial Domain Adaptation with Error-Correcting Boundaries and Feature Adaption Metric for Remote-Sensing Scene Classification

“…In the early stage of development, traditional machine learning methods have been used for scene classification tasks, such as support vector machine and bag of words [2,3]. Recently, deep learning methods have been proven to be effective for extracting image features [4][5][6][7][8], and many studies have demonstrated effective scene classification performance with the help of deep learning from various novel perspectives including self-supervised learning [9], data augmentation [10], feature fusion [11][12][13][14][15], reconstructing networks [16][17][18][19][20][21][22][23], integration of spectral and spatial information [24], balancing global and local features, refining feature maps through encoding method [25], adding a new mechanism [26,27], as well as introducing a new network [28], open set problem [29], and noisy label distillation [30]. However, a lack of annotated data has restricted the development of deep learning methods in scene classification due to the high cost of annotating data.…”