Generative adversarial networks to model air pollution under uncertainty

Toutouh, Jamal; Nesmachnow, Sergio; Rossit, Diego Gabriel

doi:10.47350/aicts.2020.20

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…Accounts along this line expanded from approaches relying on simple imputation methods [15], [16], [17], to methods exploiting the advances of diverse deep learning architectures [18], [19]. New lines of research using Generative Adversarial Networks (GANs) are being acknowledged for their potential to learn data in an unsupervised manner [20], [21]. Our work falls under this category, namely we introduce two novel adversarial architectures that generate realistic samples subjected to a conditional input, which have led to superior performance results.…”

Section: Introductionmentioning

confidence: 99%

A Complete Air Pollution Monitoring and Prediction Framework

et al. 2023

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Forecasting air pollution is crucial for understanding the phenomenological and contextual variety of mechanisms underlying pollution in a particular area or region. Analyzing high-dimensional data with spatial and temporal dependencies is pointed out as a major challenge for traditional machine learning approaches for air pollution forecasting. The unprecedented advances in deep learning employed on massive quantities of IoT sensor data raise high hopes for the future of air pollution forecasting. Drawing on past and current experiences and solutions advanced in a growing body of research on the topic, we proposed and evaluated four encoder-decoder architectures with attention for forecasting particulate matter (PM) levels, with a general applicability that is both location-and season-independent. The attention mechanism was employed to better learn representation of time series data. By selectively focusing the attention on elements that contribute the most, superior performance gains have been obtained by the proposed models. Sensor measurements obtained in the past 7 or 14 days as well a set of external factors known to affect the pollutant levels, such as weather conditions, location and time frame (e.g., season, time of day) were taken into account. In this research, we carried out a case study on PM 2.5 forecasting for the city of Skopje, allowing us to discuss the relevance of the results obtained by the proposed solutions. The ability to draw valid inferences from data has critical importance when forecasting air pollution. In this respect, any air pollution forecasting model based on deep learning would require a component for generation of realistic data samples to augment the time series dataset. We address the challenges in missing data by proposing and evaluating two adversarial networks for data augmentation. We have conducted a number of experiments to investigate the performance of the predictive models with and without augmenting the training datasets and obtain superior performance gains by the proposed adversarial models for data augmentation. The deep neural architectures advanced in this research are general enough to be used for predictive and generative tasks for other pollutants; moreover, they could be adopted for related tasks handling time series data in other domains.

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Section: Introductionmentioning

confidence: 99%

A Complete Air Pollution Monitoring and Prediction Framework

et al. 2023

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“…GANs have demonstrated being a successful tool for many applications that require the creation of synthesized data, especially those concerning multimedia data (e.g., images, sound, and video), healthcare, and other areas [2]- [5].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary latent space search for driving human portrait generation

Machín

Nesmachnow

Toutouh

2021

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI)

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This article presents an evolutionary approach for synthetic human portraits generation based on the latent space exploration of a generative adversarial network. The idea is to produce different human face images very similar to a given target portrait. The approach applies StyleGAN2 for portrait generation and FaceNet for face similarity evaluation. The evolutionary search is based on exploring the real-coded latent space of StyleGAN2. The main results over both synthetic and real images indicate that the proposed approach generates accurate and diverse solutions, which represent realistic human portraits. The proposed research can contribute to improving the security of face recognition systems.

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