PlumeNet: Large-Scale Air Quality Forecasting Using A Convolutional LSTM Network

Alléon, Antoine; Jauvion, Grégoire; Quennehen, Boris; Lissmyr, David

doi:10.48550/arxiv.2006.09204

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…On the other hand, sophisticated spatial models are introduced to better incorporate spatial dependency between different monitoring sensors or cities. Beyond carefully designed handcrafted spatial features [1], [19], deep spatial features given by embedded spatial modules like spatial convolutions [15], [20], [21], graph convolution networks [7], [8], [22], [23], [24], or transformers [25] have shown better performance. There also exists a branch of work that tries to develop a data-physical hybrid model to make better use of prior knowledge and improve performance [26], [27].…”

Section: A Air Pollution Predictionmentioning

confidence: 99%

Learning Adaptive Probabilistic Models for Uncertainty-Aware Air Pollution Prediction

Liu

et al. 2023

IEEE Access

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The air pollution problem has been a serious issue for public health and city development in recent years, which rises an urgent demand for accurate air pollution prediction models. Traditional time series prediction scheme has been challenged in such case because the air pollution signals can be highly dynamic and uncertain. Distinct latent physical processes and complex environment changes over time make it hard for one fixed model to give consistently good performance. Also probabilistic prediction and uncertainty-aware estimation are critical when the model is used for public decisions in practice. However, few previous works can simultaneously provide adaptive and probabilistic air pollution prediction abilities, which are important practical issues for the real-world implementation of data-driven air pollution prediction models. In this paper we propose an adversarial meta learning approach to address these concerns. After reformulating the target signals as a collection of independent but similar mini-tasks along time, we adversarially learn a meta model that includes an implicit generator to adaptively give more informative probabilistic predictions across tasks. We further provide a bayesian meta learning interpretation that recasts the proposed model as an approximate minimizer for the Wasserstein distance between a generative latent model and the true data distribution. Our model provides an algorithm that explicitly models how the prediction distribution is conditioned on underlying data patterns and simultaneously gives adaptive uncertainty estimation. Experiments on both synthetic and real-world air pollution datasets show that the proposed model can simultaneously provide better probabilistic and adaptive prediction while keeping stratifying point prediction error.INDEX TERMS Air pollution prediction, meta learning, probabilistic forecast, adversarial networks.

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Section: A Air Pollution Predictionmentioning

confidence: 99%

Learning Adaptive Probabilistic Models for Uncertainty-Aware Air Pollution Prediction

Liu

et al. 2023

IEEE Access

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“…Many different architectures have been proposed using ConvL-STM building blocks, applied to different problems, such as weather prediction from satelite image sequences [21], air quality forecasting [22], and video frame prediction [23]. In the architecture that served as the basis for this work [24] the authors used a standard encoder-decoder structure comprised of multiple stacked ConvL-STM layers, with each decoder layer initializing its hidden states from the output of the corresponding encoder layer.…”

Section: Spatio-temporal Modeling Using Neural Networkmentioning

confidence: 99%

Modeling the geospatial evolution of COVID-19 using spatio-temporal convolutional sequence-to-sequence neural networks

Cardoso¹,

Cavalheiro²,

Borges³

et al. 2021

Preprint

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Europe was hit hard by the COVID-19 pandemic and Portugal was one of the most affected countries, having suffered three waves in the first twelve months. Approximately between Jan 19th and Feb 5th 2021 Portugal was the country in the world with the largest incidence rate, with 14-days incidence rates per 100,000 inhabitants in excess of 1000. Despite its importance, accurate prediction of the geospatial evolution of COVID-19 remains a challenge, since existing analytical methods fail to capture the complex dynamics that result from both the contagion within a region and the spreading of the infection from infected neighboring regions.We use a previously developed methodology and official municipality level data from the Portuguese Directorate-General for Health (DGS), relative to the first twelve months of the pandemic, to compute an estimate of the incidence rate in each location of mainland Portugal. The resulting sequence of incidence rate maps was then used as a gold standard to test the effectiveness of different approaches in the prediction of the spatial-temporal evolution of the incidence rate. Four different methods were tested: a simple cell level autoregressive moving average (ARMA) model, a cell level vector autoregressive (VAR) model, a municipality-by-municipality compartmental SIRD model followed by direct block sequential simulation and a convolutional sequence-to-sequence neural network model based on the STConvS2S architecture. We conclude that the convolutional sequence-to-sequence neural network is the best performing method, when predicting the medium-term future incidence rate, using the available information.

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PlumeNet: Large-Scale Air Quality Forecasting Using A Convolutional LSTM Network

Cited by 2 publications

References 12 publications

Learning Adaptive Probabilistic Models for Uncertainty-Aware Air Pollution Prediction

Learning Adaptive Probabilistic Models for Uncertainty-Aware Air Pollution Prediction

Modeling the geospatial evolution of COVID-19 using spatio-temporal convolutional sequence-to-sequence neural networks

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