Deep-learning architecture for PM2.5 concentration prediction: A review

Environmental Science and Ecotechnology, Год журнала: 2024, Номер 21, С. 100400 - 100400

Опубликована: Фев. 17, 2024

Accurately predicting the concentration of fine particulate matter (PM

Язык: Английский

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A city-based PM2.5 forecasting framework using Spatially Attentive Cluster-based Graph Neural Network model

Journal of Cleaner Production, Год журнала: 2023, Номер 405, С. 137036 - 137036

Опубликована: Март 30, 2023

Язык: Английский

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Hourly PM2.5 concentration prediction for dry bulk port clusters considering spatiotemporal correlation: A novel deep learning blending ensemble model

Journal of Environmental Management, Год журнала: 2024, Номер 370, С. 122703 - 122703

Опубликована: Окт. 1, 2024

Язык: Английский

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Distance adaptive graph convolutional gated network-based smart air quality monitoring and health risk prediction in sensor-devoid urban areas

Sustainable Cities and Society, Год журнала: 2023, Номер 91, С. 104445 - 104445

Опубликована: Фев. 11, 2023

Язык: Английский

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Research and application of a novel selective stacking ensemble model based on error compensation and parameter optimization for AQI prediction

Environmental Research, Год журнала: 2024, Номер 247, С. 118176 - 118176

Опубликована: Янв. 11, 2024

Язык: Английский

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A hybrid PM2.5 interval concentration prediction framework based on multi-factor interval decomposition reconstruction strategy and attention mechanism

Atmospheric Environment, Год журнала: 2024, Номер 335, С. 120730 - 120730

Опубликована: Авг. 7, 2024

Язык: Английский

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A Hybrid Deep Learning Model for Air Quality Prediction Based on the Time–Frequency Domain Relationship

Atmosphere, Год журнала: 2023, Номер 14(2), С. 405 - 405

Опубликована: Фев. 20, 2023

Deep learning models have been widely used in time-series numerical prediction of atmospheric environmental quality. The fundamental feature this application is to discover the correlation between influencing factors and target parameters through a deep network structure. These relationships original data are affected by several different frequency factors. If adopted without guidance, these correlations may be masked entangled multifrequency data, which will cause problem insufficient extraction difficult model interpretation. Because wavelet transform has ability separate can extracted methods, hybrid combining transformer-like (WTformer) was designed extract time–frequency domain features air 2018–2021 hourly Guilin as benchmark training dataset. Pollutants meteorological variables local dataset decomposed into five bands wavelet. analysis WTformer showed that particulate matter (PM2.5 PM10) had an obvious low-frequency band low high-frequency band. PM2.5 temperature negative positive wind speed results laws could found model, made it possible explain model. experimental show performance established better than multilayer perceptron (MLP), one-dimensional convolutional neural (1D-CNN), gate recurrent unit (GRU), long short-term memory (LSTM) Transformer, all time steps (1, 4, 8, 24 48 h).

Язык: Английский

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Performance Assessment of Sequential Models for Solar Radiation Forecasting Over Varying Forecast Horizon

Lecture notes in networks and systems, Год журнала: 2025, Номер unknown, С. 49 - 59

Опубликована: Янв. 1, 2025

Язык: Английский

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Adaptive graph-generating jump network for air quality prediction based on improved graph convolutional network

Atmospheric Pollution Research, Год журнала: 2025, Номер unknown, С. 102488 - 102488

Опубликована: Март 1, 2025

Язык: Английский

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An Efficient Modern Convolution-Based Dynamic Spatiotemporal Deep Learning Architecture for Ozone Prediction

Environmental Modelling & Software, Год журнала: 2025, Номер unknown, С. 106424 - 106424

Опубликована: Март 1, 2025

Язык: Английский

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