A comparative study of the feed forward back propagation (FFBP) and layer recurrent (LR) neural network model for forecasting ground level ozone concentration

Gorai, Amit Kumar; Mitra, Gargi

doi:10.1007/s11869-016-0417-0

Cited by 16 publications

(1 citation statement)

References 36 publications

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“…However, owing to limitations in site layouts, efficient ozone concentration monitoring can be challenging. Traditional statistical methods, such as the autoregressive integrated moving average model and Kalman filter method, can predict atmospheric composition but are mostly limited to linear systems [6][7][8] . With the development of artificial intelligence technology, machine learning has been widely used in image processing, path planning, natural language processing [9][10] .…”

Section: Introductionmentioning

confidence: 99%

Attention-based CNN–LSTM deep learning hybrid model for the prediction of atmospheric ozone concentration

JIANG,

HUA,

WANG

et al. 2024

Preprint

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Considering that ozone is essential to understanding air quality and climate change, this study introduces a deep learning method for predicting atmospheric ozone concentrations. The method combines an attention mechanism with a convolutional neural network (CNN) and long short-term memory (LSTM) to address the nonlinear nature of multivariate time-series data. It employs CNN and LSTM to extract features from short series, enhanced by the attention mechanism for improved short-term prediction accuracy. The model uses eight meteorological and environmental parameters from 16,806 records (2018–2019) as input, selected through principal component analysis (PCA). It features a hybrid attention-CNN-LSTM model with specific settings: a time step of 5, a batch size of 25, 15 units in the LSTM layer, the Relu activation function, 25 epoch iterations, and an overfitting avoidance strategy at 0.15. Experimental results demonstrate that this hybrid model outperforms independent models and the CNN-LSTM model, especially in forward prediction with a multi-hour time lag. The model exhibits a high prediction determination coefficient (R2 = 0.971) and a root mean square error of 3.59 for a 1-hour time lag. It also shows consistent accuracy across different seasons, highlighting its robustness and superior time-series prediction capabilities for ozone concentration.

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