Capability of an Elman Recurrent Neural Network for predicting the non-linear behavior of airborne pollutants

Barrero-González, David; Ramirez-Montanez, Julio Alberto; Aceves-Fernández, Marco Antonio; Ramos-Arreguín, Juan Manuel

doi:10.1007/s12145-021-00707-1

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Developing algorithms based on artificial intelligence has allowed us to interpret and classify large datasets, generating mathematical models of its behavior [ 7 , 8 , 9 , 10 , 11 ] and, at the same time, generating predictive models [ 12 , 13 , 14 , 15 ]. Artificial intelligence has two essential areas: machine learning and deep learning [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Modeling of Particulate Pollutants Using a Memory-Based Recurrent Neural Network Implemented on an FPGA

Ramírez-Montañez,

Rangel-Magdaleno,

Aceves-Fernández

et al. 2023

Micromachines

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The present work describes the training and subsequent implementation on an FPGA board of an LSTM neural network for the modeling and prediction of the exceedances of criteria pollutants such as nitrogen dioxide (NO2), carbon monoxide (CO), and particulate matter (PM10 and PM2.5). Understanding the behavior of pollutants and assessing air quality in specific geographical regions is crucial. Overexposure to these pollutants can cause harm to both natural ecosystems and living organisms, including humans. Therefore, it is essential to develop a solution that can accurately evaluate pollution levels. One potential approach is to implement a modified LSTM neural network on an FPGA board. This implementation obtained an 11% improvement compared to the original LSTM network, demonstrating that the proposed architecture is able to maintain its functionality despite reducing the number of neurons in its initial layers. It shows the feasibility of integrating a prediction network into a limited system such as an FPGA board, but easily coupled to a different system. Importantly, this implementation does not compromise the prediction accuracy for both 24 h and 72 h time frames, highlighting an opportunity for further enhancement and refinement.

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