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Using a deep convolutional neural network to predict 2017 ozone concentrations, 24 hours in advance.

08:00 EDT 28th September 2019 | BioPortfolio

Summary of "Using a deep convolutional neural network to predict 2017 ozone concentrations, 24 hours in advance."

In this study, we use a deep convolutional neural network (CNN) to develop a model that predicts ozone concentrations 24 h in advance. We have evaluated the model for 21 continuous ambient monitoring stations (CAMS) across Texas. The inputs for the CNN model consist of meteorology (e.g., wind field, temperature) and air pollution concentrations (NO and ozone) from the previous day. The model is trained for predicting next-day, 24-hour ozone concentrations. We acquired meteorological and air pollution data from 2014 to 2017 from the Texas Commission on Environmental Quality (TCEQ). For 19 of the 21 stations in the study, results show that the yearly index of agreement (IOA) is above 0.85, confirming the acceptable accuracy of the CNN model. The results also show the model performed well, even for stations with varying monthly trends of ozone concentrations (specifically CAMS-012, located in El-Paso, and CAMS-013, located in Fort Worth, both with IOA=0.89). In addition, to ensure that the model was robust, we tested it on stations where fewer meteorological variables are monitored. Although these stations have fewer input features, their performance is similar to that of other stations. However, despite its success at capturing daily trends, the model mostly underpredicts the daily maximum ozone, which provides a direction for future study and improvement. As this model predicts ozone concentrations 24 h in advance with greater accuracy and computationally fewer resources, it can serve as an early warning system for individuals susceptible to ozone and those engaging in outdoor activities.

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This article was published in the following journal.

Name: Neural networks : the official journal of the International Neural Network Society
ISSN: 1879-2782
Pages: 396-408

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Medical and Biotech [MESH] Definitions

A shift in the balance between production and destruction of STRATOSPHERIC OZONE that results in a decline of the amount of OZONE in the lower stratosphere.

Ozone in the Earth's stratosphere. It is produced continuously by the action of solar ULTRAVIOLET RAYS on oxygen in the stratosphere. The stratospheric ozone (especially at the ozone layer) blocks much of the solar UV radiation of wavelengths of 320 nanometers or less from being transmitted to lower ATMOSPHERE of the Earth.

An early embryonic developmental process of CHORDATES that is characterized by morphogenic movements of ECTODERM resulting in the formation of the NEURAL PLATE; the NEURAL CREST; and the NEURAL TUBE. Improper closure of the NEURAL GROOVE results in congenital NEURAL TUBE DEFECTS.

The two longitudinal ridges along the PRIMITIVE STREAK appearing near the end of GASTRULATION during development of nervous system (NEURULATION). The ridges are formed by folding of NEURAL PLATE. Between the ridges is a neural groove which deepens as the fold become elevated. When the folds meet at midline, the groove becomes a closed tube, the NEURAL TUBE.

A tube of ectodermal tissue in an embryo that will give rise to the CENTRAL NERVOUS SYSTEM, including the SPINAL CORD and the BRAIN. Lumen within the neural tube is called neural canal which gives rise to the central canal of the spinal cord and the ventricles of the brain. For malformation of the neural tube, see NEURAL TUBE DEFECTS.

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