Air pollution and population morbidity forecasting with artificial neural networks

Gornov, Alexander; Zarodnyuk, Tatiana; Ефимова, Н. В.

doi:10.1088/1755-1315/211/1/012053

Cited by 2 publications

(1 citation statement)

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“… x is a matrix of features;  y is the response vector;  test_size: this is the ratio of the test data to the specified data. For example, if with 100 records in the dataset, test_size = 0.1, then the test sample will contain 10% of all records, and the training sample will contain 90%, respectively [16]. In this paper, the technique takes the following values: x_trail, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1)…”

Section: Resultsmentioning

confidence: 99%

Ergonomic system of forecasting and analysis of the probability of atmospheric air pollution by emissions of enterprises in the conditions of technogenic development of the world

Kuzmenko,

Filippov,

Filippova

et al. 2024

E3S Web Conf.

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The article is devoted to the problem of air pollution and its impact on human health and the environment. The paper considers methods of air pollution analysis based on the use of neural networks, taking into account the variety of data from the Internet. The authors emphasize the different effects of pollutants depending on the type, duration and level of exposure, as well as other factors, including individual risks to human health and the combined effects of different pollutants and stress factors. Special attention is paid to the two most common types of air pollution - smog and soot. The uneven distribution of the negative effects of air pollution, which are most often felt in low-income and colored communities, as well as the Air quality Index (AQI) developed by the Environmental Protection Agency, which informs the population about the current state of atmospheric air and its impact on human health, are considered separately. As a result of the work, the criteria for analyzing air quality, including pollution parameters and weather conditions, are presented, and the structure of future data is developed.

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Section: Resultsmentioning

confidence: 99%