Deep particulate matter forecasting model using correntropy-induced loss

Kim, Jongsu; Lee, Chang‐Hoon

doi:10.1007/s12206-021-0817-4

Cited by 2 publications

(1 citation statement)

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“…Compared with traditional statistical models, the proposed machine learning methods, such as deep learning, gradient boosting, random Forest, and ensemble methods, improved the accuracy of predicting daily concentrations of PM 2.5 . However, statistical models have limitations and are less informative when dealing with non-linearity and data change over time compared to PM 2.5 models based on artificial intelligence [ 14 ]. Machine-learning-based methods used in PM 2.5 forecasting have shown different results in different geographical locations, such as Shanghai, Beijing, Tehran, and Ho Chi Minh City, due to temporal and spatial characteristics and weather conditions.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the Negative Impact of Urban Air Pollution on Population Health Using Machine Learning Method

Temirbekov,

Temirbekova,

Tamabay

et al. 2023

IJERPH

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This study focuses on assessing the level of morbidity among the population of Almaty, Kazakhstan, and investigating its connection with atmospheric air pollution using machine learning algorithms. The use of these algorithms is aimed at analyzing the relationship between air pollution levels and the state of public health, as well as the correlations between COVID-19 infection and the development of respiratory diseases. This study analyzes the respiratory diseases of the population of Almaty and the level of air pollution as a result of suspended particles for the period of 2017–2022. The study includes recommendations to reduce harmful emissions into the atmosphere using machine learning methods. The results of the study show that air pollution is a critical factor affecting the increase in the number of diseases of the respiratory system. The study recommends taking measures to reduce air pollution and improve air quality in order to prevent the development of chronic respiratory diseases. The study offers recommendations to industrial enterprises, traffic management organizations, thermal power plants, the Department of Environmental Protection, and local executive bodies in order to reduce respiratory diseases among the population.

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Section: Introductionmentioning

confidence: 99%