Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций
Конференция: Computer Science Online Conference, CSOC 2021
Год издания: 2021
Идентификатор DOI: 10.1007/978-3-030-77448-6_17
Ключевые слова: air pollution, carbon monoxide, krasnoyarsk, lstm, nitrogen dioxide, prediction model, sulfur dioxide, time series forecasting
Аннотация: The national weather agency says Krasnoyarsk had the dirtiest air of any Russian city in 2018, beating out Mumbai and Guangzhou. Krasnoyarsk has air so bad that the authorities regularly warn people to remain inside, avoid walking and sporting outdoors. During periodic “black sky” events, Krasnoyarsk’s 1 million residents suffer frПоказать полностьюom toxic levels of smog in winter, when coal-powered emissions peak, and in late summer smoke from wildfires spread. An issue that is magnified due to a lack of effective air pollution prediction techniques, this study has used the raw numerical data of key pollutants to predict their future status through LSTM (Long Short-Term Memory) modeling. Machine learning methods trained on raw numerical time series data exhibit fundamental limitations such as a high sensitivity to the hyper parameters and even to the initialization of random weights. The article shows potential of deep learning models with high accuracy using the time series data of air pollutants and meteorological parameters #CSOC1120. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Журнал: Lecture Notes in Networks and Systems
Выпуск журнала: Vol. 228
Номера страниц: 191-198
ISSN журнала: 23673370
Издатель: Springer Science and Business Media Deutschland GmbH