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Yıl 2021, Cilt: 8 Sayı: 2, 123 - 131, 30.06.2021

Yıldıran Yılmaz Selim Buyrukoğlu

https://doi.org/10.17350/HJSE19030000222
Cited By: 6

Öz

Kaynakça

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Hybrid Machine Learning Model Coupled with School Closure For Forecasting COVID-19 Cases in the Most Affected Countries

Yıl 2021, Cilt: 8 Sayı: 2, 123 - 131, 30.06.2021

Yıldıran Yılmaz Selim Buyrukoğlu

https://doi.org/10.17350/HJSE19030000222
Cited By: 6

Öz

Coronavirus disease (Covid-19) caused millions of confirmed cases and thousands of deaths worldwide since first appeared in China. Forecasting methods are essential to take precautions early and control the spread of this rapidly expanding pandemic. Therefore, in this research, a new customized hybrid model consisting of Back Propagation-Based Artificial Neural Network (BP-ANN), Correlated Additive Model (CAM) and Auto-Regressive Integrated Moving Average (ARIMA) models were developed to forecast of Covid-19 prevalence in Brazil, US, Russia and India. Covid-19 dataset is obtained from World Health Organization website from 22 January, 2020 to 6 January, 2021. Various parameters were tested to select the best ARIMA models for these countries based on the lowest MAPE values (5.21, 11.42, 1.45, 2.72) for Brazil, US, Russia and India, respectively. On the other hand, the proposed BP-ANN model itself provided less satisfactory MAPE values. Finally, the developed new customized hybrid model was achieved to obtain the best MAPE results (4.69, 6.4, 0.63, 2.25) for forecasting of Covid-19 prevalence in Brazil, US, Russia and India, respectively. Those results emphasize the validity of our hybrid model. Besides, the proposed prediction models can assist countries in terms of taking important precautions to control the spread of Covid-19 in the world.

Anahtar Kelimeler

Covid-19 Neural Network ARIMA Time Series Correlated Additive Model

Kaynakça

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Toplam 66 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Research Article
Yazarlar

Yıldıran Yılmaz 0000-0002-5337-6090

Selim Buyrukoğlu 0000-0001-7844-3168

Yayımlanma Tarihi 30 Haziran 2021
Gönderilme Tarihi 15 Ocak 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 8 Sayı: 2

Kaynak Göster

Vancouver Yılmaz Y, Buyrukoğlu S. Hybrid Machine Learning Model Coupled with School Closure For Forecasting COVID-19 Cases in the Most Affected Countries. Hittite J Sci Eng. 2021;8(2):123-31.

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