Multi-Regional Modeling of Cumulative COVID-19 Cases Integrated with Environmental Forest Knowledge Estimation: A Deep Learning Ensemble Approach

Abdelgader Alamrouni, Fidan Aslanova, Sagiru Mati, Hamza Sabo Maccido, Afaf. A. Jibril, A. G. Usman and S. I. Abba
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Abdelgader Alamrouni: Department of Environmental Education and Management, Faculty of Education, Near East University, Nicosia 700006, Cyprus
Fidan Aslanova: Department of Environmental Engineering, Faculty of Civil and Environmental Engineering, Near East University, Nicosia 700006, Cyprus
Sagiru Mati: Department of Economics, Yusuf Maitama Sule University, Kano 700282, Nigeria
Hamza Sabo Maccido: Department of Electrical and Computer Engineering, Faculty of Engineering, Baze University, Abuja 900288, Nigeria
Afaf. A. Jibril: Faculty of Clinical Sciences, Bayero University, Kano 700006, Nigeria
A. G. Usman: Department of Analytical Chemistry, Faculty of Pharmacy, Near East University, TRNC, Mersin 99138, Turkey
S. I. Abba: Interdisciplinary Research Center for Membrane and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia

IJERPH, 2022, vol. 19, issue 2, 1-22

Abstract: Reliable modeling of novel commutative cases of COVID-19 (CCC) is essential for determining hospitalization needs and providing the benchmark for health-related policies. The current study proposes multi-regional modeling of CCC cases for the first scenario using autoregressive integrated moving average (ARIMA) based on automatic routines (AUTOARIMA), ARIMA with maximum likelihood (ARIMAML), and ARIMA with generalized least squares method (ARIMAGLS) and ensembled (ARIMAML-ARIMAGLS). Subsequently, different deep learning (DL) models viz: long short-term memory (LSTM), random forest (RF), and ensemble learning (EML) were applied to the second scenario to predict the effect of forest knowledge (FK) during the COVID-19 pandemic. For this purpose, augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) unit root tests, autocorrelation function (ACF), partial autocorrelation function (PACF), Schwarz information criterion (SIC), and residual diagnostics were considered in determining the best ARIMA model for cumulative COVID-19 cases (CCC) across multi-region countries. Seven different performance criteria were used to evaluate the accuracy of the models. The obtained results justified both types of ARIMA model, with ARIMAGLS and ensemble ARIMA demonstrating superiority to the other models. Among the DL models analyzed, LSTM-M1 emerged as the best and most reliable estimation model, with both RF and LSTM attaining more than 80% prediction accuracy. While the EML of the DL proved merit with 96% accuracy. The outcomes of the two scenarios indicate the superiority of ARIMA time series and DL models in further decision making for FK.

Keywords: artificial intelligence; ARIMA; ensemble ARIMA; forest knowledge; prediction (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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