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

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.

Neurocomputing Modelling of Hydrochemical and Physical Properties of Groundwater Coupled with Spatial Clustering, GIS, and Statistical Techniques

“…In recent years, significant advancement has risen for handling nonstationary, dynamic, and non-linearity time series data using AI-based models mainly applied in GW modelling and management can be reviewed and understood using comprehensive analysis as presented in Figure 2a. Various problems in hydrological simulation related to a single AI-based/machine learning modelling have been addressed regardless of their promising performance demonstrated in various studies [32]. A survey of the reported literature on Scopus database yielded 614 peer-reviewed papers starting from 1985 up to January 2022 adopted over the literature utilizing the feasibility of broad interest for GW modelling using different AI-based models.…”

A predictive model for daily cumulative COVID-19 cases in Ghana

“…18 Various authors have modeled, predicted and forecast cumulative cases of COVID-19 to study the dynamics of cumulative cases over a period of time. [37][38][39][40][41][42] The authors in 42 used cumulative covid-19 data and time series models to forecast the epidemiological trends of COVID-19 pandemic for top-16 countries where 70%-80% of global cumulative cases are high. Also, a deep learning ensemble approach has been adapted by the authors in 41 to determine the best auto-regressive integrated moving average (ARIMA) model for predicting and forecasting cumulative COVID-19 cases across multi-region countries.…”

“…[37][38][39][40][41][42] The authors in 42 used cumulative covid-19 data and time series models to forecast the epidemiological trends of COVID-19 pandemic for top-16 countries where 70%-80% of global cumulative cases are high. Also, a deep learning ensemble approach has been adapted by the authors in 41 to determine the best auto-regressive integrated moving average (ARIMA) model for predicting and forecasting cumulative COVID-19 cases across multi-region countries. Nonlinear growth models such as the Gompertz, Richards, and Weibull were implemented to cumulative covid-19 data in order to study the daily cumulative number of COVID-19 cases in Iraq.…”

Spatiotemporal Variability Assessment of Trace Metals Based on Subsurface Water Quality Impact Integrated with Artificial Intelligence-Based Modeling

“…AI and other computational machine learning models have been recently developed and have been demonstrated to be effective in comparison to various classical, statistical physics-based, and mathematical models [17][18][19][20]. The promising applications of AI-based models are not limited to the understanding and removal of HMs but also extend to the system identification of science and engineering problems [21][22][23][24][25][26][27].…”