Monthly Prediction of Reference Evapotranspiration in Northcentral Nigeria Using Artificial Intelligence Tools: A Comparative Study

Abdullahi, Jazuli; Elkiran, Gözen

doi:10.1007/978-3-030-92127-9_25

Cited by 1 publication

(2 citation statements)

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“…One of the most significant aspects of any ML-based prediction is the selection of the most dominant inputs; failure to do that may lead to errors and inaccuracy in results (Abdullahi and Elkiran 2021;Elkiran et al 2021). However, with difference in the rate of infections per day, population density and mitigating measures put in place by the African countries, variation in performance based on the 7-input variables is observed.…”

Section: Results Of the Standalone Modelsmentioning

confidence: 99%

“…(1) To determine the accuracy and performance of the applied models for the modelling of COVID-19 pandemic across 10 African countries, 4 global statistical indices were used including mean absolute deviation (MAD) (Khatri et al 2020), mean square error (MSE) (Hussain and Khan 2020), root mean square error (RMSE) (Abdullahi et al 2019a) and determination coefficient (R 2 ) (Abdullahi and Elkiran 2021) given by: where a i , p i , a and N are the actual values, predicted values, mean of the actual values and number of observations, respectively.…”

Section: Data Normalization and Performance Criteriamentioning

confidence: 99%

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Multi-region machine learning-based novel ensemble approaches for predicting COVID-19 pandemic in Africa

Ibrahim¹,

Tulay²,

Abdullahi

2022

Environ Sci Pollut Res

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Coronavirus disease 2019 (COVID-19) has produced a global pandemic, which has devastating effects on health, economy and social interactions. Despite the less contraction and spread of COVID-19 in Africa compared to some other continents in the world, Africa remains amongst the most vulnerable regions due to less technology and unequipped or poor health system. Recent happenings showed that COVID-19 may stay for years owing to the discoveries of new variants (such as Omicron) and new wave of infections in several countries. Therefore, accurate prediction of new cases is vital to make informed decisions and in evaluating the measures that should be implemented. Studies on COVID-19 prediction are limited in Africa despite the risks and dangers that the virus possessed. Hence, this study was performed to predict daily COVID-19 cases in 10 African countries spread across the north, south, east, west and central Africa considering countries with few and large number of daily COVID-19 cases. Machine learning (ML) models due to their nonlinearity and accurate prediction capabilities were employed for this purpose, including artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and conventional multiple linear regression (MLR) models. As any other natural process, the COVID-19 pandemic may contain both linear and nonlinear aspects. In such circumstances, neither nonlinear (ML) nor linear (MLR) models could be sufficient; hence, combining both ML and MLR models may produce better accuracy. Consequently, to improve the prediction efficiency of the ML models, novel ensemble approaches including ANN-E and SVM-E were employed. The advantage of using ensemble approaches is that they provide collective benefits of all the standalone models, thereby reducing their weaknesses and enhancing their prediction capabilities. The obtained results showed that ANFIS led to better prediction performance with MAD = 0.0106, MSE = 0.0003, RMSE = 0.0185 and R 2 = 0.9059 in the validation step. The results of the proposed ensemble approaches demonstrated very high improvements in predicting the COVID-19 pandemic in Africa with MAD = 0.0073, MSE = 0.0002, RMSE = 0.0155 and R 2 = 0.9616. The ANN-E improved the standalone models performance in the validation step up to 10%, 14%, 42%, 6%, 83%, 11%, 7%, 5%, 7% and 31% for Morocco, Sudan, Namibia, South Africa, Uganda, Rwanda, Nigeria, Senegal, Gabon and Cameroon, respectively. This study results offer a solid foundation in the application of ensemble approaches for predicting COVID-19 pandemic across all regions and countries in the world.

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Section: Results Of the Standalone Modelsmentioning

confidence: 99%