Background Millions of people have been infected worldwide in the COVID-19 pandemic. In this study, we aim to propose fourteen prediction models based on artificial neural networks (ANN) to predict the COVID-19 outbreak for policy makers. Methods The ANN-based models were utilized to estimate the confirmed cases of COVID-19 in China, Japan, Singapore, Iran, Italy, South Africa and United States of America. These models exploit historical records of confirmed cases, while their main difference is the number of days that they assume to have impact on the estimation process. The COVID-19 data were divided into a train part and a test part. The former was used to train the ANN models, while the latter was utilized to compare the purposes. The data analysis shows not only significant fluctuations in the daily confirmed cases but also different ranges of total confirmed cases observed in the time interval considered. Results Based on the obtained results, the ANN-based model that takes into account the previous 14 days outperforms the other ones. This comparison reveals the importance of considering the maximum incubation period in predicting the COVID-19 outbreak. Comparing the ranges of determination coefficients indicates that the estimated results for Italy are the best one. Moreover, the predicted results for Iran achieved the ranges of [0.09, 0.15] and [0.21, 0.36] for the mean absolute relative errors and normalized root mean square errors, respectively, which were the best ranges obtained for these criteria among different countries. Conclusion Based on the achieved results, the ANN-based model that takes into account the previous fourteen days for prediction is suggested to predict daily confirmed cases, particularly in countries that have experienced the first peak of the COVID-19 outbreak. This study has not only proved the applicability of ANN-based model for prediction of the COVID-19 outbreak, but also showed that considering incubation period of SARS-COV-2 in prediction models may generate more accurate estimations.

Severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) is a novel coronavirus that has infected more than 2,900,000 individuals worldwide. The widespread of coronavirus 2019 (COVID-19) brings about the need for a prediction model to adopt appropriate evidence-based strategies. In this study, multi-gene genetic programming (MGGP), as one of the artificial intelligence models, has been proposed for the first time for predicting the COVID-19 outbreak. Although this is a challenging task due to significant fluctuations of daily confirmed cases, the results achieved by MGGP are promising. To be more specific, the predicted confirmed cases are acceptably close to the observed values for seven countries considered in this study. Thus, MGGP is suggested for developing estimation models of COVID-19. Furthermore, similarities and differences between the proposed prediction models are presented. Finally, it is discussed why a country-based prediction model is recommended.

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The need for computer application for solving water distribution networks (WDNs) is inevitable for both educational and practical purposes. In this paper, three h-based methods for solving WDN including h-based Newton-Raphson method, finite element method, and gradient algorithm are implemented using MATLAB and Excel spreadsheet. The input data should be first inserted into Excel spreadsheet while MATLAB codes utilize this data to solve the pipe network. The output results are also presented in Excel for convenience. As educational facets of this application was the main focus of this paper, the details of this computer application were step-by-step explained with codes. Furthermore, a simple network selected from literature was analyzed using the three h-based methods. Finally, the presented codes and this computer application are believed to encourage many educators and applicants to assess them for both educational and practical purposes in this engineering field. ß 2017 Wiley Periodicals, Inc. Comput Appl Eng Educ 25:129-141, 2017; View this article online at wileyonlinelibrary.com/journal/cae;

Estimation of bridge backwater has been one of practical challenges in hydraulic engineering for decades. In this study, Genetic Programming (GP) has been applied for estimating bridge backwater for the first time based on the conducted literature review. Furthermore, two new explicit equations are developed for predicting bridge afflux using Genetic Algorithm (GA) and hybrid MHBMO-GRG algorithm. The performances of these models are compared with Artificial Neural Network (ANN) and several explicit equations available in the literature considering both laboratory and field data. Based on five considered performance evaluation criteria, the two new explicit equations outperform the ones available in the literature. Furthermore, GP and ANN achieve the 2 best results in favor of four out of five considered criteria for train and test data, respectively. To be more specific, ANN improves the MSE and R 2 values of the explicit equation developed using GA by 44% and 12% for the test data while GP enhances the corresponding values by 62% and 9% for the train data. Finally, the results demonstrate that not only artificial intelligence models considerably improve bridge afflux estimation than the explicit equations but also the suggested equations significantly improve the accuracy of the available explicit ones.

With the emergence of computers, literatures of water distribution network (WDN), and application of computer‐aided programs for modeling WDNs have been significantly improved. Although MATLAB and Excel spreadsheet provide suitable facilities for both academic and practical purposes, the comprehensive application of these programs in WDN analysis has not been addressed. In this paper, the step‐by‐step implementation of three Q‐based methods is presented for solving WDNs using these programs. In order to focus more on the educational aspects of computer application, a simple pipe network is analyzed using these Q‐based methods. However, as basics of the implementation are sufficiently covered, the provided codes can be improved to analyze more complicated pipe networks. © 2017 Wiley Periodicals, Inc. Comput Appl Eng Educ 25:277–289, 2017; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.21796

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