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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