Pipe failure prediction of wastewater network using genetic programming: Proposing three approaches

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@Article{HOSEINGHOLI:2023:asej,

• author = "Pegah Hoseingholi and Ramtin Moeini",
• title = "Pipe failure prediction of wastewater network using genetic programming: Proposing three approaches",
• journal = "Ain Shams Engineering Journal",
• volume = "14",
• number = "5",
• pages = "101958",
• year = "2023",
• ISSN = "2090-4479",
• DOI = "doi:10.1016/j.asej.2022.101958",
• URL = "https://www.sciencedirect.com/science/article/pii/S2090447922002696",
• keywords = "genetic algorithms, genetic programming, Wastewater network, Pipe failure prediction, Number of failure, Artificial neural network",
• abstract = "Finding critical points of the wastewater network by rebuilding the infrastructure is cheaper than repairing it after occurring failure. This task can be done by using predictive approaches. Therefore, in this study, a new method is proposed to predict the number of pipe failures per length of wastewater network. For this purpose, genetic programming (GP) is used to predict the pipe failure of sewer network in Isfahan region 2 using the data from year 2014 to 2017.The obtained results are compared with the results of corresponding artificial neural network (ANN) model. For this purpose, three different approaches are proposed. In the first approach named GA-CLU-T, the number of pipe failures is predicted using all data. However, in the second ones named GA-CLU-Y, the models are created and trained using the data of year 2014 and the obtained model is used to predict the number of pipe failure for other years in future. Finally, the third ones named GA-CLU-R is proposed to determine the number of pipe failures in other regions. Here, two different models are proposed for each approaches using GP method. The result shows that the best RMSE (R2) values of first, second and third approaches for test data set are 0.00316 (0.966), 0.00074 (0.996) and 0.00075 (0.997), respectively. The results show that the result accuracy of GP models is better than the corresponding ANN models",

}

Genetic Programming entries for Pegah Hoseingholi Ramtin Moeini

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