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A Method for Prediction of Waterlogging Economic Losses in a Subway Station Project

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School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, China
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Academic Editors: Alfredo Milani, Valentina Franzoni and Marco Baioletti
Mathematics 2021, 9(12), 1421; https://doi.org/10.3390/math9121421
Received: 29 January 2021 / Revised: 16 June 2021 / Accepted: 17 June 2021 / Published: 19 June 2021
(This article belongs to the Special Issue Evolutionary Algorithms in Artificial Intelligent Systems)
In order to effectively solve the problems of low prediction accuracy and calculation efficiency of existing methods for estimating economic loss in a subway station engineering project due to rainstorm flooding, a new intelligent prediction model is developed using the sparrow search algorithm (SSA), the least-squares support vector machine (LSSVM) and the mean impact value (MIV) method. First, in this study, 11 input variables are determined from the disaster loss rate and asset value, and a complete method is provided for acquiring and processing data of all variables. Then, the SSA method, with strong optimization ability, fast convergence and few parameters, is used to optimize the kernel function and the penalty factor parameters of the LSSVM. Finally, the MIV is used to identify the important input variables, so as to reduce the predicted input variables and achieve higher calculation accuracy. In addition, 45 station projects in China were selected for empirical analysis. The empirical results revealed that the linear correlation between the 11 input variables and output variables was weak, which demonstrated the necessity of adopting nonlinear analysis methods such as the LSSVM. Compared with other forecasting methods, such as the multiple regression analysis, the backpropagation neural network (BPNN), the BPNN optimized by the particle swarm optimization, the BPNN optimized by the SSA, the LSSVM, the LSSVM optimized by the genetic algorithm, the PSO-LSSVM and the LSSVM optimized by the Grey Wolf Optimizer, the model proposed in this paper had higher accuracy and stability and was effectively used for forecasting economic loss in subway station engineering projects due to rainstorms. View Full-Text
Keywords: subway station project; waterlogging economic losses; sparrow search algorithm; least-squares support vector machine; mean impact value subway station project; waterlogging economic losses; sparrow search algorithm; least-squares support vector machine; mean impact value
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MDPI and ACS Style

Wu, H.; Wang, J. A Method for Prediction of Waterlogging Economic Losses in a Subway Station Project. Mathematics 2021, 9, 1421. https://doi.org/10.3390/math9121421

AMA Style

Wu H, Wang J. A Method for Prediction of Waterlogging Economic Losses in a Subway Station Project. Mathematics. 2021; 9(12):1421. https://doi.org/10.3390/math9121421

Chicago/Turabian Style

Wu, Han, and Junwu Wang. 2021. "A Method for Prediction of Waterlogging Economic Losses in a Subway Station Project" Mathematics 9, no. 12: 1421. https://doi.org/10.3390/math9121421

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