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

Abstract

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.

A Temperature Compensation Method for aSix-Axis Force/Torque Sensor Utilizing Ensemble hWOA-LSSVM Based on Improved Trimmed Bagging

“…The selection of two parameters γ and σ 2 makes a difference to the performance of LSSVM. Optimization algorithms are practically utilized to search optimal parameters by scholars to avoid trail blindness and obtain better performance of the model [10,21,30].…”

“…Though LSSVM is powerful in nonlinear fitting, its performance is significantly influenced by the choice of its parameters. Obtaining parameters of LSSVM by blind search is a costly task; therefore, various optimization algorithms are adopted by scholars, such as the Cuckoo search algorithm (CSA), particle swarm optimization (PSO), and the sparrow search algorithm (SSA) [19][20][21]. The whale optimization algorithm (WOA) is a meta-heuristic optimization algorithm which is inspired by the bubble-net hunting behavior of humpback whales [22].…”

Survival Risk Prediction of Esophageal Squamous Cell Carcinoma Based on BES-LSSVM

“…The framework of the overall implementation of the survival prediction model for patients with ESCC is shown in Figure 8 . To verify the validity of this model, grasshopper optimization algorithm-least-squares support vector machine (GOA-LSSVM) [ 39 ], particle swarm optimization-least-squares support vector machine (PSO-LSSVM) [ 40 ], differential evolution-least-squares support vector machine (DE-LSSVM) [ 41 ], sparrow search algorithm-least-squares support vector machine (SSA-LSSVM) [ 42 ], bald eagle search-back propagation neural network(BES-BPNN), and bald eagle search-extreme learning machine(BES-ELM) are used for comparison.…”

Early Warning of the Construction Safety Risk of a Subway Station Based on the LSSVM Optimized by QPSO

“…The key to solving the LSSVM is to solve the prediction function. By combining the prediction function in Equation ( 16) with the structural risk function [14], the solution of the LSSVM can be equivalent to the following nonlinear optimization problem [31]:…”