Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Waterlogging warning has gradually become an important means of urban waterlogging prevention and control. However, the current urban waterlogging warning model still has problems such as low accuracy, real-time performance, and poor model convergence. To better address these issues, this article combines particle swarm optimization (PSO) and deep neural networks (DNN) to explore the construction of early warning models in depth. First, the influencing factors of urban waterlogging were analyzed in the article, and the PSO algorithm was used to determine the influencing factors of urban waterlogging in this study; then, the selected influencing factors were used as input data to design a backpropagation (BP) neural network (NN) structure; several representative waterlogging points can be selected to construct a BP NN model and perform fitting analysis. Afterward, combined with the PSO algorithm, the constructed model was trained and optimized. In this article, the old town of Hefei City is used as the experimental object, and the building model is used to conduct early warning research on waterlogging. The study’s findings indicate that the PSO + BP model’s average accuracy in 10 early warning tests is as high as 97.95%, with a response time of only 0.022 ms; the average accuracy and response time of the BP model are 89.06% and 0.255 ms, respectively; the Bayesian network model (BN model) is 82.78% and 0.275 ms. Through the analysis of actual cases in the old urban area of Hefei City, the advantages of this model in practical application were verified, and a new intelligent warning method for urban waterlogging prevention and control was provided, demonstrating its effectiveness and potential in dealing with complex nonlinear problems.
Waterlogging warning has gradually become an important means of urban waterlogging prevention and control. However, the current urban waterlogging warning model still has problems such as low accuracy, real-time performance, and poor model convergence. To better address these issues, this article combines particle swarm optimization (PSO) and deep neural networks (DNN) to explore the construction of early warning models in depth. First, the influencing factors of urban waterlogging were analyzed in the article, and the PSO algorithm was used to determine the influencing factors of urban waterlogging in this study; then, the selected influencing factors were used as input data to design a backpropagation (BP) neural network (NN) structure; several representative waterlogging points can be selected to construct a BP NN model and perform fitting analysis. Afterward, combined with the PSO algorithm, the constructed model was trained and optimized. In this article, the old town of Hefei City is used as the experimental object, and the building model is used to conduct early warning research on waterlogging. The study’s findings indicate that the PSO + BP model’s average accuracy in 10 early warning tests is as high as 97.95%, with a response time of only 0.022 ms; the average accuracy and response time of the BP model are 89.06% and 0.255 ms, respectively; the Bayesian network model (BN model) is 82.78% and 0.275 ms. Through the analysis of actual cases in the old urban area of Hefei City, the advantages of this model in practical application were verified, and a new intelligent warning method for urban waterlogging prevention and control was provided, demonstrating its effectiveness and potential in dealing with complex nonlinear problems.
Optimization Strategies for Urban Waterlogging Warning in Complex Environments: Based on Particle Swarm Optimization and Deep Neural Networks
2024
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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