Waterlogging model prediction method based on firefly algorithm

A firefly algorithm and model prediction technology, applied in the field of waterlogging model prediction based on the firefly algorithm, can solve the problem that the neural network is easy to fall into local minima, and achieve the effect of accurate prediction and speeding up the process.

Inactive Publication Date: 2020-08-25
HEFEI UNIV
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Problems solved by technology

[0004] The purpose of the present invention is to overcome the deficiencies of the existing waterlogging model and solve the shortcoming that the n...

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  • Waterlogging model prediction method based on firefly algorithm
  • Waterlogging model prediction method based on firefly algorithm
  • Waterlogging model prediction method based on firefly algorithm

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[0013] In order to make the technical means, creative features, objectives and effects of the present invention easy to understand, the present invention will be further clarified below with reference to the accompanying drawings.

[0014] Such as figure 1 As shown, the present invention provides a waterlogging model prediction method based on the firefly algorithm, which includes the following steps:

[0015] Step 1. Use the training error of the neural network as the fitness function of the individual firefly to train the threshold and weight of the neural network to obtain a trained neural network;

[0016] This step 1 specifically includes:

[0017] Step 1.1: Initialization

[0018] Step 1.1.1: The number of neurons in the input layer, hidden layer, and output layer of the neural network is divided into n, m, g; real number coding is used to represent the individual, and the real number coding includes the hidden layer threshold b j , The output layer threshold θ k , The weight betw...

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Abstract

The invention belongs to the technical field of waterlogging models, and particularly relates to an waterlogging model prediction method based on a firefly algorithm, and the method comprises the steps: 1, training a weight and a threshold of a neural network through employing a training error of the neural network as a fitness function of a firefly individual, and obtaining an optimal parameter;2, constructing a waterlogging prediction model by using the optimal parameters obtained in the step 1; according to the firefly algorithm-based waterlogging prediction model provided by the invention, training the weight and the threshold of the neural network by adopting the firefly swarm algorithm, and performing waterlogging prediction by adopting the trained neural network model. According tothe method, the defects that a traditional rainfall ponding model needs a large amount of basic data information, requires elaborate hydrological and physical processes and the like are overcome, meanwhile, the defect that a neural network is prone to falling into local minimum is overcome, and the method has important significance in accurate prediction of urban inland inundation and acceleration of the process of a smart city.

Description

Technical field [0001] The invention belongs to the technical field of waterlogging models, and specifically relates to a waterlogging model prediction method based on a firefly algorithm. Background technique [0002] With the development of society and the acceleration of the process of industrialization, the ecological environment has been destroyed, and waterlogging disasters occur frequently. Waterlogging will not only cause traffic paralysis, but also cause great losses to the people's personal safety and property. The research and prediction of waterlogging models are particularly important. Predicting urban waterlogging in advance and improving the accuracy of prediction can effectively reduce the impact of urban waterlogging and accelerate the process of smart cities. [0003] The traditional rainfall water model SWMM requires a lot of basic data and requires proficiency in hydrophysical processes, which limits the accuracy and use of the model. With the development of s...

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Application Information

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IPC IPC(8): G06Q10/04G06N3/00G06N3/08
CPCG06Q10/04G06N3/006G06N3/084
Inventor 张琛王晓峰檀明陈圣兵邹乐谢贻富刘胜军孟虎胡永培
Owner HEFEI UNIV
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