GA-BP neural network algorithm-based city ecological construction evaluation method

Abstract

The invention relates to a GA-BP neural network algorithm-based city ecological construction evaluation method and belongs to the field of smart city ecological construction. The method comprises thefollowing steps of constructing a city ecological evaluation index system; generating neural network training and test samples; determining a topological structure of a neural network; setting relatedparameters of neural network training; optimizing initial weight and threshold of the neural network by utilizing a genetic algorithm; and inputting the training and test samples for training and checking the GA-BP neural network to finally build a GA-BP neural network algorithm-based city ecological evaluation model. The method can realize a city ecological construction evaluation function and has relatively high scientificity, validity and practicality.

Description

technical field [0001] The invention belongs to the field of smart city ecological construction, and relates to an evaluation method for urban ecological construction based on a GA-BP neural network algorithm. Background technique [0002] With the improvement of people's living standards, the strengthening of ecological awareness, and the formation of a consensus on sustainable development and other factors, urban ecological construction has increasingly become the focus of the world. The concept of urban ecology emphasizes that people and nature are the equal center, taking into account factors such as society, nature, and the environment, which well embodies the concept of sustainable and harmonious development. In recent years, the urban economic situation has been continuously improved, accompanied by the decline of the overall environmental quality and the increasing threats to the natural system. The sustainable development of the city is facing enormous pressure. On...

AI Technical Summary

Problems solved by technology

Generally speaking, these methods are statistically simple and easy, but the disadvantage is that they have strong subjectivity, and in the face of a large amount of index data, they are only simple statistics and calculations, and the depth of data mining is not enough, especially not easy. Uncovering Implicit Correlation Indicators

This can lead to less scientific, persuasive and less intelligent evaluation results

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