Analysis of Landscape Ecological Planning Based on the High-Order Multiwavelet Neural Network Algorithm

• Published in: Computational intelligence and neuroscience Vol. 2021; no. 1; p. 9420532
• Main Authors: Yu, ChuanDong, Du, Nan
• Format: Journal Article
• Language: English
• Published: United States Hindawi 2021; John Wiley & Sons, Inc
• Subjects: Accuracy; Algorithms; Analysis; Artificial neural networks; Back propagation networks; China; Conservation of Natural Resources; Decision making; Ecological effects; Ecological evaluation; Ecology; Genetic algorithms; Humans; Industrial development; Landscape architecture; Landscape ecology; Model accuracy; Natural resources; Neural networks; Neural Networks, Computer; Planning; Principles; Sustainable development; Wavelet Analysis; China
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2021/9420532

Landscape architecture has both natural and social properties, which is the embodiment of people protecting the natural environment. Since the industrial revolution, the modern industry has developed rapidly. It has increased the living standard of people and consumed a lot of natural resources such as forest and energy. The ecological environment has been greatly damaged, and the landscape of gardens has been affected. Therefore, it is of great significance to find a method to evaluate the landscape ecology and plan the landscape ecology. This paper proposes a new high-order wavelet neural network algorithm combining wavelet analysis and artificial neural network. A model of ecological evaluation of landscape based on high-order wavelet neural network algorithm is proposed to evaluate the landscape ecology and provide reference data for the ecological planning of the landscape. The results show that the training times of the wavelet neural network to achieve the target accuracy are 3600 times less than those of the BP neural network. The MSE and MAE of the WNN are 0.0639 and 0.1501, respectively. The average error of the model to the comprehensive evaluation index of the landscape ecology is 0.005. The accuracy of the model to evaluate the sustainability of landscape land resources is 98.67%. The above results show that the model based on the wavelet neural network can effectively and accurately complete the evaluation of landscape ecology and then provide a decision-making basis for landscape ecological planning, which is of high practicability.