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.

中文翻译：

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基于高阶多小波神经网络算法的景观生态规划分析

风景园林兼具自然属性和社会属性，是人们保护自然环境的体现。工业革命以来，现代工业迅速发展。它提高了人们的生活水平，消耗了大量的森林、能源等自然资源。生态环境遭到极大破坏，园林景观受到影响。因此，寻找景观生态评价方法和景观生态规划具有重要意义。本文提出一种结合小波分析和人工神经网络的高阶小波神经网络算法。提出一种基于高阶小波神经网络算法的景观生态评价模型，对景观生态进行评价，为景观生态规划提供参考数据。结果表明，小波神经网络达到目标精度的训练次数比BP神经网络减少了3600倍。WNN 的 MSE 和 MAE 分别为 0.0639 和 0.1501。模型对景观生态综合评价指标的平均误差为0.005。该模型对景观土地资源可持续性评价的准确率为98.67%。以上结果表明，基于小波神经网络的模型能够有效、准确地完成景观生态评价，进而为景观生态规划提供决策依据，具有较高的实用性。