Abstract Urban parks enhance the aesthetic quality of urban area and increase the restorative potential of cities to avoid the negative psycho-physiological impact of living in the built environment. This research aimed to model some aesthetic preference and mental restoration values in urban parks based on landscape natural characteristics to compare the MLP (Multi-Layer Perceptron), RBFNN (Radial Basis Function Neural Network) and SVM (Support Vector Machine) models in urban parks aesthetic and mental restoration potential prediction. Therefore, we recorded 11 landscape characteristics in 200 urban parks. We developed the landscape model to predict aesthetic and mental restoration potential using data mining techniques such as MLP, RBFNN, and SVM. The SVM model was developed as the most accurate model to predict the landscape score of urban parks. SVM the model represents the highest value of R2 in training (0.9), test (0.83) and all data sets (0.88). According to the sensitivity analysis, trees, water bodies, buildings, flowers, and decorations in park landscapes were prioritized respectively as the most significant inputs influencing the SVM model outputs. The results of SVM, especially its determined accuracy (R2 = 0.83) in comparison with MLP (R2 = 0.77), and RBFNN (R2 = 0.78) test results showed that SVM is the most successful comparative landscape assessment model in aesthetic and mental restoration potential prediction. Using MATLAB software, the SVM model is applicable in urban parks where the characteristics of the newly designed landscape are in the range of studied area. The SVM modeling technique would be applicable for landscape architectures to model the landscape of urban areas. The urban park landscapes with more trees, water bodies, flowers, decorations, and fewer buildings would likely attract citizens' attraction and recover their mental stresses. In practice, the designed graphical user interface is applied by landscape architects to predict the landscape score.

中文翻译：

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城市公园审美偏好与心理恢复预测：环境建模方法的应用

摘要 城市公园提高了城市地区的审美品质，增加了城市的修复潜力，避免了居住在建成环境中的负面心理生理影响。本研究旨在基于景观自然特征对城市公园中的一些审美偏好和心理修复价值进行建模，以比较城市公园中的 MLP（多层感知器）、RBFNN（径向基函数神经网络）和 SVM（支持向量机）模型审美和心理恢复潜力预测。因此，我们记录了 200 个城市公园的 11 个景观特征。我们开发了景观模型，使用 MLP、RBFNN 和 SVM 等数据挖掘技术来预测审美和心理恢复潜力。SVM 模型被开发为预测城市公园景观得分的最准确模型。SVM 该模型代表 R2 在训练 (0.9)、测试 (0.83) 和所有数据集 (0.88) 中的最高值。根据敏感性分析，公园景观中的树木、水体、建筑物、花卉和装饰分别作为影响 SVM 模型输出的最重要输入。SVM 的结果，尤其是与 MLP (R2 = 0.77) 和 RBFNN (R2 = 0.78) 测试结果相比的确定准确度 (R2 = 0.83) 表明，SVM 是最成功的美学和心理恢复潜力比较景观评估模型预言。使用MATLAB软件，SVM模型适用于新设计景观特征在研究区域范围内的城市公园。SVM 建模技术将适用于景观建筑以模拟城市地区的景观。多树木、多水体、多花、多装饰、少建筑的城市公园景观，可能会吸引市民的注意力，恢复他们的心理压力。在实践中，景观设计师应用设计的图形用户界面来预测景观分数。