The characteristics of birds' sounds assume a primary role in tourists' mental restoration and stress recovery. The aim of this research is the evaluation of birds' sound composition in mental restoration of urban tourists to develop a decision support system as a practical tool. In this order, the recorded sounds of six birds were composed (57 composed sounds) and the human perception approach was used to assess the impact of sounds on the urban tourist's mental restoration. The MLP (Multi-Layer Perceptron), RBFNN (Radial Basis Function Neural Network) and SVM (Support Vector Machine) models were developed for mental restoration prediction in different birds' sound compositions. The results indicated that RBFNN model output (R² training = 0.89, and R² test = 0.85) has the best accuracy compared to the MLP and SVM models in prediction of birds' soundscape score in natural urban areas. According to the sensitivity analysis, the values of White eared Bulbul (Pycononotus leucotis), Great Tit (Parus major), House Sparrow (Passer domesticus), Laughing Dove (Spilopelia senegalensis), White Wagtail (Motacilla alba), and Eurasian Magpie (Pica pica) are prioritized respectively that influence the RBFNN model outputs. In practice, the designed environmental decision support system tool is applied by urban planners, managers, psychoacoustic researchers, and landscape architects to predict the landscape score in different birds' habitats.

鸟的声音特征在游客的心理恢复和压力恢复中起主要作用。这项研究的目的是评估城市游客心理恢复过程中的鸟类声音成分，以开发决策支持系统作为一种实用工具。按照此顺序，录制了六只鸟的声音（其中有57种是声音），并且使用了人类感知的方法来评估声音对城市游客的心理恢复的影响。开发了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，用于预测不同鸟类声音成分的心理恢复。结果表明，RBFNN模型输出（R ²训练= 0.89，R ²测试= 0.85）与MLP和SVM模型相比，在预测自然城市地区鸟类的声景得分时具有最高的准确性。根据敏感性分析，白耳鳞茎（Pycononotus leucotis），大山雀（Parus major），麻雀（Passer domesticus），笑鸽（Spilopelia senegalensis），白Wa（Motacilla alba）和欧亚Mag（Pica）的值分别对影响RBFNN模型输出的优先级（pica）进行优先级排序。实际上，城市规划人员，管理人员，心理声学研究人员和景观设计师使用设计的环境决策支持系统工具来预测不同鸟类栖息地的景观得分。