Forest management should be directed towards multifunctional management and utilization of forest services (other than wood production) in order to achieve maximum utilization and minimum degradation. Artificial intelligence enables forest managers to plan for utilization of forest landscape aesthetic values. Visual quality evaluation is a stochastic problem in natural forest landscapes and it is influenced by forest characteristics. We aimed to landscape visual quality evaluation by expert/human-perception-based approach and application of artificial intelligence modeling techniques for the visual quality prediction of forest landscapes. Therefore, we recorded five landscape attributes in 100 forest landscapes. We developed the stochastic model to evaluate visual quality potential by artificial intelligence techniques. Comparing to multi-layer regression (R² = 0.588) and multi-layer perceptron (R² = 0.847), the radial basis function (RBF) (R² = 0.887) model represents the highest value of R² in the test data set. The water, shrubs, roads, rocky hills, and trees, in forest landscapes were introduced respectively as the most important attributes which influence the RBF model. The designed graphical user interface tool, as an environmental decision support system, evaluates landscape visual quality of forests, and it helps to solve stochastic problems such as visual quality value.

森林管理应针对森林服务（木材生产除外）的多功能管理和利用，以实现最大程度的利用和最低程度的退化。人工智能使森林管理者能够规划森林景观美学价值的利用。视觉质量评估是天然森林景观中的一个随机问题，受森林特征的影响。我们旨在通过基于专家/人类感知的方法对景观视觉质量进行评估，并将人工智能建模技术应用于森林景观的视觉质量预测。因此，我们在100个森林景观中记录了五个景观属性。我们开发了随机模型来通过人工智能技术评估视觉质量潜力。²  = 0.588）和多层感知器（R ²  = 0.847），径向基函数（RBF）（R ²  = 0.887）模型代表测试数据集中R ^2的最大值。分别介绍了森林景观中的水，灌木，道路，石质山和树木，这是影响RBF模型的最重要属性。设计的图形用户界面工具作为一种环境决策支持系统，可以评估森林的景观视觉质量，并有助于解决诸如视觉质量价值之类的随机问题。