Environmental modeling of landscape aesthetic value in natural urban parks using artificial neural network technique

Abstract

An accurate assessment of the landscapes in a region requires having sufficient information of the influential factors as well as the type, manner, and the impact rate of each of them on the user’s perception of the landscape quality. The purpose of this study is modeling landscape aesthetic quality of urban parks using an artificial neural network to predict the value of landscape aesthetic and prioritizing the influential variables of the model. To evaluate the landscape aesthetic quality in the urban park, a combination of user perspective and artificial neural network modeling approach has been used. The aesthetic quality of 100 urban park landscapes was quantified based on citizens' perception. Totally, 15 landscape attributes were recorded as influential variables on visual quality of landscape. According to the results, the multi-layer perceptron model with structure of 15-8-1 (15 input variables, 8 neurons in the hidden layer, and one output variable) and the maximum value of coefficient of determination (R2) in three data sets, namely training, validation, and test which are 0.97, 0.88 and 0.90, present the best performance of structure optimization. Accordingly, land slope, flowers and bushes, buildings, and hard surfaces ratio with a model sensitivity coefficient of 0.56, 0.24, 0.07, and 0.07, respectively, show the maximum effect on the landscape aesthetic quality in urban parks. The developed multi-layer perceptron model in MATLAB software is known as a decision support system in designing the structure of urban parks and also provides possibility of predicting the landscape aesthetic value in new parks.