A comparison of different heuristic, mathematical, and intelligent methods in urban landscape aesthetic evaluation (Case study: Gorgan city)

In today's era, human interventions have caused chaos in landscape patterns and degradation in landscape quality. Therefore, identifying landscape aesthetic beauty, and also fundamental planning and valuable areas, and proper planning and design in order to protect and promote the aesthetic value seem to be necessary and unavoidable. In this research, the aim is to investigate the performance of various experimental methods (multi-criteria evaluation using weighted linear combination), mathematical (logistic regression), and intelligent (neural network)) in estimating the suitability of the aesthetic value of Gorgan city. After theoretical studies and determination of effective criteria, mapping and standardization of the criteria were done and finally, the map of aesthetic-value suitability was prepared based on the methods of weighted linear combination, neural network, and logistic regression. In order to evaluate the performance of different methods and choose the optimal method, ground control points and ROC validation methods were used. The results showed that in the map resulting from the weighted linear combination method, a large part of the data was lost as a result of the linear combination of layers and weighting, and the neural network method with intelligent performance and the ability to combine and analyze non-linearly compared to the weighted linear combination method and also performing back and forth analysis compared to the logistic regression method, better separates the value of the studied area. According to the results of this research, it can be concluded that when there is little knowledge about the studied area and it is not possible to conduct field surveys to record valuable points of view, performing the weighted linear combination method can be a solution, but if it is possible to conduct field surveys to prepare a map of real educational samples as a dependent variable, more accurate results can be obtained with the help of the neural network method and logistic regression, more accurate results can be achieved, and in the meantime, the intelligent neural network method has a higher ability to distinguish the values of the environment image.