A method of assessing near-view scenic beauty models: A comparison of neural networks and multiple linear regression

Abstract

With recent advances in artificial intelligence, new methods are being developed that provide faster, and more consistent predictions for data in complex environments. In the field of landscape assessment, where an array of physical variables effect environmental perception, natural resource managers need tools to assist them in isolating the significant predictors critical for the protection and management of these resources. Recent studies that have utilized neural networks to assist in developing predictive models of scenic beauty that have typically utilized linear regression techniques have found limited success. The goal of this research is to compare NN's with linear regression models to determine their efficiency predictive capability for assessing near view scenic beauty in the Cedar City District of the Dixie National forest (DNF). Results of this study strongly conclude that neural networks are consistently better predictors of near view scenic beauty in spruce/fir dominated forests than hierarchical linear regression models.