Sycamore failure hazard classification model (SFHCM): an environmental decision support system (EDSS) in urban green spaces

Abstract

Although the pruning of trees is known as the one of the principal domains of green space management, it is includes shortcomings in practical models or methodologies to classify or prioritize hazardous trees. This paper aims to formulate physical and biological and associated tree factors with sycamore failure probability which results to classify tree failure hazard using artificial neural network (ANN) modeling, as an environmental decision support system (EDSS). Considering the outputs of modeling process, multi-layer feed-forward network (MLFN), which is used to train sycamore (Platanus orientalis) failure hazard classification model (SFHCM), indicates that the performance of SFHCM is statistically accurate. Changes in physical and biological tree factors with the maximum priority in sensitivity analysis assist decision makers to increase SFHCM class and decrease tree failure hazard. Finally a soundly applicable EDSS with new tool is designed for green space managers to prioritize criteria in sycamore failure hazard in order to plan for the most appropriate mitigation plan.