How Tree Risk Assessment Methods Work: Sensitivity Analyses of Sixteen Methods Reveal the Value of Quantification and the Impact of Inputs on Risk Ratings

M.B. Norris and G.M. Moore

Abstract: Sixteen tree risk assessment methods were subjected to sensitivity analysis to determine which factors most influenced the output of each method. The analyses indicate the relative influence that the input variables exert on the final risk value. Excel was used to create a simple ± 25% or ± 1 rank change (depending on the method) for each criterion, with the change to the output recorded as a percentage. Palisade’s @Risk software was used to undertake a Monte Carlo (with Latin Hypercube sampling) simulation of 5000 iterations based on the input variables and output formula. From the simulation, multivariate stepwise regression was undertaken to determine the influence of each method’s input variables in determining the output values. Results from the sensitivity analysis indicate some clear and strong differences amongst the 16 methods, reflecting that the underlying mathematics, input categories, ranges, and scaling influence the way that different methods process and express risk. It is not surprising that methods perform differently in different circumstances and express risk level differently. The analyses demonstrated that most methods placed too great an emphasis on limited aspects of risk assessment. Most methods strongly focused on the hazard or defect aspects of assessment and the likelihood of failure rather than the consequence aspect of an assessment. While methods were uniquely different, they could be placed into 3 broad groups: Group 1 methods produced a normal distribution with most values around the mean; Group 2 methods produced outputs at the lower end of the risk scale; and Group 3 methods produced outputs evenly if not continuously across the risk scale. Users of tree risk assessment should understand the strengths and weaknesses of any method used, as it could be relatively simple to challenge the results of a risk assessment based on limitations inherent in the underlying methodology.

Keywords: Risk Assessment; Risk Consequence; Risk Likelihood; Sensitivity Analysis; Tree Risk

https://doi.org/10.48044/jauf.2020.030