## 1 Introduction  
Severe weather, such as high winds, hurricanes, thunderstorms, and tornadoes, frequently induces tree failures in proximity to power lines, resulting in power outages and damage to utility infrastructure. These weather-related power outages cost the United States of America (USA) $25-75 billion annually [1]. This cost will continue to increase with the increasing frequency and intensity of storm activities due to climate change. During severe weather, tree failures are responsible for most power outages [2]. Tree failures not only cause catastrophic damage to power lines but also pose risks to public safety and infrastructure. The costs of restoring power lines and the anticipated increase in storm activity due to climate change have driven utilities to enhance grid resilience by identifying vegetation risks and reinforcing infrastructure. Accurate modeling of tree-related power outage risks along distribution power lines is essential for effectively implementing grid resiliency programs ahead of storm events. Numerous scientific investigations have focused on predicting power outages due to vegetation and identifying the underlying factors contributing to these outages in power distribution systems. For instance, [PERSON] et al. [3] introduced a statistical framework to predict tree-related outages under normal operational conditions. Their investigation specifically focused on the impact of tree-trimming practices on the incidence of vegetation-related outages, utilizing a dataset comprising historical outages, geographical data, and tree-trimming records. In a separate study, [PERSON] et al. [4] proposed a methodology to predict the rate of tree-related outages due to annual vegetation growth, measured as the number of outages per mile-year. Key inputs to their models included historical outage data and climatic variables known to influence vegetation growth. [PERSON] et al. [5] conducted a study to assess the effects of various factors on predicting vegetation-related outages during hurricane events. By leveraging LiDAR tree height data alongside information on vegetation management practices and system infrastructure, they developed an ensemble machine learning algorithm to predict the likelihood of vegetation-related outages during hurricane events. [PERSON] et al. [6] proposed a data-driven methodology to predict the number of vegetation-related outages in power distribution systems using time series and nonlinear machine learning regression models. Over the past two decades, much research has explored the applicability of various parametric and non-parametric models to power outage modeling problems. Initially, several parametric statistical models, such as the negative binomial regression model and Generalized Linear Models (GLMs), were used to predict the number of hurricane-related [4; 7] and ice-storm-related outages [7]. [PERSON] et al. [8] compared multiple models (parametric, GLMs, and semi/non-parametric models, Generalized Additive Models (GAMs), Bayesian Additive Regression Trees (BART), and classification and regression trees (CART)) to predict post-hurricane damage to the electrical overhead distribution network (i.e., utility poles). They observed higher accuracy rates with semi- and non-parametric models than parametric ones. [PERSON] et al. [9] modeled the outage duration using both parametric (regression methods) and non-parametric models (BART, multivariate adaptive regression splines (MARS), and CART) and demonstrated the applicability of the BART model with its predictive accuracy and lower prediction error compared to other methods. Recently, many studies have adopted machine learning algorithms for power outage-related research and demonstrated the benefits of using machine learning (ML) models to predict power outages [5; 10; 11; 12]. ML models have become more popular due to numerous advantages, including flexibility, adaptability, and the ability to analyze diverse data types [13].