## 5 Conclusions  
This study sheds new light on the effectiveness of non-parametric machine learning algorithms in localizing storm-induced tree-related outage risk at the device exposure zone level, where faults are detected and handled by utilities. A total of 15 predictor variables were analyzed using five non-parametric machine learning algorithms and evaluated based on their model performance and confidence interval values. The RF emerged as the best model according to the performance metrics of accuracy, the AUC-ROC, precision, and the F1-score. Both the RF and the SVM showed superior performance according to the AUC-ROC metric when identifying DEZs with tree-related outage risk. The RF and XGBoost demonstrated higher precision values, highlighting weaknesses in other models' ability to capture falsely identified outage presence areas (false positives) compared to the RF and XGBoost. Conversely, the SVM and k-NN reported higher recall values, indicating their ability to identify outage presence areas while minimizing falsely identified outage absence areas (false negatives). When a model captures a higher number of false positives, utilities have to spend extra capital and labor on resiliency programs. In contrast, utilities will be misguided when the risk model tends to detect more false negatives, causing risk to both humans and infrastructure. Accurate modeling of tree-related outage probability enables efficient resource allocation, prevents damage to the grid infrastructure, and lowers the cost of vegetation management.  
While this study has discussed desirable outcomes, we believe it is important to acknowledge and address the limitations encountered. These limitations provide valuable insights for future research. Tree health is a critical determinant of tree failures during adverse weather events. Additionally, resistance to storm conditions largely varies across different species and the physical structure of the trees. Therefore, further development of vegetation risk models requires information on tree health and tree species. Also, further research is needed to address model uncertainty and to optimize the performance by employing different modeling techniques, including ensemble machine learning approaches.  
Conceptualization, H.W. and C.W.; methodology, H.W. and C.W.; software, H.W. and J.P.; validation, H.W.; formal analysis, H.W.; investigation, H.W.; resources, C.W.; data curation, H.W. and J.P.; writing--original draft preparation, H.W. and C.W.; writing--review and editing, R.F., D.C., J.P. and A.S.P.; supervision, C.W., R.F. and D.C.; project administration, C.W.; funding acquisition, C.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data is contained within the article.  
We gratefully acknowledge the support of the Eversource Energy Center at the University of Connecticut for providing the data necessary for this study. Additionally, we extend our appreciation to [PERSON], a graduate student in the Department of Natural Resources and the Environment, for his valuable insights into improving this paper.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results.