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A Neural Network Model to Study Factors Impacting the Selection of Hazardous Fuel Treatment Types in Colorado’s National Forests
Abstract
Background: Selecting suitable fuel treatment locations and types is important for reducing wildfire risks in Colorado's national forests. Understanding factors influencing these decisions is needed for effective management. Aims: This study analyzes land management data from 2015 to 2024 using a single multiclass neural network model to understand the drivers influencing fuel treatment decisions in 11 national forests in Colorado. Methods: We utilize Forest Activity Tracking System data, incorporating variables such as wildfire risk, landscape features, and human influences. The model employs a feedforward backpropagation technique to train a neural network model on the spatial dataset. Key Results: The model identifies significant factors associated with past fuel treatment decisions, including burn probability, wildfire hazard potential, conditional flame length, and proximity to structures. The analysis reveals the importance of these variables in shaping treatment selection strategies, with the model achieving an AUC of 0.91, indicating strong predictive performance across the six treatment categories. Conclusions: Neural networks provide a robust method for analyzing past fuel treatment choices. By accurately identifying key factors, this approach enhances our understanding of historical treatment decisions and provides suggestions to improve future fuel treatment decisions. Implications: This approach can enhance wildfire mitigation planning across Colorado's 14.5 million acres of national forests. The findings support more informed wildfire mitigation strategies, with potential applications extending to broader forest management practices.
WF24024 Accepted 02 December 2024
© CSIRO 2024
