Introduction

Recent wildfires in Colorado have highlighted the critical need for effective hazardous fuel treatments (HFT) to reduce wildfire risks in the state's national forests. This study employs a neural network model to analyze data from 2001 to 2021, aiming to understand the factors influencing fuel treatment decisions in 11 national forests. By examining the drivers behind these decisions, the study seeks to enhance future wildfire management strategies.

Methods

The study utilized data from the Forest Activity Tracking System, incorporating variables such as wildfire risk, landscape features, and human influences. A feedforward backpropagation neural network model was trained on this spatial dataset. The model's input features included burn probability, wildfire hazard potential, and proximity to structures, among others. The architecture consisted of an input layer, a hidden layer with 64 neurons, and an output layer predicting six types of fuel treatments.

Key Findings

The neural network model revealed that burn probability, wildfire hazard potential, and proximity to structures significantly influence fuel treatment decisions. The analysis indicated that prescribed fires are often conducted in lower-risk areas to maintain low fuel loads, while thinning is prioritized in regions with higher wildfire risks. Multi-treatment approaches are more likely near urban areas, reflecting a strategy to balance wildfire risk reduction and infrastructure protection.

Implications

This study demonstrates that neural networks can effectively analyze past fuel treatment choices, providing valuable insights for future strategic planning. The findings suggest that incorporating such models into wildfire mitigation planning can enhance decision-making processes, leading to more informed and efficient fuel treatment strategies. The approach could potentially be applied to broader forest management practices, supporting more resilient and sustainable ecosystems. By linking empirical data with practical treatment decisions, this model helps identify trends in past management decisions, providing a method to standardize and improve fuel treatment selection processes.