Machine Learning for Avalanche Frequency Modeling
Description
The machine learning (Random Forest) was used to identify vegetation characteristics in avalanche paths. This helps determine avalanche return periods in specific avalanche paths.
Detailed example
The Random Forest model outputs vegetation classification that is necessary for identifying avalanche return periods.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
Benefits include results that inform avalanche forecasters, transportation departments, and infrastructure planners on estimating spatial extents of avalanche return periods.
Controls / human review
ATO: No; PIA: Not published
Data needed
We used high point density lidar data. Data collection and processing adhered to a maximum nominal post spacing of 0.35 m, with a mean point density of 16 points/m2. We also used four-band (red, green, blue, and near-infrared) NAIP imagery at 0.6-m horizontal spatial resolution.