Project Info
Assessing the Value of Remote Sensing Data for Rock Fall Hazard Prediction
Gabriel Walton | gwalton@mines.edu
Rock fall hazards pose a multimillion dollar challenge for the Colorado Department of Transportation (CDOT). Remote-sensing data (e.g. Lidar, photogrammetry) has the potential to aid in the prediction thus prevention of these hazards. The student will perform statistical analyses to determine the best types of data to prevent/ predict rock fall. This work has the potential to help define methodology of prioritization of resources for hazard mitigation for CDOT.
Here are two links to news stories relevant to the motivation for the proposed research:
https://www.aspendailynews.com/news/crews-reopen-i–westbound-through-canyon-after-rockfall/article_996e3852-294f-11e9-af88-4793b7efce59.html
An example of a value of information study in a different domain can be found here:
https://library.seg.org/doi/full/10.1190/geo2013-0337.1?casa_token=O7LQdTv8D2cAAAAA%3A_16pp3O967irr6m6XjCU_eU3NbNi9R-j6gIQYZ0gOGEey29etBNrR4DWRHH9gwuaD8m6A09k0P4
Student Preparation
Qualifications
A basic knowledge of statistics/probability theory is all that is required.
Time Commitment
20 hours/month
Skills/Techniques Gained
The project will expose the student to both statistical tools need to assess the value of information, the fundamentals of rock fall and the fundamentals of remote sensing data.
Mentoring Plan
Weekly or bi-weekly meetings to address students questions, provide guidance and monitor progress. We can also team up the student with graduate students working with similar data.