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Rights Holder and Contact
Hohmann, Alejandro; Muvva, Bhanu; Tong, Chunxia
Description
Debris Flows are a distinct type of landslide that suddenly occur without warning. They are fast-moving channels of water and soil that carry large natural objects like boulders and trees, or human-made objects including cars. In the American West, Debris Flows have directly caused death and property damage. Debris Flows often occur after rain events and the burn scars left behind by wildfires increase their likelihood. Given the increasing frequency of extreme weather events, it is critical to predict Debris Flows and take precautionary action before they occur. This project builds upon prior research of predicting Debris Flows using additional geological features and more advanced machine learning techniques. The project also includes an intuitive interface for decision makers to access these probability estimates. Research Data Curation Program, UC San Diego, La Jolla, 92093-0175 (https://lib.ucsd.edu/rdcp) This project relies on external software packages, modules/libraries, or programs, use of which may carry specific license requirements. Users should comply with any licenses specified within the contents of this project. Hohmann, Alejandro; Muvva, Bhanu; Tong, Chunxia (2023). Debris Flow Risk Analysis. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects. UC San Diego Library Digital Collections. https://doi.org/10.6075/J0PG1RZH
Type
dataset
Identifier
ark:/20775/bb6461505j
Language
English
Subject
Logistic regression Visualization Neural networks (Computer science) Machine learning Debris flow Post-fire debris glow Capstone projects Dash Task: Regression Data Science & Engineering Master of Advanced Study (DSE MAS) Wildfires DSE MAS - 2023 Cohort
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