Hide Advanced Options
Courses - Spring 2024
Engineering, Civil Department Site
Open Seats as of
05/22/2024 at 01:30 PM
Seminar; Statistical and Machine Learning Models for Natural Hazards Prediction
Credits: 3
Grad Meth: Reg, Aud
Cross-listed with ENCE489X. Credit only granted for ENCE489X or ENCE689X. Infrastructure and other engineered systems are exposed to a variety of natural hazards, such as precipitation, high winds, storm surge, and earthquake-induced ground shaking. Assessment of the risks these hazards pose to engineered systems often involves the development of models to predict hazard severity. While hazard severity prediction models may involve a range of techniques, this course centers on prediction models that involve statistical and machine-learning approaches. Specifically, this course will focus on the practical application of statistical and machine learning regression models, including data processing, model specification, model development, and model assessment within the context of natural hazard predictions. Models selected for consideration in this course (e.g., linear/non-linear models, tree-based approaches, neural networks,and Gaussian process regression) are informed by present-day practices for multiple types of natural hazards.