Written by Joshua Simmons 2022
Also available as a Streamlit web app!
In this notebook, we will fit a Bayesian Linear Regression to predict shoreline change due to coastal storms. This will mirror the simple empirical model developed by:
Harley, M. D., Turner, I. L., Short, A. D., & Ranasinghe, R. (2009). An empirical model of beach response to storms–SE Australia. In Coasts and Ports (pp. 600-606).
This model is of the form:
To provide uncertainty alongside the model prediction, we will use the probabilistic programming language NumPyro to fit a Bayesian Linear Regression.
Disclaimers:
- This is an overly simplified analysis for the purpose of demonstrating uncertainty quantification (via Bayesian inference) with NumPyro and presentation via streamlit.
- Model predictions of shoreline change should not be relied upon for real-world applications.