This tutorial explains four ways to explore the uncertainties of two predictions made by a relatively simple, fast-running model. These are: Linear analysis Sampling a
GMDSI presented three webinars delivered by John Doherty (author of PEST). Webinar 1: Wednesday 28th September – Video Recording and PowerPoint PresentationWebinar 2: Wednesday 12th October
We invite you to join us for an upcoming GMDSI Webinar to be held 11am-1pm (AEST) on Thursday August 18 on Zoom. Register for the
There has been a lot of discussion recently on groundwater modelling workflows – that is, how to build and history-match a model, and then use
intro_to_pyemu Intro to pyEMU¶ This notebook provides a quick run through some of the capabilities of pyemu for working with PEST(++). This run through is
Introduction to Regression¶ This tutorial proves an overview of linear regression. It illustrates fitting a polynomial to noisy data, including the role of SSE
These videos were prepared as lectures for the Groundwater Modelling topic of the Groundwater Hydrology course at Flinders University. They aim to introduce history matching
In contrast to linear uncertainty analysis, non-linear methods do not suffer from the limitation of assuming a linear relationship between model predictions and model parameters.
A variance-covariance matrix, often referred to as a covariance matrix, is a square matrix that provides covariances between pairs of elements of a random vector.
The present tutorial addresses the ability (or otherwise) of yet-ungathered data to reduce the uncertainties of decision-critical predictions using linear analysis utilities from the PEST
GMDSI is managed by the National Centre for Groundwater Research and Training (NCGRT) and administered by Flinders University.