Rui Hugman, Author at Groundwater Modelling Decision Support Initiative

Applied Decision Support Groundwater Modeling With Python: A Guided Self-Study Course

1 Comment / Featured, Guided Self Study, Tutorials / Rui Hugman

View the webinar recording introducing the course here. This free guided self-study course and weekly Q & A sessions with the GMDSI team provided guidance and answered any questions participants had. These sessions occurred in NZ/AUS time and USA/EU time. To view the recordings of the weekly sessions click below. The instructions for the installation of […]

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GMDSI Workshop: Focus on the problem. Don’t let the model become the problem

Leave a Comment / Events, Workshops / Rui Hugman

There are many management contexts where numerical simulation can productively support environmental decision-making. This is not because it can replicate environmental behaviour very well. (We should drop that illusion.) However decision-focussed simulation can: deliver information to decision-makers and stakeholders in ways that they can understand; deliver the repercussions of lack of information to decision-makers and

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Problem Decomposition in Decision-Support Groundwater Modelling

Leave a Comment / Featured, Monographs / Rui Hugman

Decision-support groundwater modelling is as much an art as it is a science. Much of the “art” is in decomposing the management problem that modelling must address. If this is done properly, then decision-support modelling has a much greater chance of being useful to decision-makers, while being acceptable to stakeholders. Problem decomposition requires that those

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Second PEST Conference

Leave a Comment / Events, past event / Rui Hugman

The second PEST conference will be held in San Diego, California. Subtitled “The Path from Data to Decisions”, it will run from March 6th to March 9th, 2023. The conference will address how tasks performed by PEST and PEST++ (which include calibration, sensitivity analysis, uncertainty analysis, optimisation and optimisation under uncertainty) can support model-based environmental

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The Maths Behind PEST and PEST++

Leave a Comment / Featured, Webinars / Rui Hugman

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 – Video Recording and PowerPoint PresentationWebinar 3: Wednesday 26th October – Video Recording and PowerPoint Presentation These webinars will give you a good understanding of the mathematical concepts that underpin model

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Webinar: Perspectives on Environmental Regulation

GMDSI Tech-Talk: Scripted Workflows vs. GUIs

Leave a Comment / Events, Webinars / Rui Hugman

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 it to support important decisions. GMDSI’s next webinar explores this topic by hosting presenters from both ends of the workflow spectrum. Both of our presenters are groundwater modelling heavyweights. They

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The Regulatory Framework: Consultations and Discussions

Leave a Comment / Featured, Monographs, Workshops / Rui Hugman

The last in a series of meetings that GMDSI hosted on the groundwater regulatory framework was held on August 18th, 2022. This meeting (attended by 73 people, most of whom were not regulators) discussed the use of groundwater modelling in the regulatory framework. After a brief introduction by Peter Baker (Office of Water Science), and

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intro to pyemu

Leave a Comment / Decision Support Notebooks, Education / Rui Hugman

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 very “high level”. We will not go into detail, merely show some of the core functionality as a primer for subsequent tutorials. We assume the reader has at least some

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Introduction to Regression

Leave a Comment / Decision Support Notebooks, Education / Rui Hugman

Introduction to Regression¶ This tutorial proves an overview of linear regression. It illustrates fitting a polynomial to noisy data, including the role of SSE (reponse curve), degrees of freedom, the gradient of that surface (Jacobian) and overfitting. We use a very simple model (a polynomial function) to illustrate these core concepts and the maths

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Rui Hugman