On-demand: Python for hydrology and hydrogeology
Explore Python programming for water modelling.
Take sessions anytime, at your own pace with unlimited course access for 30-days.
In this 3-part course attendees will learn how to perform; data wrangling and multivariate exploratory data analysis, time series analysis and data visualisation. Each session will be hosted by experts in their field, who will delve in-depth into the key topic, explaining best practice techniques and approaches. Practical examples and demonstrations will be analysed, allowing attendees to apply the knowledge gained and learn hands-on how to use Python- in order to save you time, money and integrate systems effectively.
Format: 6+ hours of training recordings to step through via the learning platform
Cost: AUD $495 (includes GST)
This course is designed to be highly practical, with 6+ hours of training session recordings.
The course will cover 3 main topics across 3 parts.
Part 1 | Lead by Luk Peeters
Data wrangling and multivariate exploratory data analysis
1. Hydrochemistry data
- Download data from GA’s website
- Explore data through scatter plots
- Make a Piper plot (and map)
- Multivariate analysis: principal component analysis, clustering (touching on machine learning in scikit learn)
2. Exploratory Data Analysis APY Lands groundwater dataset
- Load pre-compiled dataset
- Summarise dataset
- Visually explore relationships and test hypothesis in the dataset using violin-plots
3. Datacube: AWRA
- Load dataset of Australian Water Resources Assessment model from Bureau of Meteorology website
- Visualise and explore maps and time series of AWRA outputs
- Multivariate analysis and visualisation
Part 2 | Lead by Chris Turnadge
Time series analysis
1. Data pre-processing
- Using interpolation to fill gaps
- Detection and removal of outliers
- Resampling to higher or lower sampling resolution
- Temporal differencing
- Detrending data using time and frequency domain methods
2. Decomposing hydrograph data
- Quantifying the relative contributions of component processes
3. Interpreting responses to time-lagged processes
- Demonstration of convolution
- Regression deconvolution
4. Interpreting responses to periodic processes
- The discrete Fourier transform
- Periodogram-based approaches
- Harmonic least squares
Part 3 | Lead by Vincent Post
1. Data visualisation and linking it with Google Earth
2. Visualisation of modelled flowpaths in 3D
3. Evaluation of pumping tests
The course is delivered through units via the learning platform
- 6+ hours of recordings
- Pre-and-post-course materials to go through via the learning platform.
- Exercises between the 3 parts.
- Manual of the course and working model download/s.
- Ability to access all the online course materials with unlimited course access for 30-days.
Pre-course reading and video watching is encouraged.
A good internet connection and software and downloads as described in the learning platform.
On completion of the course attendees will be issued with a Certificate of Participation.