Machine learning and optimisation for modelling coral reef evolution


Globally, coral reef systems are under threat from major changes in environmental parameters (e.g. sea level, sea surface temperature, pH and water quality). Forward Forward Stratigraphic Modelling (FSM) represents a powerful tool to model the past and future evolution coral reef systems. This research project will utilise optimisation and Bayesian inference methods for established coral reef models. It will use machine learning methods to investigate the contribution or impact of specific environmental parameters on reef model evolution and allow the more accurate prediction of fossil reef cores.


Professor Jody Webster, Dr Rohitash Chandra, Dr Tristan Salles

Research Location

School of Geosciences

Program Type



The past 10 years has seen significant advances in the use of Forward Stratigraphic Modelling (FSM) to model carbonate sedimentary systems. However, most efforts have been focused on large-scale simulations of carbonate platform systems over long time scales (millions of years). To successfully model coral reef systems significant challenges need to be overcome including: (i) too coarse spatial and temporal scales; (ii) poorly represented biological processes (e.g. spawning, settlement, growth and competition); and (iii) the oversimplification of key physical–chemical–biological processes involved in sediment production, transport and deposition. PyReef model is a complex reef modelling software tool that requires constraint parameter optimisation for accurate prediction of reef cores that represents reef evolution over thousands of years. Bayesian inference methods have been used in the past for uncertainty quantification in free parameters for these models. However, there are a number of limitations when the complexity of the model increases with additional parameters. The absence of prior knowledge regarding the parameters makes it a major challenge for inference and optimisation. The first part of the project will explore hybrid methods that feature Bayesian inference with evolutionary algorithm for optimisation and uncertainty quantification. This will include multi-threading implementations of the hybrid algorithms for timely convergence. Machine learning methods would be used for investigation of the contribution or impact of the parameters in the reef models. This will help in determining which of the features (eg. hydo-dynamic flow, Malthusian (competition) and sediment flux parameters) make the major contributing factors towards the model for better accuracy of reef core prediction.

Additional Information

In this project you will interact directly with scientists from Geocoastal Research and Earthbyte Groups in the School of Geosciences as well as the Centre for Translational Data Science. This represents a unique opportunity to work across the disciplines and learn advanced techniques in the analysis of empirical coral reef data, numerical modeling and data science.

HDR Inherent Requirements
In addition to the academic requirements set out in the Science Postgraduate Handbook, you may be required to satisfy a number of inherent requirements to complete this degree. Example of inherent requirement may include:

- Confidential disclosure and registration of a disability that may hinder your performance in your degree;
- Confidential disclosure of a pre-existing or current medical condition that may hinder your performance in your degree (e.g. heart disease, pace-maker, significant immune suppression, diabetes, vertigo, etc.);
- Ability to perform independently and/or with minimal supervision;
- Ability to undertake certain physical tasks (e.g. heavy lifting);
- Ability to undertake observatory, sensory and communication tasks;
- Ability to spend time at remote sites (e.g. One Tree Island, Narrabri and Camden);
- Ability to work in confined spaces or at heights;
- Ability to operate heavy machinery (e.g. farming equipment);
- Hold or acquire an Australian driver’s licence;
- Hold a current scuba diving license;
- Hold a current Working with Children Check;
- Meet initial and ongoing immunisation requirements (e.g. Q-Fever, Vaccinia virus, Hepatitis, etc.)

You must consult with your nominated supervisor regarding any identified inherent requirements before completing your application.

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Reef Modelling, Machine learning, and data science

Opportunity ID

The opportunity ID for this research opportunity is: 2319

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