Machine learning the soil water function

Machine learning soil water retention function from soil moisture sensor data

Degree type

PhD

Closing date

29 October 2021

Campus

Hobart

Citizenship requirement

Domestic/International Onshore

About the research project

To develop a system to determine the soil water retention function SWRC and soil water limits from in situ soil moisture sensors. Knowledge of soil water is very important for management of agriculture and irrigation practices. The soil water retention function is related to the physical characteristics of the soil pores which dictates the soils’ ability to hold and transmit water.  The derivation the SWRC usually involves a significant amount of time-consuming laboratory analyses which are prone to disturbance required in performing the tests.  Advances in computing power and modelling methods have led to the possibilities of using the data science techniques of automatic optimisation and self-calibration with a range of soil system models to try to determine parameters that describe the SWRC from in situ sensor data rather than determining the parameters by physical experiment.  Due to the highly variable nature of soil characteristics and limitations of many of the models in their representation of the SWRC the ability to generalise findings is usually not possible and opportunities exist for further exploration of methods, algorithms, feature selection and aggregation of model outputs to mitigate against some of these shortcomings.

Objectives:

  1. To collect primary, experimental field data and archived secondary data sets to allow experimentation with a range of data science methods to characterise the specific physical environment under observation.
  2. To identify, implement and evaluate methods to derive model parameters to describe the soil water retention function and water limits for the data sets available.
  3. To utilise the generated soil water retention function and water limits in systems models to provide actionable outcomes.

Note: this thesis topic was previously commenced by Darren West who withdrew for personal reasons shortly after confirmation. The new candidate will be able to build upon the work commenced by Darren, who wishes to remain engaged with the project. Approval from the SOILCRC has been granted for re-commencing this candidature.

Primary Supervisor

Meet Dr Marcus Hardie

Funding

Applicants will be considered for a Research Training Program (RTP) scholarship or Tasmania Graduate Research Scholarship (TGRS) which, if successful, provides:

  • a living allowance stipend of $28,597 per annum (2021 rate, indexed annually) for 3.5 years
  • a relocation allowance of up to $2,000
  • a tuition fees offset covering the cost of tuition fees for up to four years (domestic applicants only)

If successful, international applicants will receive a University of Tasmania Fees Offset for up to four years.

As part of the application process you may indicate if you do not wish to be considered for scholarship funding.

Eligibility

The project is open to domestic (Australian and New Zealand) and international applicants who are already in Australia (onshore) at the time of submitting their application.

Due to current Australian COVID-19 travel restrictions the University cannot accept applications from international applicants who are currently overseas.

Applicants should review the Higher Degree by Research minimum entry requirements and the following additional eligibility criteria specific to this project:

  • Research must be undertaken on a full-time basis
  • Applicants must already have been awarded a first class Honours degree or hold equivalent qualifications or relevant and substantial research experience in an appropriate sector
  • A Masters of Research Degree with a Minimum Research Component
  • Applicants must be able to demonstrate strong research and analytical skills

Selection Criteria

The project is competitively assessed and awarded.  Selection is based on academic merit and suitability to the project as determined by the College.

Additional essential selection criteria specific to this project:

  • Experience of python and or Matlab programming (inc. SciKit-Learn, Tensor Flow or similar)
  • Comfortable with high level pure and applied mathematics
  • Knowledge of Data Science and/or machine learning principles
  • Strong practical skills (in-field fault finding and diagnosis)
  • Independent critical thinking and a willingness to proactively explore different pathways to success
  • Ability to rapidly acquire a working knowledge of soil physics principals
  • Ability to conduct field and laboratory work if required

Application process

There is a three-step application process:

  1. Select the project, and check you meet the eligibility and selection criteria;
  2. Contact the Primary Supervisor, Dr Marcus Hardie, if you have any questions about the project; and
  3. Click here to submit an application by the closing date listed above.
    • Copy and paste the title of the project from this advertisement into your application. If you don’t correctly do this your application may be rejected.
    • As part of your application you will be required to submit a covering letter, a CV including 2 x referees and your project research proposal.

Following the application closing date applications will be assessed within the College. Applicants should expect to receive notification of the outcome by email.

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