PhD Student Profile
University of Tasmania
My research aims to develop innovative approaches to predict soil water retention function using machine learning and data assimilation algorithms with real-world dynamic soil datasets, resulting in an automatic learning and prediction system.
It will integrate several algorithms to learn from data, predict soil characteristics and discover the truth. The project began with a comprehensive analysis of the limitations associated with existing methods for soil water retention function prediction. This is a selection and evaluation process to verify their capacities for dynamic soil moisture trend simulation, parameter estimation, and optimisation. Some chosen algorithms were improved and integrated with soil physical models to predict soil moisture and hydraulic parameters.
An essential part of the research involves algorithms’ designing, coding, debugging, and testing in Python to ensure their accuracy, efficiency, and robustness. To validate the performance of proposed algorithms, experiments are implemented by diverse initial settings, multi-layer heterogeneity, and several years of soil water datasets. The objective was to provide a new, more accurate, and reliable AI-based system for predicting soil water retention function, offering substantial implications for fields such as agriculture and hydrology.
PhD Title: “Machine learning-based prediction system for soil water retention function”
PhD start date: December 2021
Supervisors: Dr Marcus Hardie, University of Tasmania, Assoc. Prof. Quan Bai, University of Tasmania
What interested you about this research?
I am interested in algorithm/model design and implementation to solve unknown/non-determined problems, especially applying artificial intelligence techniques to complex real-world problems.
What do you love about your PhD?
I love to face the challenge of complex and real-world problems and solve them with beautiful mathematical models and algorithms.
How will your PhD help make a difference for farmers?
My PhD will help improve our understanding of soil moisture characteristics, soil physical health, and suitable irrigation options.
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