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  • Data-driven Predictive Analytic Models for Rainfall, Temperature or Drought Simulation with Hybrid Machine Learning-wavelet Transformation-bootstrapping Algorithms

Research Thesis Topic

Data-driven Predictive Analytic Models for Rainfall, Temperature or Drought Simulation with Hybrid Machine Learning-wavelet Transformation-bootstrapping Algorithms


Topic ID
24

Thesis Topic/Title
Data-driven Predictive Analytic Models for Rainfall, Temperature or Drought Simulation with Hybrid Machine Learning-wavelet Transformation-bootstrapping Algorithms

Description

Chunks of environmental signals (classified normally as big data) contain useful features that may be analysed to extract information for climate risk management and agricultural simulation models. Agriculture, ecosystems, health and environment are hugely impacted by natural hazards such as floods, drought and heatwaves. Our challenges to embrace climate adaptation, agricultural planning or water management is addressed with efficient and reliable models.

This project innovates high-precision modelling of meteorological properties (e.g. rainfall, temperature, drought) to assist decision-makers in designing robust forewarning systems for climate risk prediction and assessment. Artificial Intelligence models are well-placed to analyse big data, particularly time-series of rainfall, temperature and drought. The project develops predictive analytics model using artificial neutral network (ANN), support vector machines (SVM), extreme learning machine (ELM), Bayesian techniques, genetic programming, Gaussian process regressions, etc. and test their application in environmental modelling, agriculture, hydrology and water resources management.

The project will develop hybrid drought models for climate risk modelling in agriculture, climate risk management and environment. The project will suit students with background in environmental science, civil engineering (hydrology), mathematics, computing and climate/atmospheric physics. The project is scalable for further research, such as to a Masters or a PhD program to advance research careers, acquire programming skills and modelling. Students will learn artificial neural network models, machine learning in agriculture, expert system development with smart predictive tools and evaluation of such models in real-time forecasting of drought prediction.


Principal Supervisor


Research Affiliations
  • Institute for Agriculture and the Environment
  • International Centre for Applied Climate Sciences
  • National Centre for Engineering in Agriculture
  • School of Agricultural, Computational and Environmental Sciences

Field of Research
  • Applied Mathematics
  • Atmospheric Sciences
  • Civil Engineering
  • Environmental Science and Management


Application Open Date
29/02/2016

Application Close Date
31/12/2019

USQ Scholarship Applications

Other Scholarship Funding Details
Australia Awards (Country-Specific Closing Dates), and Australian Endeavor Scholarships (Close June 2018)

Pre-approved for Ethics
Not Applicable

Admission Requirements

Please review the admission requirements for the academic program associated with this Thesis Topic

Suits students interested with background in civil engineering (hydrology), atmospheric science, environmental physics, climate science, computing and water science.




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