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Research Thesis Topic

Real-time Flood Early Warning: Copula-Statistical Joint Models and Machine Learning System Design


Topic ID
26

Thesis Topic/Title
Real-time Flood Early Warning: Copula-Statistical Joint Models and Machine Learning System Design

Description

Australia suffers from extreme and catastrophic floods. Flood monitoring is undertaken in conjunction with forecasts to provide advanced warning and heightened chance of a flood by estimation of river heights, streamflow, time of rainfall occurrence, and peak flow rates at a specified point in time resulting from changes in rainfall. Bureau of Meteorology, in partnership with agencies at state and local government, provides water level forecasts in rivers. Despite their effectiveness, the methods can be time-consuming, expensive and complex to implement in geographically diverse locations.

In this exciting project, students will learn about copula-statistical joint models and machine learning to predict hourly and daily flood events. Such forecasts are used in flood mitigation design. A new flood index based on daily effective precipitation for flood monitoring will be used. This method will consider a weighted sum of current and antecedent rainfall, and a time-dependent reduction formula applied on the recent (vs. older) rainfall to account for water accumulation due to hydrological factors. Hourly & daily data will be used to develop flood model considering rainfall accumulation redistributed by an objective formula factored viz a time-dependent function, and applying machine learning models to predict flood events in advance.

The project will suit students with background in engineering hydrology, mathematics, computing, climate, water management and environment. Students will also explore deep learning to enhance the performance accuracy of flood prediction system. 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.


Principal Supervisor

Associate Supervisors

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

Field of Research
  • Artificial Intelligence and Image Processing
  • Atmospheric Sciences
  • Civil Engineering
  • Environmental Science and Management


Application Open Date
29/02/2016

Application Close Date
31/12/2020

USQ Scholarship Applications

Other Scholarship Funding Details
Australia Awards (Country-Specific Closing Dates) 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 or climate science, computing and agriculture.




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