Research Thesis Topic
Disaster Risk Early Warning System: Flood Forecasting with Copula Models and Deep Learning Algorithms
In a changing climate, nations suffer from extreme and catastrophic floods. Flood monitoring and forecasting provides advanced warnings to mitigate the impacts of floods. These are mainly achieved by an estimation of river height, streamflow, time of rainfall and peak flow at a specified point in time resulting from changes in rainfall. The Australian Bureau of Meteorology, in partnership with national agencies provides river water level forecasts. Despite their effectiveness, the traditional flood forecasting methods can be somewhat time-consuming, expensive and relatively complex to implement especially in geographically diverse locations.
In this exciting project, research students learn about joint distribution (i.e. multivariate) models based on copulas and deep learning algorithms to predict hourly and daily flood events. Such forecasts can be used in flood mitigation design system. The study will adopt flood indices based on daily effective precipitation to monitor and forecast the events considering 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 research project is strongly centred on natural disaster risk mitigation theme. It will suit students with good background in engineering, hydrology, mathematics, computing, climate or water resources. Students will explore deep learning to enhance the performance accuracy of flood prediction system.
This project is suitable for PhD, Research or Coursework Masters Thesis. It provides opportunity to publish in high quality Q1 journals. The research student will be part of the Advanced Data Analytics Research Group under Prof Ravinesh Deo.
For more details see https://eportfolio.usq.edu.au/view/view.php?id=116719.
- Institute for Agriculture and the Environment
- International Centre for Applied Climate Sciences
- School of Agricultural, Computational and Environmental Sciences
- Artificial Intelligence and Image Processing
- Atmospheric Sciences
- Environmental Science and Management
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.