Early warning of drought events is very helpful in preparing the vulnerable communities to their harmful impacts. In this project, you will use field observations, climate data derived from satellites along with several state-of-the art machine learning methods to find casual connections between climate variables and droughts that can help in improving drought forecasts.


The project will develop new prediction models for droughts and assess their abilities to capture the temporal and spatial characteristics of droughts over Australia.

We’re looking for a student with the following skills:

  • Knowledge of GIS/Remote sensing
  • Statistical skills
  • Programming in R or Python

Student benefits

The student will understand land-atmosphere feedback and learn how to:

  • Manipulate large datasets
  • Build machine learning models and assess their fidelity

Supervisors: A/Prof. Gab Abramowitz and Dr. Sanaa Hobeichi

Get involved

To learn more about this project, contact A/Prof. Gab Abramowitz or Dr. Sanaa Hobeichi.