Prediction of extreme events is heavily reliant on numerical models, and such process-based models are important tools in the understanding of physical processes. This research encompasses all techniques related to model-based prediction through model confrontation with data. Studied algorithms include, but are not limited to, filtering and smoothing techniques, real-time control, uncertainty analysis, and parameter estimation. Domains of application include structural analysis, traffic behaviour, flood and drought management, urban development, etc. Both new algorithm developments and existing models and/or algorithms used in new and innovative ways for resilient solutions to societal problems is the basis for this thematic area.
To become leaders in innovative data fusion techniques for complex engineering problems.
Building tools to transfer data into knowledge for actions that benefit society.
Awards and Honours
- A/Prof Valentijn Pauwels has been awarded a Future Fellowship in 2013 from the ARC.
- Pauwels, V., 2014-2017, A novel and theoretically consistent method for correcting systematic errors in earth observation data and earth system model results, ARC Future Fellowship, AU$608K.
- Sarvi, M., 2014-2017, Planning and managing road transport systems for extreme events through spatial enablement, ARC Linkage $550K.
- Walker, J., Rüdiger, C., Jackson, T., Entekhabi, D., De Jeu, R., Merlin, O., Kim, E., Renzullo, L., 2014-2016, MoistureMonitor: A multi-mission soil moisture monitoring system for a water limited future, ARC Discovery, $565k
- Sarvi, M., 2012-2015, Innovative tools to improve stations design and management of crowds in emergency and panic conditions, ARC Linkage, $480K.
- Pauwels, V., Walker, J., 2014, Improving flood forecast skill using remote sensing data, Bushfire and Natural Hazards CRC, AU$960K.
Xiaoying Cao (Shelly)
Mohammad Sajjad Shafiei
- The Monash computing facilities