Present position: Research Fellow, Singapore-Delft Water Alliance (SDWA), National University of Singapore
|Thesis title:||Dealing with complexity and dimensionality in water resources management|
|Research area:||Applied systems analysis and operations research|
The optimal management of large environmental systems is often limited by the high computational burden associated to the process-based models commonly adopted to describe such systems. This work proposes a novel data-driven dynamic emulation modelling approach for the construction of small, computationally efficient models that accurately emulate the main dynamics of the original process-based models, but with less computational requirements. The approach combines the many advantages of data-based modelling in representing complex, non-linear relationships, but preserves the state-space representation, which is both particularly effective in several applications (e.g. optimal management and data assimilation) and facilitates the ex-post physical interpretation of the emulator structure, thus enhancing the credibility of the model to stakeholders and decision-makers. The core mechanism is a novel variable selection procedure that is recursively applied to a data-set of input, state and output variables generated via simulation of the process-based model. The effectiveness of the proposed approach is demonstrated on two real-world case studies: the reduction of a 3D, physically-based model describing the hydrodynamic and ecological conditions of Googong reservoir (AUS), the reduction of a large conceptual model providing the irrigation water demand of the Muzza-Bassa Lodigiana district (IT).