The improvement of climate change investigations by developing and applying innovative evolutionary subset time series modelling using semi-parametric sparse-patterned approaches (Externally led)

The project examines complexity of climate change on environmental energy resources and will produce superior quantitative tools for improved decision-making in environmental risk management and investments. The project comprises 4 stages: modelling environmental system movements using evolutionary parallel-computing neural networks; predicting the likelihood of large climate disasters; evaluating environmental energy sector impacts; examining effects on housing markets. The investigation involves the combination of inter-disciplinary concepts and models that provide a unique examination of environmental system movements, leading to practical insights in the application of these models to climate change, energy and housing.