Reducing uncertainty in ecosystem service modelling through weighted ensembles

Quantifying Uncertainty through Global and Mesoscale Ensembles

The long-term goal of this project is to develop robust global and mesoscale ensemble analysis and forecast systems that are able to provide probabilistic forecast guidance in an operational environment. Although most operational centers rely solely on single deterministic forecasts from numerical weather prediction models, recent advances in probabilistic prediction, or ensemble forecasting, h...

Reducing Price Uncertainty through Demand Side Management

The integration of intermittent resources of power generation, such as wind and solar, will lead to unprecedented energy price fluctuations and increasing price uncertainty. Besides improving forecasting, electricity retailers can meet this challenge by partly adopting the demand side of the electricity markets to the fluctuating supply side. The necessary communication infrastructure for this ...

Uncovering Ecosystem Service Bundles through Social Preferences

Ecosystem service assessments have increasingly been used to support environmental management policies, mainly based on biophysical and economic indicators. However, few studies have coped with the social-cultural dimension of ecosystem services, despite being considered a research priority. We examined how ecosystem service bundles and trade-offs emerge from diverging social preferences toward...

Reducing Model Systematic Error through Super Modelling

Weighted Clustering Ensembles

Cluster ensembles offer a solution to challenges inherent to clustering arising from its ill-posed nature. Cluster ensembles can provide robust and stable solutions by leveraging the consensus across multiple clustering results, while averaging out emergent spurious structures that arise due to the various biases to which each participating algorithm is tuned. In this paper, we address the prob...