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Timescale: Oct.2015 – Sept.2018
Dr. Heidi Kreibich, Helmholtz Centre Potsdam GFZ German Research Centre For Geosciences
Prof. Dr. Frank Scherbaum, University of Potsdam
This project will analyse past changes in exposure and susceptibility of small to medium companies to flooding (detection of change) and identify the main drivers of change (attribution). The innovative aspect is that susceptibility will be analysed in addition to exposure. The project aims to develop and apply data mining techniques such as decision trees or Bayesian networks to assess and quantify the non-stationarity of flood risk from changes in technical vulnerability. It should thus contribute to the over-arching goal of identifying processes associated with transient flood risk. Contributions to the detection and attribution of flood risk changes are expected. This applies to economic and technological, and therefore vulnerability changes of companies, but corresponding climate-induced changes in flood frequency may play a role as well.
Objectives and Methods
Flood vulnerability data will be analysed with multivariate statistical methods including data-mining (e. g. decision tree algorithm, Bayesian networks). For instance, decision trees or complex networks may be used to analyse the complex interactions of factors that influence the motivation of companies to undertake precautionary measures or prepare for a flood event. If long enough time series data are available, also methods of time series analysis (e.g. decomposition) will be applied to analyse the predictability and trend of factors (e.g. economic indicators) that change the vulnerability of companies.
Tobias Sieg is based at the Hydrology research group of the German Research Centre for Geosciences GFZ.