Data-Driven Induction of Shadowed Sets Based on Grade of Fuzziness

We propose a procedure devoted to the induction of a shadowed set through the post-processing of a fuzzy set, which in turn is learned from labeled data. More precisely, the fuzzy set is inferred using a modified support vector clustering algorithm, enriched in order to optimize the fuzziness grade. Finally, the fuzzy set is transformed into a shadowed set through application of an optimal alpha-cut. The procedure is tested on synthetic and real-world datasets

Flood mitigation, model uncertainty and process diagnostics - Bridging the gap between operational practice and research

This dissertation is about flood mitigation, model uncertainty and process diagnostics. I develop and apply methods relevant for reservoir operation and flood forecasting, I introduce pattern matching procedures for streamflow time series and I analyze a comprehensive environmental dataset with regard to catchment runoff production

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas