Dynamic Data Citation Service-Subset Tool for Operational Data Management

In earth observation and climatological sciences, data and their data services grow on a daily basis in a large spatial extent due to the high coverage rate of satellite sensors, model calculations, but also by continuous meteorological in situ observations. In order to reuse such data, especially data fragments as well as their data services in a collaborative and reproducible manner by citing the origin source, data analysts, e.g., researchers or impact modelers, need a possibility to identify the exact version, precise time information, parameter, and names of the dataset used. A manual process would make the citation of data fragments as a subset of an entire dataset rather complex and imprecise to obtain. Data in climate research are in most cases multidimensional, structured grid data that can change partially over time. The citation of such evolving content requires the approach of "dynamic data citation". The applied approach is based on associating queries with persistent identifiers. These queries contain the subsetting parameters, e.g., the spatial coordinates of the desired study area or the time frame with a start and end date, which are automatically included in the metadata of the newly generated subset and thus represent the information about the data history, the data provenance, which has to be established in data repository ecosystems. The Research Data Alliance Data Citation Working Group (RDA Data Citation WG) summarized the scientific status quo as well as the state of the art from existing citation and data management concepts and developed the scalable dynamic data citation methodology of evolving data. The Data Centre at the Climate Change Centre Austria (CCCA) has implemented the given recommendations and offers since 2017 an operational service on dynamic data citation on climate scenario data. With the consciousness that the objective of this topic brings a lot of dependencies on bibliographic citation research which is still under discussion, the CCCA service on Dynamic Data Citation focused on the climate domain specific issues, like characteristics of data, formats, software environment, and usage behavior. The current effort beyond spreading made experiences will be the scalability of the implementation, e.g., towards the potential of an Open Data Cube solution

Application of machine learning to weather-triggered hazards and damages in alpine territory

Die Anwendung von entscheidungsbaumbasierten Machine Learning Methoden im Umfeld der Extremereignisvorhersage ist herausfordernd. Ungleich verteilte binäre Klassifikationsaufgaben bedingen einer passenden Gegenmaßnahme um die Vorhersagequalität des Modells in Bezug auf die Minderheitsklasse (Extremereignis) zu verbessern. Wir evaluieren das Potential unterschiedlicher Techniken [Branco, Torgo und Ribeiro, 2015] wie pre-processing, special purpose learning methods und post-processing durch ihre Anwendung auf random forests und gradient boosted trees. K-nearest neighbor Schwierigkeitsanalysen und unterschiedliche Validierungsmetriken wurden für zehn synthetische Datensätze ausgewertet und liefern die besten Ergebnisse für special purpose learning Methoden. Bezugnehmend auf Enigl u. a., 2019 analysieren wir die jährlichen Summen von Extremereignissen für sieben Kategorien und erweitern den Schadensdatensatz mit ’zeitlich unabhängigen’ Prädiktoren aus Terrain-, Vegetations-, Boden- und Geologiedaten. Der gesamte Datensatz wird auf Rutschungsereignisse reduziert und eine Schwierigkeitsanalyse für unterschiedliche geologische Domainen wird durchgeführt. Die Analyse zeigt den hohen Grad des Ungleichgewichtes und der Schwierigkeit welche, bei vergleichbaren synthetische Datensätzen, die Grenze valider Auswertemöglichkeiten darstellen. Ein umfangreicher Suszeptibilitätsmodellierungsversuch für die Ereigniskategorie Hangrutschung wird mit skalierten random forest (SXRF) und skalierten gradient boosted tree (SXGBT) Modellen aus dem XGBoost Framework [T. Chen und Guestrin, 2016] durchgeführt. Hierbei bezeichnet der Term ’skaliert’ die balancierte Gewichtung der Minoritäts- und Majoritätsklasse in der Kostenfunktion unter Berücksichtigung ihres globalen Ungleichgewichtes. SXRF liefert schlechtere Ergebnisse als SXGBT. Die 5-fach Kreuzvalidierung von SXGBT mit konsistente Sensitivitäten von ∼ 0.75 und den Flächen unter den Grenzwertoptimierungskurven von ∼ 0.8 deuten auf die Robustheit des Modells und der gewählten Prädiktoren hin. Die wichtigsten numerischen Prädiktoren sind ’Hangneigung’ (unter Berücksichtigung korrelierter Prädiktoren), ’Distanz zur nächstgelegenen Straße’, ’Distanz zur nächstgelegenen geologischen Grenzschicht’ und der Oberflächenparameter ’Bodendichte’. Die wichtigsten binären one-hot kodierten kategorischen Prädiktoren sind die Landbedeckungsklasse ’Wald’ und die geologischen Domänen ’Austroalpine Einheit’ und ’Siliciklastika’.The application of tree-based machine learning methods in the field of hazard event prediction is challenging. The imbalanced binary classification task requires suitable countermeasures in order to enhance the models predictability of the minority class (hazard event). We estimate the potential of different techniques [Branco, Torgo, and Ribeiro, 2015], such as pre-processing, special purpose learning methods and post-processing, by applying them on random forests and gradient boosted trees under well known synthetic conditions. The special purpose learning methods outperform the pre- and post-processing approaches, whereto k-nearest neighbor difficulty analyses and various performance metrics for ten synthetic data sets are evaluated. Following up on Enigl et al., 2019 we analyse the yearly sum of hazard events for seven categories and expanded the Austrian hazard event space by ”time independent” features derived from terrain, soil, vegetation and geological data. The data is further filtered for slide events on which we perform a difficulty analysis for different geological domains. Thus, the analysis reveals the degree of imbalance and difficulty, at which comparable synthetic data sets tend to be in limbo of viability. Nevertheless, an extensive modeling approach for the hazard category slide is performed using scaled random forests (SXRF) and scaled gradient boosted trees (SXGBT), both implemented in the XGBoost framework [T. Chen and Guestrin, 2016]. Whereat, the term ”scaled” refers to the fact that weights are balanced in the cost function for minority and majority instances in relation to their global imbalance ratio. SXRF is outperformed by SXGBT and 5-fold cross validation scores indicate the robustness of the model with consistent sensitivity scores of approximately 0.75 and areas under the receiver operator curve of approximately 0.80. The most important numeric features are ”slope” (considering correlated features), ”minimum distance to street”, ”minimum distance to nappe boundary” and the ”topsoil physical property bulk density”. The most important binary one-hot encoded categorical features are the landcover class ”Woody” and the geological domains ”Austroalpine Units” and ”Siliclastic Rocks”. Non-linear tree-based machine learning methods may further improve data-driven models for susceptibility mapping of spatially non-persistent hazard events. Nevertheless, they depend heavily on the quality of the underlying feature and hazard event data