© 2026 SOME RIGHTS RESERVED
The content of this web site is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 2.0 Germany License.
(2020) DOI:10.25932/publishup-55983
Over the past decades, there has been a growing interest in “extreme events” owing to the increasing threats that climate-related extremes such as floods, heatwaves, droughts, etc., pose to society. While extreme events have diverse definitions across various disciplines, ranging from earth science to neuroscience, they are characterized mainly as dynamic occurrences within a limited time frame that impede the normal functioning of a system. Although extreme events are rare in occurrence, it has been found in various hydro-meteorological and physiological time series (e.g., river flows, temperatures, heartbeat intervals) that they may exhibit recurrent behavior, i.e., they do not end the lifetime of the system. The aim of this thesis is to develop sophisticated methods to study various properties of extreme events.
One of the main challenges in analyzing such extreme event-like time series is that they have large temporal gaps due to the paucity of observations. As a result, existing time series analysis tools are usually not helpful to decode the underlying information. I use the edit distance (ED) method to analyze extreme event-like time series in their unaltered form. ED is a specific distance metric designed to measure the similarity/dissimilarity between point-process-like data. I combine ED with recurrence plot techniques to identify the recurrence property of flood events in the Mississippi River in the United States. I also use recurrence quantification analysis to show deterministic properties and serial dependency in flood events.
After that, I use this nonlinear similarity measure (ED) to compute the pairwise dependency in extreme precipitation event series. I incorporate the similarity measure within the framework of complex network theory to study the collective behavior of climate extremes. Under this architecture, the nodes are defined by the spatial grid points of the given spatio-temporal climate dataset. Each node is associated with a time series corresponding to the temporal evolution of the climate observation at that grid point. The network links are functions of the pairwise statistical interdependence between the nodes. Various network measures, such as degree, betweenness centrality, clustering coefficient, etc., can be used to quantify the network’s topology. We apply this methodology to study the spatio-temporal coherence pattern of extreme rainfall events in the United States and the Ganga River basin, revealing its relation to various climate processes and the orography of the region.
The identification of precursors associated with the occurrence of extreme events in the near future is extremely important to prepare the public for an upcoming disaster and mitigate potential risks. Motivated by this goal, I propose an in-data prediction framework for predicting the data structures that typically occur prior to extreme events using the Echo State Network, a type of Recurrent Neural Network that is part of the reservoir computing framework. However, unlike previous studies that identify precursory structures in the same variable in which extreme events are manifested (active variable), I try to predict these structures using data from another dynamic variable (passive variable) which does not show large excursions from nominal conditions but still carries imprints of these extreme events. Furthermore, my results demonstrate that the quality of prediction depends on the magnitude of events: the higher the magnitude of the extreme, the better the predictability. I show quantitatively that this is because the input signals collectively form a more coherent pattern for extreme events of higher magnitude, which enhances the efficiency of the machine in predicting forthcoming extremes.
© 2026 SOME RIGHTS RESERVED
The content of this web site is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 2.0 Germany License.
Please note: The abstracts of the bibliography database may underly other copyrights.
Ihr Browser versucht gerade eine Seite aus dem sogenannten Internet auszudrucken. Das Internet ist ein weltweites Netzwerk von Computern, das den Menschen ganz neue Mglichkeiten der Kommunikation bietet.
Da Politiker im Regelfall von neuen Dingen nichts verstehen, halten wir es fr notwendig, sie davor zu schtzen. Dies ist im beidseitigen Interesse, da unntige Angstzustnde bei Ihnen verhindert werden, ebenso wie es uns vor profilierungs- und machtschtigen Politikern schtzt.
Sollten Sie der Meinung sein, dass Sie diese Internetseite dennoch sehen sollten, so knnen Sie jederzeit durch normalen Gebrauch eines Internetbrowsers darauf zugreifen. Dazu sind aber minimale Computerkenntnisse erforderlich. Sollten Sie diese nicht haben, vergessen Sie einfach dieses Internet und lassen uns in Ruhe.
Die Umgehung dieser Ausdrucksperre ist nach 95a UrhG verboten.
Mehr Informationen unter www.politiker-stopp.de.