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Adaptive clustering: reducing the computational costs of distributed (hydrological) modelling by exploiting time-variable similarity among model elements

Ehret, Uwe; van Pruijssen, Rik; Bortoli, Marina; Loritz, Ralf; Azmi, Elnaz; Zehe, Erwin

Abstract:
In this paper we propose adaptive clustering as a new method for reducing the computational efforts of distributed modelling. It consists of identifying similar-acting model elements during runtime, clustering them, running the model for just a few representatives per cluster, and mapping their results to the remaining model elements in the cluster. Key requirements for the application of adaptive clustering are the existence of (i) many model elements with (ii) comparable structural and functional properties and (iii) only weak interaction (e.g. hill slopes, subcatchments, or surface grid elements in hydrological and land surface models). The clustering of model elements must not only consider their time-invariant structural and functional properties but also their current state and forcing, as all these aspects influence their current functioning. Joining model elements into clusters is therefore a continuous task during model execution rather than a one-time exercise that can be done beforehand. Adaptive clustering takes this into account by continuously checking the clustering and re-clustering when necessary.
We explain the steps of adaptive clustering and provide a proof of concept at the example of a distributed, conceptual hydrological model fit to the Attert basin in Luxembourg. ... mehr

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Verlagsausgabe §
DOI: 10.5445/IR/1000124809
Veröffentlicht am 19.10.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Wasser und Gewässerentwicklung (IWG)
Steinbuch Centre for Computing (SCC)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 1607-7938
KITopen-ID: 1000124809
Erschienen in Hydrology and earth system sciences
Band 24
Heft 9
Seiten 4389–4411
Vorab online veröffentlicht am 09.09.2020
Nachgewiesen in Web of Science
Scopus
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