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Uncover Similarities of Groundwater Dynamics with Machine Learning based Hydrograph Clustering (Oral Talk IN43A-07)

Wunsch, Andreas; Liesch, Tanja; Broda, Stefan

Abstract (englisch):
Understanding and characterizing groundwater system properties is of great importance to develop sustainable groundwater management strategies. For this purpose, groundwater hydrographs are a valuable source of knowledge, since they contain information about system properties (e.g. aquifer type), artificial (e.g. withdrawal/infiltration) and natural environmental factors (e.g. groundwater-streamflow interaction). Such factors interact and superimpose temporally and spatially, which makes determining the individual contributions a challenging task. However, understanding spatial dynamics patterns is a precious source of information for this purpose. Generally, in many regions, large amounts of groundwater data with high resolution in time and space are available but lack an adequate set of tools for analysis. Data driven models are possibly suited to fill in this gap. We developed a machine learning based ensemble-modelling approach to characterize and cluster groundwater hydrographs on regional scale according to their dynamics. We apply feature-based clustering to reduce data quality requirements and to improve exploitation of heterogeneous datasets. ... mehr

Zugehörige Institution(en) am KIT Institut für Angewandte Geowissenschaften (AGW)
Publikationstyp Vortrag
Publikationsdatum 12.12.2019
Sprache Englisch
Identifikator KITopen-ID: 1000104549
Veranstaltung Fall Meeting (AGU 100 2019), San Francisco, CA, USA, 09.12.2019 – 13.12.2019
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