Advanced geostatistics, clustering and classification for water, earth and environmental sciences
Co-sponsored by
IAHS-ICSH
Convener:
Svenja Fischer
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Co-conveners:
Nilay Dogulu,
Vanessa A. Godoy,
Jaime Gómez-Hernández,
Gerard Heuvelink,
Alessandra Menafoglio,
Georgia Papacharalampous
Orals
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Wed, 26 Apr, 08:30–10:15 (CEST) Room 2.15
Posters on site
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Attendance Wed, 26 Apr, 10:45–12:30 (CEST) Hall A
Posters virtual
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Attendance Wed, 26 Apr, 10:45–12:30 (CEST) vHall HS
Clustering and classification algorithms are increasingly and extensively applied in hydrology as the need for pattern recognition and data mining tasks persists with higher availability of large multivariate datasets. While both approaches share the goal of dividing data into convenient groups, classification approaches pre-define such groups (i.e. supervised learning) whereas clustering approaches group data with similar properties without preconceived notions about which groups are expected to be in the data (i.e. unsupervised learning).
Geostatistical methods are commonly applied in the water, earth and environmental sciences to quantify spatial variation, produce interpolated maps with quantified uncertainty and optimize spatial sampling designs. Space-time geostatistics explores the dynamic aspects of environmental processes and characterise the dynamic variation in terms of correlations. Geostatistics can also be combined with machine learning and mechanistic models to improve the modelling of real-world processes and patterns. Such methods are used to model soil properties, produce climate model outputs, simulate hydrological processes, and to better understand and predict uncertainties overall.
Topics covered in this session are:
1) How can clustering/classification approaches increase our understanding and improve our prediction of hydrological processes?
2) To what extent should clustering/classification algorithm settings be finetuned for hydrological applications?
3) How can geostatistical approaches be used for the characterization of uncertainties and error propagation?
4) How can spatial and temporal aspects be combined in geostatistics and how do they improve our understanding of hydrological processes?
5) What is the benefit of integrating machine-learning approaches to geostatistics?
08:30–08:35
5-minute convener introduction
Part I: Geostatistics
08:35–08:45
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EGU23-16088
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ECS
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On-site presentation
08:45–08:55
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EGU23-7160
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ECS
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On-site presentation
08:55–09:05
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EGU23-14743
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Virtual presentation
09:15–09:25
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EGU23-5541
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On-site presentation
Part II: Clutering and Classification
09:45–09:55
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EGU23-9114
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On-site presentation
09:55–10:05
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EGU23-1049
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ECS
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On-site presentation
vHS.14
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EGU23-16194