Advanced geostatistics for water, earth and environmental sciences
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. Big data analysis and data fusion have become major topics of research due to technological advances and the abundance of new data sources from remote and proximal sensing as well as a multitude of environmental sensor networks. Methodological advances, such as hierarchical Bayesian modeling, machine learning, sparse Gaussian processes, local interaction models, as well as advances in geostatistical software modules in R and Python have enhanced the geostatistical toolbox.
This session aims to provide a forum where scientists from different disciplines can present and discuss innovative geostatistical methods targeting important problems in the Water, Earth and Environmental sciences. We also encourage contributions focusing on real-world applications of state-of-the-art geostatistical methods.
The topics of interest include:
1) Space-time geostatistics for hydrology, soil, climate system observations and modelling
2) Hybrid methods: Integration of geostatistics with optimization and machine learning approaches
3) Advanced parametric and non-parametric spatial estimation and prediction techniques
4) Big spatial data: analysis and visualization
5) Optimisation of spatial sampling frameworks and space-time monitoring designs
6) Algorithms and applications on Earth Observation Systems
7) Data Fusion, mining and information analysis
8) Application of covariance functions and copulas for the identification of spatio-temporal relationships
9) Geostatistical characterization of uncertainties and error propagation
10) Bayesian geostatistical analysis and hierarchical modelling
11) Functional data analysis approaches to geostatistics
12) Multiple point geostatistics
This session is co-sponsored by the International Association for Mathematical Geosciences (IAMG), https://www.iamg.org/
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. Big data analysis and data fusion have become major topics of research due to technological advances and the abundance of new data sources from remote and proximal sensing as well as a multitude of environmental sensor networks. Methodological advances, such as hierarchical Bayesian modeling, machine learning, sparse Gaussian processes, local interaction models, as well as advances in geostatistical software modules in R and Python have enhanced the geostatistical toolbox.
This session aims to provide a forum where scientists from different disciplines can present and discuss innovative geostatistical methods targeting important problems in the Water, Earth and Environmental sciences. We also encourage contributions focusing on real-world applications of state-of-the-art geostatistical methods.
The topics of interest include:
1) Space-time geostatistics for hydrology, soil, climate system observations and modelling
2) Hybrid methods: Integration of geostatistics with optimization and machine learning approaches
3) Advanced parametric and non-parametric spatial estimation and prediction techniques
4) Big spatial data: analysis and visualization
5) Optimisation of spatial sampling frameworks and space-time monitoring designs
6) Algorithms and applications on Earth Observation Systems
7) Data Fusion, mining and information analysis
8) Application of covariance functions and copulas for the identification of spatio-temporal relationships
9) Geostatistical characterization of uncertainties and error propagation
10) Bayesian geostatistical analysis and hierarchical modelling
11) Functional data analysis approaches to geostatistics
12) Multiple point geostatistics
This session is co-sponsored by the International Association for Mathematical Geosciences (IAMG), https://www.iamg.org/
