Semi-Supervised Learning for Grain Size Distribution Interpolation

@inproceedings{Kobs2020SemiSupervisedLF,
  title={Semi-Supervised Learning for Grain Size Distribution Interpolation},
  author={Konstantin Kobs and Christian Sch{\"a}fer and Michael Steininger and Anna Krause and Roland Baumhauer and Heiko Paeth and Andreas Hotho},
  booktitle={ICPR Workshops},
  year={2020}
}
High-resolution grain size distribution maps for geographical regions are used to model soil-hydrological processes that can be used in climate models. However, measurements are expensive or impossible, which is why interpolation methods are used to fill the gaps between known samples. Common interpolation methods can handle such tasks with few data points since they make strong modeling assumptions regarding soil properties and environmental factors. Neural networks potentially achieve better… 
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References

SHOWING 1-10 OF 34 REFERENCES

Spatial interpolation methods applied in the environmental sciences: A review

A Practical Guide to Geostatistical Mapping

This book will first introduce the basic principles of geostatistical mapping and regression-kriging, as the key prediction technique, then guide you through software tools — R+gstat/geoR, SAGA GIS and Google Earth — which will be used to prepare the data, run analysis and make final layouts.

A two-dimensional interpolation function for irregularly-spaced data

  • D. Shepard
  • Environmental Science
    ACM National Conference
  • 1968
In many fields using empirical areal data there arises a need for interpolating from irregularly-spaced data to produce a continuous surface, and it is extremely useful, if not essential, to define a continuous function fitting the given values exactly.

Feed forward neural network and interpolation function models to predict the soil and subsurface sediments distribution in Bam, Iran

An application of the artificial neural network (ANN) approach for predicting mean grain size using electric resistivity data from Bam city is presented. A feed forward back propagation network was

A Central European precipitation climatology - Part I: Generation and validation of a high-resolution gridded daily data set (HYRAS)

A new precipitation climatology (DWD/BfG-HYRAS-PRE) is presented which covers the river basins in Germany and neighbouring countries. In order to satisfy hydrological requirements, the gridded

Climate change impact assessment under data scarcity

According to current climate projections, Mediterranean countries are at high risk for an even pronounced susceptibility to changes in the hydrological budget and extremes. These changes are expected

Neural Networks: Tricks of the Trade

It is shown how nonlinear semi-supervised embedding algorithms popular for use with â œshallowâ learning techniques such as kernel methods can be easily applied to deep multi-layer architectures.

Random Forests

Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.