Combining Fourier Analysis And Machine Learning To Estimate The Shallow-Ground Thermal Diffusivity In Switzerland

We propose a methodology combining physical modelling and machine learning (ML) to estimate the apparent ground thermal diffusivity at the scale of a country. Based on ground temperature time series at different depths, we estimate the diffusivity at 49 Swiss stations using Fourier analysis. Using a geology database, the diffusivity estimations are cross-validated with typical values for common rocks. Random Forests, an ML algorithm, are used to train a model using the previous diffusivity estimations as output values and multiple geological, elevation and temperature features. The model, showing a testing error of 16.5%, is then used to perform the estimation of apparent diffusivity everywhere in Switzerland.


Published in:
Igarss 2018 - 2018 Ieee International Geoscience And Remote Sensing Symposium, 1144-1147
Presented at:
38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, SPAIN, Jul 22-27, 2018
Year:
Jan 01 2018
Publisher:
New York, IEEE
ISSN:
2153-6996
ISBN:
978-1-5386-7150-4
Keywords:




 Record created 2019-01-03, last modified 2019-12-05


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