期刊
IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM
卷 -, 期 -, 页码 1144-1147出版社
IEEE
关键词
Ground Thermal diffusivity; Ground temperature; Fourier analysis; Random Forests
资金
- CTI (Commission for Technology and Innovation) within SCCER FEEBD [CTI. 2014.0119]
- Swiss National Science Foundation [P300P2 174514]
- Swiss National Science Foundation (SNF) [P300P2_174514] Funding Source: Swiss National Science Foundation (SNF)
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.
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