6533b86ffe1ef96bd12cd459
RESEARCH PRODUCT
Retrieval of coloured dissolved organic matter with machine learning methods
Kari KallioGustau Camps-vallsAna B. RuescasSampsa KoponenMartin Hieronymisubject
FOS: Computer and information sciencesComputer Science - Machine Learning010504 meteorology & atmospheric sciences0211 other engineering and technologiesFOS: Physical sciences02 engineering and technologyMachine learningcomputer.software_genre01 natural sciencesMachine Learning (cs.LG)Physics - GeophysicsKrigingDissolved organic carbonLinear regression021101 geological & geomatics engineering0105 earth and related environmental sciencesMathematicsPolynomial regressionbusiness.industry6. Clean waterGeophysics (physics.geo-ph)Random forestNonlinear systemColored dissolved organic matterKernel (statistics)Artificial intelligencebusinesscomputerdescription
The coloured dissolved organic matter (CDOM) concentration is the standard measure of humic substance in natural waters. CDOM measurements by remote sensing is calculated using the absorption coefficient (a) at a certain wavelength (e.g. 440nm). This paper presents a comparison of four machine learning methods for the retrieval of CDOM from remote sensing signals: regularized linear regression (RLR), random forest (RF), kernel ridge regression (KRR) and Gaussian process regression (GPR). Results are compared with the established polynomial regression algorithms. RLR is revealed as the simplest and most efficient method, followed closely by its nonlinear counterpart KRR.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-07-01 |