Search results for "Mean square"

showing 4 items of 274 documents

Global Estimation of Biophysical Variables from Google Earth Engine Platform

2018

This paper proposes a processing chain for the derivation of global Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fraction Vegetation Cover (FVC), and Canopy water content (CWC) maps from 15-years of MODIS data exploiting the capabilities of the Google Earth Engine (GEE) cloud platform. The retrieval chain is based on a hybrid method inverting the PROSAIL radiative transfer model (RTM) with Random forests (RF) regression. A major feature of this work is the implementation of a retrieval chain exploiting the GEE capabilities using global and climate data records (CDR) of both MODIS surface reflectance and LAI/FAPAR datasets allowing the global estim…

random forestsCWC010504 meteorology & atmospheric sciencesMean squared errorScience0211 other engineering and technologiesGoogle Earth Engine; LAI; FVC; FAPAR; CWC; plant traits; random forests; PROSAIL02 engineering and technologyLand cover01 natural sciencesAtmospheric radiative transfer codesRange (statistics)Parametrization (atmospheric modeling)FAPARLeaf area index021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingPROSAILQ15. Life on landFVCLAIRandom forestplant traits13. Climate actionPhotosynthetically active radiationGeneral Earth and Planetary SciencesEnvironmental scienceGoogle Earth EngineRemote Sensing; Volume 10; Issue 8; Pages: 1167
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ESTIMATING SOIL PARTICLE-SIZE DISTRIBUTION FOR SICILIAN SOILS

2009

The soil particle-size distribution (PSD) is commonly used for soil classification and for estimating soil behavior. An accurate mathematical representation of the PSD is required to estimate soil hydraulic properties and to compare texture measurements from different classification systems. The objective of this study was to evaluate the ability of the Haverkamp and Parlange (HP) and Fredlund et al. (F) PSD models to fit 243 measured PSDs from a wide range of 38 005_Bagarello(547)_33 18-11-2009 11:55 Pagina 38 soil textures in Sicily and to test the effect of the number of measured particle diameters on the fitting of the theoretical PSD. For each soil textural class, the best fitting perf…

sicilian soilsParticle-size distributionMean squared errorSoil textureMechanical Engineeringlcsh:SBioengineeringSoil classificationSoil sciencelcsh:S1-972Industrial and Manufacturing EngineeringSoil gradationlcsh:AgricultureLoamParticle-size distributionSoil waterRange (statistics)Settore AGR/08 - Idraulica Agraria E Sistemazioni Idraulico-ForestaliParticle-size distribution Particle-size distribution models Soil physical properties.lcsh:Agriculture (General)MathematicsJournal of Agricultural Engineering
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Supercooled Water Confined in a Silica Xerogel: Temperature and Pressure Dependence of Boson Peak and of Mean Square Displacements

2013

A silica xerogel can be obtained from an alcoxide precursor (TMOS, tetramethylortosilcate) via the sol-gel method: TMOS hydrolysis and subsequent polycondensation yields a solid, disordered, porous SiO2 matrix (average pore dimensions ~20Å). Inside the pores water is trapped and the hydration level h=gr[H2O]/gr[SiO2] can be easily controlled. The presence and temperature dependence of the boson peak (BP) in xerogel confined supercooled water was studied with inelastic neutron scattering (spectrometer IN6 at ILL, Grenoble) in xerogel samples having h=0.4 and h=0.2. After careful subtraction of the contributions arising from the matrix and from quasi-elastic scattering, the BP contribution wa…

silica xerogel boson peak inelastic neutron scattering excess density of states LDL->HDL transition mean square displacements elastic neutron scattering protein dynamical transitionSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)
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DOBRO : a prediction error correcting robot under drifts

2016

We propose DOBRO, a light online learning module, which is equipped with a smart correction policy helping making decision to correct or not the given prediction depending on how likely the correction will lead to a better prediction performance. DOBRO is a standalone module requiring nothing more than a time series of prediction errors and it is flexible to be integrated into any black-box model to improve its performance under drifts. We performed evaluation in a real-world application with bus arrival time prediction problem. The obtained results show that DOBRO improved prediction performance significantly meanwhile it did not hurt the accuracy when drift does not happen.

ta113Concept driftComputer scienceMean squared prediction error02 engineering and technologyARIMAconcept drifton-line prediction error correction020204 information systems0202 electrical engineering electronic engineering information engineeringRobot020201 artificial intelligence & image processingAutoregressive integrated moving averageSimulation
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