Search results for " Random Forest"

showing 3 items of 13 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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Application of selected methods of black box for modelling the settleability process in wastewater treatment plant

2017

The paper described how the results of measurement s of inflow wastewater temperature in the chamber, a degree of external and internal recirculation in the biological-mechanical wastewater treatment plan t (WWTP) in Cedzyna near Kielce, Poland, were used to make predictions of settleability of activated sludge. Three methods,namely: multivariate adaptive regression splines (MARS), random forests (RF) and modified random forests (RF+ SOM) were employed to compute activated sludge settleability. The results of analysis indicate that modified random forests demonstrate the best predictive abilities.

random forestsmodified random forestssludge settleabilitymultivariate adaptive regression splinesEcological Chemistry and Engineering S-Chemia I Inzynieria Ekologiczna S
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ENSEMBLE METHODS FOR RANKING DATA

2017

The last years have seen a remarkable flowering of works about the use of decision trees for ranking data. As a matter of fact, decision trees are useful and intuitive, but they are very unstable: small perturbations bring big changes. This is the reason why it could be necessary to use more stable procedures, as ensemble methods, in order to find which predictors are able to explain the preference structure. In this work ensemble methods as BAGGING and Random Forest are proposed, from both a theoretical and computational point of view, for deriving classification trees when ranking data are observed. The advantages of these procedures are shown through an example on the SUSHI data set.

ranking data ensemble methods bagging random forestSettore SECS-S/01 - Statistica
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