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| Registros recuperados: 12 | |
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| Picart, Stephane Saux; Tandeo, Pierre; Autret, Emmanuelle; Gausset, Blandine. |
| Machine learning techniques are attractive tools to establish statistical models with a high degree of non linearity. They require a large amount of data to be trained and are therefore particularly suited to analysing remote sensing data. This work is an attempt at using advanced statistical methods of machine learning to predict the bias between Sea Surface Temperature (SST) derived from infrared remote sensing and ground “truth” from drifting buoy measurements. A large dataset of collocation between satellite SST and in situ SST is explored. Four regression models are used: Simple multi-linear regression, Least Square Shrinkage and Selection Operator (LASSO), Generalised Additive Model (GAM) and random forest. In the case of geostationary satellites for... |
| Tipo: Text |
Palavras-chave: Machine learning; Systematic error; Sea surface temperature; Random forest. |
| Ano: 2018 |
URL: https://archimer.ifremer.fr/doc/00426/53797/54721.pdf |
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| Cardinal, Mireille; Chaussy, Marianne; Donnay-moreno, Claire; Cornet, Josiane; Rannou, Cecile; Fillonneau, Catherine; Prost, Carole; Baron, Regis; Courcoux, Philippe. |
| To use salmon protein hydrolysates as food ingredients and to mask the fish odor, Maillard reactions were associated with enzymatic production of hydrolysates. The study explored an original approach based on regression trees (RT) and random forest (RF) methodologies to predict hydrolysate odor profiles from volatile compounds. An experimental design with four factors: enzyme/substrate ratio, quantity of xylose, hydrolysis and cooking times was used to create a range of enzymatic hydrolysates. Twenty samples were submitted to a trained panel for sensory descriptions of odor. Hydrolysate volatile compounds were extracted by means of Headspace Solid Phase MicroExtraction (HS-SPME) and analyzed using gas chromatography/mass spectrometry (GC-MS). The results... |
| Tipo: Text |
Palavras-chave: Sensory characteristics; Volatile compounds; HS-SPME/GC-MS; Regression tree; Random forest; Hydrolysate; Maillard reactions. |
| Ano: 2020 |
URL: https://archimer.ifremer.fr/doc/00624/73590/73024.pdf |
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| Kopf, R. Keller; Yen, Jian D. L.; Nimmo, Dale G.; Brosse, Sébastien; Villeger, Sébastien; Tittensor, Derek. |
| Aim The aim of this study was test whether maximum body mass and jaw length are reliable predictors of trophic position (TP) in fishes, and to compare linear and nonlinear machine‐learning (ML) models incorporating biogeography, habitat and other morphological traits. Location Global. Time period Modern. Major taxa studied Fishes. Methods We compiled a global database of TP (2.0–4.5), maximum body mass, jaw length, order, ecoregion, habitat and other morphological traits of freshwater, estuarine and diadromous fishes (n = 1,991). We used Bayesian linear mixed effects and ML, with r2 analogues and 10‐fold cross‐validation, to explain and predict TP. Results Random forest models outperformed Bayesian models in all comparisons. Jaw length was the most... |
| Tipo: Text |
Palavras-chave: Allometric trophic network models; Allometry; Body mass; Gape limitation; Machine learning; Predator– Prey; Random forest; Trophic network theory. |
| Ano: 2021 |
URL: https://archimer.ifremer.fr/doc/00661/77349/78823.pdf |
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| Goydaragh,Maryam Ghebleh; Jafarzadeh,Ali Asghar; Shahbazi,Farzin; Oustan,Shahin; Taghizadeh-Mehrjardi,Ruhollah; Lado,Marcos. |
| ABSTRACT Characterizing the elemental composition provides useful information about the weathering degree of soils. In Miandoab County, Northern Iran, this characterization was missing, and thus the objectives of this work were to evaluate the weathering degrees for the most typical soils in the area from their elemental compositions, and to estimate this elemental composition using Fourier transform infrared spectroscopy and Random Forest models. Five soil profiles, including Aridisols and Inceptisols, were selected as the most representative of the area. Major elemental oxides were determined in each genetic horizon by X-ray fluorescence, showing that these soils were at early developmental stages. Only Al2O3 and CaO were accurately estimated, with R2... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: FTIR spectra; Random forest; Weathering. |
| Ano: 2019 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662019000600460 |
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| Oldoni,Lucas V.; Cattani,Carlos E. V.; Mercante,Erivelto; Johann,Jerry A.; Antunes,João F. G.; Almeida,Luiz. |
| ABSTRACT In the state of Paraná, Brazil, there are no major changes in areas cultivated with annual crops, mainly due to environmental laws that do not allow expansions to new areas. There is a great contribution of the annual crops to the domestic demand of food and economic demand in the exports. Thus, the area and distribution of annual crops are information of great importance. New methodologies, such as data mining, are being tested with the objective of analyzing and improving their potential use for classification of land use and land cover. This study used the classifiers decision tree and random forest with Normalized Difference Vegetation Index (NDVI) temporal metrics on images from Operational Land Imager (OLI)/Landsat-8. The results were... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Decision tree; Random forest; NDVI temporal metrics. |
| Ano: 2019 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662019001200952 |
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| Valadares,Alan Pessoa; Coelho,Ricardo Marques; Oliveira,Stanley Robson de Medeiros. |
| ABSTRACT: Data Mining techniques play an important role in the prediction of soil spatial distribution in systematic soil surveying, though existing methodologies still lack standardization and a full understanding of their capabilities. The aim of this work was to evaluate the performance of preprocessing procedures and supervised classification approaches for predicting map units from 1:100,000-scale conventional semi-detailed soil surveys. Sheets of the Brazilian National Cartographic System on the 1:50,000 scale, “Dois Córregos” (“Brotas” 1:100,000-scale sheet), “São Pedro” and “Laras” (“Piracicaba” 1:100,000-scale sheet) were used for developing models. Soil map information and predictive environmental covariates for the dataset were obtained from the... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Machine learning algorithms; Random forest; Tacit soil-landscape relationships; Digital soil mapping. |
| Ano: 2019 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162019001500439 |
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| Silva,Sérgio Henrique Godinho; Silva,Elen Alvarenga; Poggere,Giovana Clarice; Pádua Junior,Alceu Linares; Gonçalves,Mariana Gabriele Marcolino; Guilherme,Luiz Roberto Guimarães; Curi,Nilton. |
| ABSTRACT Sulfuric acid digestion analyses (SAD) provide useful information to environmental studies, in terms of the geochemical balance of nutrients, parent material uniformity, nutrient reserves for perennial crops, and mineralogical composition of the soil clay fraction. Yet, these analyses are costly, time consuming, and generate chemical waste. This work aimed at predicting SAD results from portable X-ray fluorescence (pXRF) spectrometry, which is proposed as a “green chemistry” alternative to the current SAD method. Soil samples developed from different parent materials were analyzed for soil texture and SAD, and scanned with pXRF. The SAD results were predicted from pXRF elemental analyses through simple linear regressions, stepwise multiple linear... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Soil clay fraction; Weathering indices; Random forest; Proximal sensors; Green chemistry. |
| Ano: 2020 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162020000401402 |
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| SCHIKOWSKI,ANA B.; CORTE,ANA P.D.; RUZA,MARIELI S.; SANQUETTA,CARLOS R.; MONTAÑO,RAZER A.N.R.. |
| Abstract Taper functions and volume equations are essential for estimation of the individual volume, which have consolidated theory. On the other hand, mathematical innovation is dynamic, and may improve the forestry modeling. The objective was analyzing the accuracy of machine learning (ML) techniques in relation to a volumetric model and a taper function for acácia negra. We used cubing data, and fit equations with Schumacher and Hall volumetric model and with Hradetzky taper function, compared to the algorithms: k nearest neighbor (k-NN), Random Forest (RF) and Artificial Neural Networks (ANN) for estimation of total volume and diameter to the relative height. Models were ranked according to error statistics, as well as their dispersion was verified.... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Artificial intelligence; Data mining; Random forest; ANN. |
| Ano: 2018 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652018000703389 |
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| TEIXEIRA,ANITA F.S.; SILVA,JACQUELINE S.; VILELA,LAÍZE A.F.; COSTA,PATRÍCIA F.; COSTA,ELAINE M. DA; GUIMARÃES,AMANDA A.; SANTOS,JESSÉ V. DOS; SILVA,SÉRGIO H.G.; CARNEIRO,MARCO AURÉLIO C.; MOREIRA,FATIMA M.S.. |
| Abstract: Several microbiological indicators of soil quality present high sensitivity, but little is known about the influence of topographic factors on them. This work aimed to evaluate variability of biological indicators of soil quality across a hillslope under native forest and the influence of topographic factors on them. Four positions on a hillslope were evaluated. Activity of the enzymes β-glucosidase, acid phosphatase, urease and fluorescein diacetate (FDA) hydrolysis were determined, as well as basal and substrate-induced respiration, and density of microorganisms: total bacteria, total fungi, actinobacteria, phosphate solubilizers, ammonifiers, native rhizobia, free-living N2-fixing bacteria, spores of arbuscular mycorrhizal fungi and percentage... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Biological indicators; Digital elevation model; Enzymes; Microbial activity; Random forest. |
| Ano: 2019 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652019000700866 |
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| Registros recuperados: 12 | |
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