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| Registros recuperados: 25 | |
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| Pereira,Tonismar dos S.; Robaina,Adroaldo D.; Peiter,Marcia X.; Torres,Rogerio R.; Bruning,Jhosefe. |
| ABSTRACT The aim of this study was to present and to evaluate methodologies for the estimation of soil resistance to penetration (RP) using artificial intelligence prediction techniques. In order to do so, a data base with values of physical-water characteristics of the soils available in the literature was used, and the performances of Artificial Neural Networks (ANN) and Support Vector Machines (SVM) were evaluated. The models generated from the ANNs were implemented through the multilayer perceptron with backpropagation algorithm of Matlab software, varying the number of neurons in the input and intermediate layers. For the procedure from SVM, the RapidMiner software was used, varying input variables, the kernel function and the coefficients of these... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Soil compaction; Machine learning; Support vector machines; Artificial neural networks. |
| Ano: 2018 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162018000100142 |
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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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| Rosso, Isabella; Mazloff, Matthew R.; Talley, Lynne D.; Purkey, Sarah G.; Freeman, Natalie M.; Maze, Guillaume. |
| The Southern Ocean (SO) is one of the most energetic regions in the world, where strong air‐sea fluxes, oceanic instabilities, and flow‐topography interactions yield complex dynamics. The Kerguelen Plateau (KP) region in the Indian sector of the SO is a hotspot for these energetic dynamics, which result in large spatio‐temporal variability of physical and biogeochemical (BGC) properties throughout the water column. Data from Argo floats (including biogeochemical) are used to investigate the spatial variability of intermediate and deep water physical and BGC properties. An unsupervised machine learning classification approach is used to organize the float profiles into five SO frontal zones based on their temperature and salinity structure between 300 and... |
| Tipo: Text |
Palavras-chave: Southern Ocean; Kerguelen Plateau; Argo; Unsupervised clustering; Machine learning. |
| Ano: 2020 |
URL: https://archimer.ifremer.fr/doc/00613/72471/71438.pdf |
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| Zeng, J; Tan, Zh; Matsunaga, T; Shirai, T. |
| A Support Vector Machine (SVM) for regression is a popular machine learning model that aims to solve nonlinear function approximation problems wherein explicit model equations are difficult to formulate. The performance of an SVM depends largely on the selection of its parameters. Choosing between an SVM that solves an optimization problem with inequality constrains and one that solves the least square of errors (LS-SVM) adds to the complexity. Various methods have been proposed for tuning parameters, but no article puts the SVM and LS-SVM side by side to discuss the issue using a large dataset from the real world, which could be problematic for existing parameter tuning methods. We investigated both the SVM and LS-SVM with an artificial dataset and a... |
| Tipo: Text |
Palavras-chave: Support vector machine for regression; SVM; LS-SVM; Machine learning; Parameter optimization; Global ocean CO2. |
| Ano: 2019 |
URL: https://archimer.ifremer.fr/doc/00676/78774/80949.pdf |
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| Renosh, Pannimpullath R.; Jourdin, Frederic; Charantonis, Anastase A.; Yala, Khalil; Rivier, Aurelie; Badran, Fouad; Thiria, Sylvie; Guillou, Nicolas; Leckler, Fabien; Gohin, Francis; Garlan, Thierry. |
| Hydro-sedimentary numerical models have been widely employed to derive suspended particulate matter (SPM) concentrations in coastal and estuarine waters. These hydro-sedimentary models are computationally and technically expensive in nature. Here we have used a computationally less-expensive, well-established methodology of self-organizing maps (SOMs) along with a hidden Markov model (HMM) to derive profiles of suspended particulate inorganic matter (SPIM). The concept of the proposed work is to benefit from all available data sets through the use of fusion methods and machine learning approaches that are able to process a growing amount of available data. This approach is applied to two different data sets entitled “Hidden” and “Observable”. The hidden... |
| Tipo: Text |
Palavras-chave: Suspended particulate inorganic matter; Self-organizing maps; Hidden Markov Model; Machine learning; English Channel; ROMS. |
| Ano: 2017 |
URL: http://archimer.ifremer.fr/doc/00415/52653/53511.pdf |
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| Martinez, Elodie; Gorgues, Thomas; Lengaigne, Matthieu; Fontana, Clement; Sauzède, Raphaëlle; Menkes, Christophe; Uitz, Julia; Di Lorenzo, Emanuele; Fablet, Ronan. |
| Monitoring the spatio-temporal variations of surface chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass) greatly benefited from the availability of continuous and global ocean color satellite measurements from 1997 onward. These two decades of satellite observations are however still too short to provide a comprehensive description of Chl variations at decadal to multi-decadal timescales. This paper investigates the ability of a machine learning approach (a non-linear statistical approach based on Support Vector Regression, hereafter SVR) to reconstruct global spatio-temporal Chl variations from selected surface oceanic and atmospheric physical parameters. With a limited training period (13 years), we first demonstrate that Chl variability... |
| Tipo: Text |
Palavras-chave: Machine learning; Phytoplankton variability; Satellite ocean color; Decadel variability; Global scale. |
| Ano: 2020 |
URL: https://archimer.ifremer.fr/doc/00641/75314/75810.pdf |
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| Tang, Weiyi; Cassar, Nicolas. |
| Diazotrophs play a critical role in the biogeochemical cycling of nitrogen, carbon, and other elements in the global ocean. Despite their well‐recognized role, the diversity, abundance, and distribution of diazotrophs in the world's ocean remain poorly characterized largely due to limited observations. Here we update the database of diazotroph nifH gene abundances and assess how environmental factors may regulate diazotrophs at the global scale. Our meta‐analysis more than doubles the number of observations in the previous database. Using linear and nonlinear regressions, we find that the abundances of Trichodesmium, UCYN‐A, UCYN‐B, and Richelia relate differently to temperature, light, and nutrients. We further apply a random forest algorithm to estimate... |
| Tipo: Text |
Palavras-chave: Diazotrophs; Marine nitrogen fixation; Meta-analysis; Machine learning. |
| Ano: 2019 |
URL: https://archimer.ifremer.fr/doc/00591/70322/68359.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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| Kedzierski, Mikaël; Falcou-préfol, Mathilde; Kerros, Marie Emmanuelle; Henry, Maryvonne; Pedrotti, Maria Luiza; Bruzaud, Stéphane. |
| The development of methods to automatically determine the chemical nature of microplastics by FTIR-ATR spectra is an important challenge. A machine learning method, named k-nearest neighbors classification, has been applied on spectra of microplastics collected during Tara Expedition in the Mediterranean Sea (2014). To realize these tests, a learning database composed of 969 microplastic spectra has been created. Results show that the machine learning process is very efficient to identify spectra of classical polymers such as poly(ethylene), but also that the learning database must be enhanced with less common microplastic spectra. Finally, this method has been applied on more than 4000 spectra of unidentified microplastics. The verification protocol... |
| Tipo: Text |
Palavras-chave: Microplastic; Tara mediterranean campaign; FTIR spectra; Machine learning; K-nearest neighbor classification. |
| Ano: 2019 |
URL: https://archimer.ifremer.fr/doc/00501/61247/64825.pdf |
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| Bittig, Henry C.; Steinhoff, Tobias; Claustre, Harve; Fiedler, Bjoern; Williams, Nancy L.; Sauzede, Raphaelle; Koertzinger, Arne; Gattuso, Jean-pierre. |
| This work presents two new methods to estimate oceanic alkalinity (A(T)), dissolved inorganic carbon (C-T), pH, and pCO(2) from temperature, salinity, oxygen, and geolocation data. "CANYON-B" is a Bayesian neural network mapping that accurately reproduces GLODAPv2 bottle data and the biogeochemical relations contained therein. "CONTENT" combines and refines the four carbonate system variables to be consistent with carbonate chemistry. Both methods come with a robust uncertainty estimate that incorporates information from the local conditions. They are validated against independent GO-SHIP bottle and sensor data, and compare favorably to other state-of-the-art mapping methods. As "dynamic climatologies" they show comparable performance to classical... |
| Tipo: Text |
Palavras-chave: Carbon cycle; GLODAP; Marine carbonate system; Surface pCO(2) climatology; Revelle buffer factor increase; Machine learning; Nutrient estimation. |
| Ano: 2018 |
URL: https://archimer.ifremer.fr/doc/00675/78681/80879.pdf |
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| Grosjean, Philippe; Wacquet, Guillaume. |
| This report details the work accomplished to enhance the Zoo/PhytoImage software to optimize its use for the analysis of phytoplankton samples in general, but more particularly, in the framework of an operational survey of coastal seawater (REPHY, IFREMER). Zoo/PhytoImage allows to analyze “numerically recorded” plankton samples, that is, by using digital images gathered with specialized devices such as the FlowCAM, or the FastCAM (see report 3). A machine learning approach allows to automatically classify the digitized particles into various taxonomic groups. Once this is done, global statistics are calculated on each sample, including the number of particles, the biomass, and the size spectrum per taxonomic group. Two major changes are introduced in the... |
| Tipo: Text |
Palavras-chave: Phytoplancton; REPHY; Analyse d'image; Classification supervisée; Dénombrement de cellules; Apprentissage actif; Manche; Atlantique.; Phytoplankton; REPHY; Image analysis; Machine learning; Cells enumeration; Active learning; The Channel; Atlantic Ocean. |
| Ano: 2016 |
URL: http://archimer.ifremer.fr/doc/00389/49990/50578.pdf |
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| Pilaszy, Istvan; Dobrowiecki, Tadeusz. |
| In this paper we presented a lot of experiments that examine how the particular parts of the documents do contribute to the performance of a classifier. We evaluated text classifiers on two very different text corpora. We conclude that some parts of the text are more important from the point of text classification performance. Giving higher weights to more important parts can increase the performance of the classifier. The question, that which parts are more or less important depends on the nature of the documents in the corpora. Some tasks that remains to be done: − More text corpora should be investigated. − In section 6.4 we optimized the number of features to be kept independent from the section. However, it could be optimized for each section. −... |
| Tipo: Journal Article |
Palavras-chave: Machine learning; Text categorization; Classifier ensembles; Research and Development/Tech Change/Emerging Technologies. |
| Ano: 2007 |
URL: http://purl.umn.edu/58927 |
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| Meier,Martin; Souza,Eliana de; Francelino,Marcio Rocha; Fernandes Filho,Elpídio Inácio; Schaefer,Carlos Ernesto Gonçalves Reynaud. |
| ABSTRACT: Increasingly, applications of machine learning techniques for digital soil mapping (DSM) are being used for different soil mapping purposes. Considering the variety of models available, it is important to know their performance in relation to soil data and environmental variables involved in soil mapping. This paper investigated the performance of eight machine learning algorithms for soil mapping in a tropical mountainous area of an official rural settlement in the Zona da Mata region in Brazil. Morphometric maps generated from a digital elevation model, together with Landsat-8 satellite imagery, and climatic maps, were among the set of covariates to be selected by the Recursive Feature Elimination algorithm to predict soil types using machine... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Soil classification; Machine learning; Pedometrics; Land use planning; Agrarian reform. |
| Ano: 2018 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832018000100313 |
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| Weber,Vanessa Aparecida de Moraes; Weber,Fabricio de Lima; Gomes,Rodrigo da Costa; Oliveira Junior,Adair da Silva; Menezes,Geazy Vilharva; Abreu,Urbano Gomes Pinto de; Belete,Nícolas Alessandro de Souza; Pistori,Hemerson. |
| Abstract The objective with this study was to analyze the body measurements of Girolando cattle, as well as measurements extracted from their images, to generate a model to understand which measures further explain the cattle body weight. Therefore, the experiment physically measured 34 Girolando cattle (two males and 32 females), for the following traits: heart girth (HGP), circumference of the abdomen, body length, occipito-ischial length, wither height, and hip height. In addition, images of the dorsum and the body lateral area of these animals allowed measurements of hip width (HWI), body length, tail distance to the neck, dorsum area (DAI), dorsum perimeter, wither height, hip height, body lateral area, perimeter of the lateral area, and rib height.... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Cattle; Computer vision; Livestock precision; Machine learning; Mass estimation. |
| Ano: 2020 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-35982020000100800 |
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| Registros recuperados: 25 | |
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