1) La descarga del recurso depende de la página de origen
2) Para poder descargar el recurso, es necesario ser usuario registrado en Universia


Opción 1: Descargar recurso

Opción 2: Descargar recurso

Detalles del recurso

Descripción

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 which a large number of collocations is available, results show that the random forest model is the best model to predict the systematic errors and it is computationally fast, making it a good candidate for operational processing. It is able to explain nearly 31% of the total variance of the bias (in comparison to about 24% for the multi-linear regression model).

Pertenece a

ArchiMer, Institutional Archive of Ifremer (French Research Institute for Exploitation of the Sea)  

Autor(es)

Picart, Stephane Saux -  Tandeo, Pierre -  Autret, Emmanuelle -  Gausset, Blandine - 

Id.: 71041151

Idioma: eng  - 

Versión: 1.0

Estado: Final

Tipo:  application/pdf - 

Palabras clavemachine learning - 

Tipo de recurso: Texto Narrativo  -  Publication  -  info:eu-repo/semantics/article  - 

Tipo de Interactividad: Expositivo

Nivel de Interactividad: muy bajo

Audiencia: Estudiante  -  Profesor  -  Autor  - 

Estructura: Atomic

Coste: no

Copyright: sí

: 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Formatos:  application/pdf - 

Requerimientos técnicos:  Browser: Any - 

Relación: [IsBasedOn] Remote Sensing (2072-4292) (Mdpi), 2018-02 , Vol. 10 , N. 2 , P. 224 (11p.)

Fecha de contribución: 14-abr-2018

Contacto:

Localización:
* DOI:10.3390/rs10020224

Otros recursos de la mismacolección

  1. The GEOTRACES Intermediate Data Product 2017 The new IDP2017 is a significant improvement over the earlier IDP2014 and roughly doubles the number...
  2. The effect of algae diets ( Skeletonema costatum and Rhodomonas baltica ) on the biochemical composition and sensory characteristics of Pacific cupped oysters ( Crassostrea gigas ) during land-based refinement Oyster refinement, a common practice in France, is aimed at increasing the weight of oyster tissue a...
  3. Ligament, hinge, and shell cross-sections of the Atlantic surfclam (Spisula solidissima): Promising marine environmental archives in NE North America The Atlantic surfclam (Spisula solidissima) is a commercially important species in North American wa...
  4. Zeaxanthin from Porphyridium purpureum induces apoptosis in human melanoma cells expressing the oncogenic BRAF V600E mutation and sensitizes them to the BRAF inhibitor vemurafenib Zeaxanthin, an abundant carotenoid present in fruits, vegetables and algae was reported to exert ant...
  5. Optimal age of the donor graft tissue in relation to cultured pearl phenotypes in the mollusc, Pinctada margaritifera Ageing is defined as the progressive decline in tissue and organ functions over time. This study aim...

Aviso de cookies: Usamos cookies propias y de terceros para mejorar nuestros servicios, para análisis estadístico y para mostrarle publicidad. Si continua navegando consideramos que acepta su uso en los términos establecidos en la Política de cookies.