Publicaciones en colaboración con investigadores/as de University of Southampton (11)

2018

  1. Feature selection approaches for predictive modelling of groundwater nitrate pollution: An evaluation of filters, embedded and wrapper methods

    Science of the Total Environment, Vol. 624, pp. 661-672

2015

  1. Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines

    Ore Geology Reviews, Vol. 71, pp. 804-818

2014

  1. Categorical indicator kriging for assessing the risk of groundwater nitrate pollution: The case of Vega de Granada aquifer (SE Spain)

    Science of the Total Environment, Vol. 470-471, pp. 229-239

  2. Predictive modeling of groundwater nitrate pollution using Random Forest and multisource variables related to intrinsic and specific vulnerability: A case study in an agricultural setting (Southern Spain)

    Science of the Total Environment, Vol. 476-477, pp. 189-206

  3. Regression trees for modeling geochemical data-An application to Late Jurassic carbonates (Ammonitico Rosso)

    Computers and Geosciences, Vol. 73, pp. 198-207

2012

  1. Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture

    Remote Sensing of Environment, Vol. 121, pp. 93-107

2011

  1. Image fusion by spatially adaptive filtering using downscaling cokriging

    ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 66, Núm. 3, pp. 337-346

  2. Incorporating spatial variability measures in land-cover classification using Random Forest

    Procedia Environmental Sciences

2010

  1. DSCOKRI: A library of computer programs for downscaling cokriging in support of remote sensing applications

    Computers and Geosciences, Vol. 36, Núm. 7, pp. 881-894

2008

  1. Downscaling cokriging for super-resolution mapping of continua in remotely sensed images

    IEEE Transactions on Geoscience and Remote Sensing

2006

  1. Downscaling cokriging for image sharpening

    Remote Sensing of Environment, Vol. 102, Núm. 1-2, pp. 86-98