V
Victor Rodriguez-Galiano
Researcher at University of Seville
Publications - 51
Citations - 4647
Victor Rodriguez-Galiano is an academic researcher from University of Seville. The author has contributed to research in topics: Random forest & Phenology. The author has an hindex of 20, co-authored 46 publications receiving 3313 citations. Previous affiliations of Victor Rodriguez-Galiano include University of Southampton & University of Granada.
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An assessment of the effectiveness of a random forest classifier for land-cover classification
TL;DR: In this paper, the performance of the random forest classifier for land cover classification of a complex area is explored based on several criteria: mapping accuracy, sensitivity to data set size and noise.
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Machine learning predictive models for mineral prospectivity: an evaluation of neural networks, random forest, regression trees and support vector machines
TL;DR: The results of applying the above algorithms to epithermal Au prospectivity mapping of the Rodalquilar district, Spain, indicate that the RF outperformed the other MLA algorithms (ANNs, RTs and SVMs), showing higher stability and robustness with varying training parameters and better success rates and ROC analysis results.
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Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture
Victor Rodriguez-Galiano,Mario Chica-Olmo,F. Abarca-Hernandez,Peter M. Atkinson,C. Jeganathan +4 more
TL;DR: In this article, a random forest classifier was applied to spectral as well as mono- and multi-seasonal textural features extracted from Landsat TM imagery to increase the accuracy of land cover classification over a complex Mediterranean landscape.
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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).
Victor Rodriguez-Galiano,Maria Paula Mendes,Maria Jose Garcia-Soldado,Mario Chica-Olmo,Luís Ribeiro +4 more
TL;DR: The performance of the RF regression for predictive modeling of nitrate pollution is explored, based on intrinsic and specific vulnerability assessment of the Vega de Granada aquifer, and prediction results show the ability of RF to build accurate models with strong predictive capabilities.
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Feature selection approaches for predictive modelling of groundwater nitrate pollution: An evaluation of filters, embedded and wrapper methods.
Victor Rodriguez-Galiano,Victor Rodriguez-Galiano,Juan Antonio Luque-Espinar,Mario Chica-Olmo,Maria Paula Mendes +4 more
TL;DR: A comprehensive GIS database of twenty parameters regarding hydrogeological and hydrological features and driving forces were used as inputs for predictive models of nitrate pollution to provide indications of agroecosystem dynamics.