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)

Watershed management decisions need robust methods, which allow an accurate predictive modeling of pollutant occurrences. Random Forest (RF) is a powerful machine learning data driven method that is rarely used in water resources studies, and thus has not been evaluated thoroughly in this field, whe...

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Bibliographic Details
Published in:	The Science of the total environment Vol. 476-477; pp. 189 - 206
Main Authors:	Rodriguez-Galiano, Victor, Mendes, Maria Paula, Garcia-Soldado, Maria Jose, Chica-Olmo, Mario, Ribeiro, Luis
Format:	Journal Article
Language:	English
Published:	Netherlands Elsevier B.V 01.04.2014
Subjects:	Agriculture algorithms aquifers artificial intelligence case studies Environmental Monitoring - methods geographic information systems Groundwater Groundwater - chemistry groundwater contamination Machine learning techniques Models, Chemical Nitrates Nitrates - analysis pollutants prediction Random Forest Regression Analysis remote sensing Spain Vulnerability assessment Water Pollutants, Chemical - analysis Water Pollution, Chemical - statistics & numerical data watershed management Spain Machine learning techniques Nitrates Groundwater Vulnerability assessment Random Forest
ISSN:	0048-9697, 1879-1026, 1879-1026
Online Access:	Get full text
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