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Please use this identifier to cite or link to this item: http://dspace.ucuenca.edu.ec/handle/123456789/35379
Title: Development of pedotransfer functions for water retention in tropical mountain soil landscapes: spotlight on parameter tuning in machine learning
Authors: Ellinger, Monja
Lieb, Mareike
Gebauer, Anika
Brito Gomez, Victor Manuel
metadata.dc.ucuenca.correspondencia: Gebauer, Anika , anika.gebauer@ufz.de
Keywords: Pedotransfer functions
metadata.dc.ucuenca.areaconocimientofrascatiamplio: 1. Ciencias Naturales y Exactas
metadata.dc.ucuenca.areaconocimientofrascatidetallado: 1.5.10 Recursos Hídricos
metadata.dc.ucuenca.areaconocimientofrascatiespecifico: 1.5 Ciencias de la Tierra y el Ambiente
metadata.dc.ucuenca.areaconocimientounescoamplio: 05 - Ciencias Físicas, Ciencias Naturales, Matemáticas y Estadísticas
metadata.dc.ucuenca.areaconocimientounescodetallado: 0521 - Ciencias Ambientales
metadata.dc.ucuenca.areaconocimientounescoespecifico: 052 - Medio Ambiente
Issue Date: 2020
metadata.dc.ucuenca.volumen: Volumen 6, número 1
metadata.dc.source: SOIL
metadata.dc.identifier.doi: 10.5194/soil-6-215-2020
metadata.dc.type: ARTÍCULO
Abstract: 
© 2020 Copernicus Gmb H. All rights reserved. Machine-learning algorithms are good at computing non-linear problems and fitting complex composite functions, which makes them an adequate tool for addressing multiple environmental research questions. One important application is the development of pedotransfer functions (PTFs). This study aims to develop water retention PTFs for two remote tropical mountain regions with rather different soil landscapes: (1) those dominated by peat soils and soils under volcanic influence with high organic matter contents and (2) those dominated by tropical mineral soils. Two tuning procedures were compared to fit boosted regression tree models: (1) tuning with grid search, which is the standard approach in pedometrics; and (2) tuning with differential evolution optimization. A nested cross-validation approach was applied to generate robust models. The area-specific PTFs developed outperform other more general PTFs. Furthermore, the first PTF for typical soils of Páramo landscapes (Ecuador), i.e., organic soils under volcanic influence, is presented. Overall, the results confirmed the differential evolution algorithm's high potential for tuning machine-learning models. While models based on tuning with grid search roughly predicted the response variables' mean for both areas, models applying the differential evolution algorithm for parameter tuning explained up to 25 times more of the response variables' variance.
URI: http://dspace.ucuenca.edu.ec/handle/123456789/35379
https://soil.copernicus.org/articles/6/215/2020/#top
metadata.dc.ucuenca.urifuente: https://www.soil-journal.net/
ISSN: 2199-3971
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