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Open AccessArticle

A Comparison of Machine-Learning Methods to Select Socioeconomic Indicators in Cultural Landscapes

1
Department of Biology and Geology, University of Almería, 04120 Almería, Spain
2
Department of Applied Mathematics, Rey Juan Carlos University, 28933 Madrid, Spain
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sustainability 2018, 10(11), 4312; https://doi.org/10.3390/su10114312
Received: 31 October 2018 / Revised: 12 November 2018 / Accepted: 15 November 2018 / Published: 21 November 2018
(This article belongs to the Special Issue Social-Ecological Systems. Facing Global Transformations)
Cultural landscapes are regarded to be complex socioecological systems that originated as a result of the interaction between humanity and nature across time. Cultural landscapes present complex-system properties, including nonlinear dynamics among their components. There is a close relationship between socioeconomy and landscape in cultural landscapes, so that changes in the socioeconomic dynamic have an effect on the structure and functionality of the landscape. Several numerical analyses have been carried out to study this relationship, with linear regression models being widely used. However, cultural landscapes comprise a considerable amount of elements and processes, whose interactions might not be properly captured by a linear model. In recent years, machine-learning techniques have increasingly been applied to the field of ecology to solve regression tasks. These techniques provide sound methods and algorithms for dealing with complex systems under uncertainty. The term ‘machine learning’ includes a wide variety of methods to learn models from data. In this paper, we study the relationship between socioeconomy and cultural landscape (in Andalusia, Spain) at two different spatial scales aiming at comparing different regression models from a predictive-accuracy point of view, including model trees and neural or Bayesian networks. View Full-Text
Keywords: cultural landscapes; socioeconomic indicators; multiple linear regression; model trees; neural networks; probabilistic graphical models cultural landscapes; socioeconomic indicators; multiple linear regression; model trees; neural networks; probabilistic graphical models
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Maldonado, A.D.; Ramos-López, D.; Aguilera , P.A. A Comparison of Machine-Learning Methods to Select Socioeconomic Indicators in Cultural Landscapes. Sustainability 2018, 10, 4312.

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