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

Ana D. Maldonado, Darío Ramos-López and Pedro A. Aguilera
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Ana D. Maldonado: Department of Biology and Geology, University of Almería, 04120 Almería, Spain
Darío Ramos-López: Department of Applied Mathematics, Rey Juan Carlos University, 28933 Madrid, Spain
Pedro A. Aguilera: Department of Biology and Geology, University of Almería, 04120 Almería, Spain

Sustainability, 2018, vol. 10, issue 11, 1-16

Abstract: 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.

Keywords: cultural landscapes; socioeconomic indicators; multiple linear regression; model trees; neural networks; probabilistic graphical models (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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