A Comparison of Machine-Learning Methods to Select Socioeconomic Indicators in Cultural Landscapes
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 bet...
| Main Authors: | , , |
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| Format: | info:eu-repo/semantics/article |
| Language: | English |
| Published: |
MDPI
2020
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10835/7538 |
| _version_ | 1789406668319096832 |
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| author | Maldonado González, Ana Devaki Ramos López, Darío Aguilera Aguilera, Pedro |
| author_facet | Maldonado González, Ana Devaki Ramos López, Darío Aguilera Aguilera, Pedro |
| author_sort | Maldonado González, Ana Devaki |
| collection | DSpace |
| description | 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. |
| format | info:eu-repo/semantics/article |
| id | oai:repositorio.ual.es:10835-7538 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2020 |
| publisher | MDPI |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-75382023-04-12T19:00:21Z A Comparison of Machine-Learning Methods to Select Socioeconomic Indicators in Cultural Landscapes Maldonado González, Ana Devaki Ramos López, Darío Aguilera Aguilera, Pedro cultural landscapes socioeconomic indicators multiple linear regression model trees neural networks probabilistic graphical models 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. 2020-01-17T09:59:56Z 2020-01-17T09:59:56Z 2018-11-21 info:eu-repo/semantics/article 2071-1050 http://hdl.handle.net/10835/7538 en https://www.mdpi.com/2071-1050/10/11/4312 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess MDPI |
| spellingShingle | cultural landscapes socioeconomic indicators multiple linear regression model trees neural networks probabilistic graphical models Maldonado González, Ana Devaki Ramos López, Darío Aguilera Aguilera, Pedro A Comparison of Machine-Learning Methods to Select Socioeconomic Indicators in Cultural Landscapes |
| title | A Comparison of Machine-Learning Methods to Select Socioeconomic Indicators in Cultural Landscapes |
| title_full | A Comparison of Machine-Learning Methods to Select Socioeconomic Indicators in Cultural Landscapes |
| title_fullStr | A Comparison of Machine-Learning Methods to Select Socioeconomic Indicators in Cultural Landscapes |
| title_full_unstemmed | A Comparison of Machine-Learning Methods to Select Socioeconomic Indicators in Cultural Landscapes |
| title_short | A Comparison of Machine-Learning Methods to Select Socioeconomic Indicators in Cultural Landscapes |
| title_sort | comparison of machine-learning methods to select socioeconomic indicators in cultural landscapes |
| topic | cultural landscapes socioeconomic indicators multiple linear regression model trees neural networks probabilistic graphical models |
| url | http://hdl.handle.net/10835/7538 |
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