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Advanced machine learning model for better prediction accuracy of soil temperature at different depths

Soil temperature has a vital importance in biological, physical and chemical processes of terrestrial ecosystem and its modeling at different depths is very important for land-atmosphere interactions. The study compares four machine learning techniques, extreme learning machine (ELM), artificial neu...

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Autores principales: Alizamir, Meysam, Kisi, Ozgur, Ahmed, Ali Najah, Mert, Cihan, Fai, Chow Ming, Kim, Sungwon, Kim, Nam Won, El-Shafie, Ahmed
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7156082/
https://www.ncbi.nlm.nih.gov/pubmed/32287272
http://dx.doi.org/10.1371/journal.pone.0231055
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author Alizamir, Meysam
Kisi, Ozgur
Ahmed, Ali Najah
Mert, Cihan
Fai, Chow Ming
Kim, Sungwon
Kim, Nam Won
El-Shafie, Ahmed
author_facet Alizamir, Meysam
Kisi, Ozgur
Ahmed, Ali Najah
Mert, Cihan
Fai, Chow Ming
Kim, Sungwon
Kim, Nam Won
El-Shafie, Ahmed
author_sort Alizamir, Meysam
collection PubMed
description Soil temperature has a vital importance in biological, physical and chemical processes of terrestrial ecosystem and its modeling at different depths is very important for land-atmosphere interactions. The study compares four machine learning techniques, extreme learning machine (ELM), artificial neural networks (ANN), classification and regression trees (CART) and group method of data handling (GMDH) in estimating monthly soil temperatures at four different depths. Various combinations of climatic variables are utilized as input to the developed models. The models’ outcomes are also compared with multi-linear regression based on Nash-Sutcliffe efficiency, root mean square error, and coefficient of determination statistics. ELM is found to be generally performs better than the other four alternatives in estimating soil temperatures. A decrease in performance of the models is observed by an increase in soil depth. It is found that soil temperatures at three depths (5, 10 and 50 cm) could be mapped utilizing only air temperature data as input while solar radiation and wind speed information are also required for estimating soil temperature at the depth of 100 cm.
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spelling pubmed-71560822020-04-16 Advanced machine learning model for better prediction accuracy of soil temperature at different depths Alizamir, Meysam Kisi, Ozgur Ahmed, Ali Najah Mert, Cihan Fai, Chow Ming Kim, Sungwon Kim, Nam Won El-Shafie, Ahmed PLoS One Research Article Soil temperature has a vital importance in biological, physical and chemical processes of terrestrial ecosystem and its modeling at different depths is very important for land-atmosphere interactions. The study compares four machine learning techniques, extreme learning machine (ELM), artificial neural networks (ANN), classification and regression trees (CART) and group method of data handling (GMDH) in estimating monthly soil temperatures at four different depths. Various combinations of climatic variables are utilized as input to the developed models. The models’ outcomes are also compared with multi-linear regression based on Nash-Sutcliffe efficiency, root mean square error, and coefficient of determination statistics. ELM is found to be generally performs better than the other four alternatives in estimating soil temperatures. A decrease in performance of the models is observed by an increase in soil depth. It is found that soil temperatures at three depths (5, 10 and 50 cm) could be mapped utilizing only air temperature data as input while solar radiation and wind speed information are also required for estimating soil temperature at the depth of 100 cm. Public Library of Science 2020-04-14 /pmc/articles/PMC7156082/ /pubmed/32287272 http://dx.doi.org/10.1371/journal.pone.0231055 Text en © 2020 Alizamir et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Alizamir, Meysam
Kisi, Ozgur
Ahmed, Ali Najah
Mert, Cihan
Fai, Chow Ming
Kim, Sungwon
Kim, Nam Won
El-Shafie, Ahmed
Advanced machine learning model for better prediction accuracy of soil temperature at different depths
title Advanced machine learning model for better prediction accuracy of soil temperature at different depths
title_full Advanced machine learning model for better prediction accuracy of soil temperature at different depths
title_fullStr Advanced machine learning model for better prediction accuracy of soil temperature at different depths
title_full_unstemmed Advanced machine learning model for better prediction accuracy of soil temperature at different depths
title_short Advanced machine learning model for better prediction accuracy of soil temperature at different depths
title_sort advanced machine learning model for better prediction accuracy of soil temperature at different depths
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7156082/
https://www.ncbi.nlm.nih.gov/pubmed/32287272
http://dx.doi.org/10.1371/journal.pone.0231055
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