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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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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. |
format | Online Article Text |
id | pubmed-7156082 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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