Advanced machine learning model for better prediction accuracy of soil temperature at different depths
air temperature; article; artificial neural network; atmosphere; chemical reaction; controlled study; ecosystem; linear regression analysis; prediction; soil depth; soil temperature; solar radiation; wind speed; chemistry; ecosystem; environmental monitoring; machine learning; river; soil; statistic...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
Public Library of Science
2023
|
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Universiti Tenaga Nasional |
| id |
my.uniten.dspace-25525 |
|---|---|
| record_format |
dspace |
| spelling |
my.uniten.dspace-255252023-05-29T16:10:29Z Advanced machine learning model for better prediction accuracy of soil temperature at different depths Alizamir M. Kisi O. Ahmed A.N. Mert C. Fai C.M. Kim S. Kim N.W. El-Shafie A. 57188682009 6507051085 57214837520 54898539300 57214146115 36349835400 34770123000 16068189400 air temperature; article; artificial neural network; atmosphere; chemical reaction; controlled study; ecosystem; linear regression analysis; prediction; soil depth; soil temperature; solar radiation; wind speed; chemistry; ecosystem; environmental monitoring; machine learning; river; soil; statistical model; temperature; Atmosphere; Ecosystem; Environmental Monitoring; Linear Models; Machine Learning; Neural Networks, Computer; Rivers; Soil; Temperature 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. � 2020 Alizamir et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Final 2023-05-29T08:10:29Z 2023-05-29T08:10:29Z 2020 Article 10.1371/journal.pone.0231055 2-s2.0-85083389932 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083389932&doi=10.1371%2fjournal.pone.0231055&partnerID=40&md5=af5e7e065e77ef426d65b63e53eb138b https://irepository.uniten.edu.my/handle/123456789/25525 15 4 e0231055 All Open Access, Gold, Green Public Library of Science Scopus |
| institution |
Universiti Tenaga Nasional |
| building |
UNITEN Library |
| collection |
Institutional Repository |
| continent |
Asia |
| country |
Malaysia |
| content_provider |
Universiti Tenaga Nasional |
| content_source |
UNITEN Institutional Repository |
| url_provider |
http://dspace.uniten.edu.my/ |
| description |
air temperature; article; artificial neural network; atmosphere; chemical reaction; controlled study; ecosystem; linear regression analysis; prediction; soil depth; soil temperature; solar radiation; wind speed; chemistry; ecosystem; environmental monitoring; machine learning; river; soil; statistical model; temperature; Atmosphere; Ecosystem; Environmental Monitoring; Linear Models; Machine Learning; Neural Networks, Computer; Rivers; Soil; Temperature |
| author2 |
57188682009 |
| author_facet |
57188682009 Alizamir M. Kisi O. Ahmed A.N. Mert C. Fai C.M. Kim S. Kim N.W. El-Shafie A. |
| format |
Article |
| author |
Alizamir M. Kisi O. Ahmed A.N. Mert C. Fai C.M. Kim S. Kim N.W. El-Shafie A. |
| spellingShingle |
Alizamir M. Kisi O. Ahmed A.N. Mert C. Fai C.M. Kim S. Kim N.W. El-Shafie A. Advanced machine learning model for better prediction accuracy of soil temperature at different depths |
| author_sort |
Alizamir M. |
| title |
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_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_sort |
advanced machine learning model for better prediction accuracy of soil temperature at different depths |
| publisher |
Public Library of Science |
| publishDate |
2023 |
| _version_ |
1806424541455450112 |