Publication: Advanced machine learning model for better prediction accuracy of soil temperature at different depths
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Date
2020
Authors
Alizamir M.
Kisi O.
Ahmed A.N.
Mert C.
Fai C.M.
Kim S.
Kim N.W.
El-Shafie A.
Journal Title
Journal ISSN
Volume Title
Publisher
Public Library of Science
Abstract
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.
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