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

Alizamir, Meysam and Kisi, Ozgur and Ahmed, Ali Najah and Mert, Cihan and Fai, Chow Ming and Kim, Sungwon and Kim, Nam Won and El-Shafie, Ahmed (2020) Advanced machine learning model for better prediction accuracy of soil temperature at different depths. PLoS ONE, 15 (4). ISSN 1932-6203, DOI

Full text not available from this repository.


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.

Item Type: Article
Funders: Korea Institute of Civil Engineering and Building Technology (20200027-001)
Uncontrolled Keywords: Machine learning; Prediction; Soil temperature; Different depths
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Engineering > Department of Civil Engineering
Depositing User: Ms Zaharah Ramly
Date Deposited: 17 Jan 2023 02:35
Last Modified: 17 Jan 2023 02:35

Actions (login required)

View Item View Item