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001     894126
005     20220131120350.0
024 7 _ |2 doi
|a 10.22059/jdesert.2020.79256
024 7 _ |2 Handle
|a 2128/28321
037 _ _ |a FZJ-2021-03053
082 _ _ |a 550
100 1 _ |0 P:(DE-HGF)0
|a Fathololoumi, S.
|b 0
|e Corresponding author
245 _ _ |a Soil temperature modeling using machine learning techniques
260 _ _ |a Tehran
|b Univ., International Research Center for Living with Desert
|c 2020
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336 7 _ |2 BibTeX
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|a Journal Article
520 _ _ |a Soil Temperature (ST) is critical for environmental applications. While its measurement is often difficult, estimation from environmental parameters has shown promise. The purpose of this study was to model ST in cold season from soil properties and environmental parameters. This study was conducted as a pot experiment in Ardebil, Iran. Automatic thermal sensors were installed at 5 and 10 cm depths. Besides, soil properties and environmental parameters were determined based on field and laboratory works. Machine learning methods including Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Interface System (ANFIS) were used for modeling ST. The air temperature was observed as the most effective factor in ST modeling. The relationship between soil and air temperature was stronger at 5 cm depth compared to 10 cm. The R2 between soil and air temperature was higher in the absence of sunlight than in its presence. The prediction of ANFIS (R2= 0.96 and MAPE= 10.5) was closer to the observed ST values compared to the ANN (R2= 0.91 and MAPE= 35) and MLR (R2= 0.57 and MAPE= 41). The results revealed the advantage of ANFIS method for ST modeling. This approach can be applied for soil depths and locations with data gap.
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|a Vaezi, A. R.
|b 1
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|a Alavipanah, S. K.
|b 2
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|a Montzka, C.
|b 3
700 1 _ |0 P:(DE-HGF)0
|a Ghorbani, A.
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700 1 _ |0 P:(DE-HGF)0
|a Biswas, A.
|b 5
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|a 10.22059/jdesert.2020.79256
|n 2
|p 185-199
|t Desert
|v 25
|x 1026-1346
|y 2020
856 4 _ |u https://juser.fz-juelich.de/record/894126/files/JDESERT_Volume%2025_Issue%202_Pages%20185-199.pdf
|y OpenAccess
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|a DE-HGF
|b Forschungsbereich Erde und Umwelt
|l Erde im Wandel – Unsere Zukunft nachhaltig gestalten
|v Für eine nachhaltige Bio-Ökonomie – von Ressourcen zu Produkten
|x 0
914 1 _ |y 2021
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