Soil temperature modeling using machine learning techniques مقالة

كاتب مسؤول: Fathololoumi، S ؛

مؤلف: Vaezi، A.R ؛ Alavi panah، S.K ؛ Montzka، C ؛ Ghorbani، A ؛ Biswas، A ؛

DESERT Summer & Autumn 2020, Volume 25 - Number 2 التصنيف: ب (‎15 صفحة - من 185 إلی 199 )

الکلمات المفتاحية: modeling Soil properties shadow Environmental parameters Soil ANFIS AT ANN MLR Iran

خلاصة:

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.

ملخص الجهاز:

Machine learning methods including Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Interface System (ANFIS) were used for modeling ST. Given these research gaps, the purpose of this study was to model ST using short time air and soil temperature data, soil properties, and environmental parameters in cold season using regression and machine learning algorithms. The cold season was considered because ST data were not widely available in the hot season; also, to study the effect of environmental conditions such as air temperature on ST at different depths, the use of cold season data was more appropriate owing to the low impact of solar radiation on ST. The relationship between soil and air temperature was also studied at two depths and two times of day (12 pm and 1 am) based on different soil textures and one manure sample. Saturated hydraulic conductivity (Ks), pH, electrical conductivity (EC), and calcium carbonate equivalent (CCE) of different soil types of were measured using constant head method (Wang and Benson 2018), saturated paste, 1:205 (sample: water) suspension (Jackson, 2005), and the method described in (Nelson, 1982), respectively. ST modeling based on soil properties and environmental parameters Air temperature (AT), SM, BD, sun (sun- shadow) (S), soil depth (D), day per year (DA), and hour per day (T) were included as independent variables to predict ST. Multiple linear regression (MLR) was used in this study to examine the impact of soil properties and environmental parameters on ST and its modeling. Prediction of soil temperature using regression and artificial neural network models.

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