Fractional prediction of ground temperature based on soil field spectrum

التفاصيل البيبلوغرافية
العنوان: Fractional prediction of ground temperature based on soil field spectrum
المؤلفون: Heigang Xiong, Cheng-Biao Fu, Long Yu, An-Hong Tian, Zheng-Biao Li
المصدر: Thermal Science, Vol 24, Iss 4, Pp 2301-2309 (2020)
بيانات النشر: VINCA Institute of Nuclear Sciences, 2020.
سنة النشر: 2020
مصطلحات موضوعية: Surface (mathematics), Physics, Field (physics), Renewable Energy, Sustainability and the Environment, lcsh:Mechanical engineering and machinery, Spectrum (functional analysis), Signal, model evaluation criteria, Computational physics, Fractional calculus, Wavelength, ground temperature, fractional derivate, soil field spectra signal, Ground temperature, Reflection (physics), lcsh:TJ1-1570
الوصف: Ground temperature is an important physical indicator reflecting the natural ecological environment of the Earth's surface. Soil spectrum is a comprehensive reflection of soil properties, but there are few studies on the prediction of ground temperature based on soil field spectrum using fractional calculus. In this paper, the fractional derivative is used to study the correlation between soil spectrum and ground temperature from zeroth order to second order, and the characteristic wavelength bands are extracted. Simulations show that the fractional approach can amplify the difference of the soil field spectral signal. The wavelength bands for the 0.01 significance test begin with 0.6th order, while the 1.3th order sees 33 wavelength bands. Coefficients of determination of 0.7, 0.8, 0.9, 1.3, 1.4, 1.5, 1.6, and 1.7-order are all greater than 0.66, indicating that the established model of linear stepwise multiple regression gives a better prediction.
اللغة: English
تدمد: 0354-9836
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::00830374f2fb14028982b660e8424637
http://www.doiserbia.nb.rs/img/doi/0354-9836/2020/0354-98362004301T.pdf
Rights: OPEN
رقم الانضمام: edsair.doi.dedup.....00830374f2fb14028982b660e8424637
قاعدة البيانات: OpenAIRE