دورية أكاديمية

Short-term forecasting of soil temperature using artificial neural network

التفاصيل البيبلوغرافية
العنوان: Short-term forecasting of soil temperature using artificial neural network
المؤلفون: Tabari, Hossein, Hosseinzadeh Talaee, Parisa, Willems, Patrick
بيانات النشر: Cambridge University Press
سنة النشر: 2015
المجموعة: KU Leuven: Lirias
مصطلحات موضوعية: Soil temperature, ANN, Forecasting
الوصف: Soil temperature is one of the most important meteorological parameters which plays a critical role in land surface hydrological processes. In the current study, artificial neural network (ANN) models were developed and tested for 1-day ahead soil temperature forecasting at 5-, 10-, 20-, 30-, 50- and 100-cm depths. Antecedent soil temperatures plus concurrent and antecedent air temperatures were used as inputs of the ANN models. Soil and air temperatures data were collected from two Iranian weather stations located in humid and arid regions for the period 2004-2005. The models’ accuracies were evaluated using the Nash-Sutcliffe coefficient of efficiency, the correlation coefficient, the root mean square error and the mean bias error between the observed and forecasted soil temperature values. The Nash-Sutcliffe coefficient of efficiency values higher than 0.94 and correlation coefficient higher than 0.96 for all the ANN models show that the models can be successfully applied to provide accurate and reliable short-term soil temperature forecasts. ; status: published
نوع الوثيقة: article in journal/newspaper
وصف الملف: 1737618 bytes; application/pdf
اللغة: English
تدمد: 1350-4827
1469-8080
العلاقة: Meteorological Applications vol:22 pages:576-585; https://lirias.kuleuven.be/handle/123456789/474278Test; https://lirias.kuleuven.be/bitstream/123456789/474278/4//Hossein-Soil+temperature+%28Meteorol+Appl+2015%29.pdfTest
DOI: 10.1002/met.1489
الإتاحة: https://doi.org/10.1002/met.1489Test
https://lirias.kuleuven.be/handle/123456789/474278Test
https://lirias.kuleuven.be/bitstream/123456789/474278/4//Hossein-Soil+temperature+%28Meteorol+Appl+2015%29.pdfTest
رقم الانضمام: edsbas.2FA206DC
قاعدة البيانات: BASE
الوصف
تدمد:13504827
14698080
DOI:10.1002/met.1489