Open AccessForecasting soil temperature based on surface air temperature using a wavelet artificial neural network
Author(s) -
Araghi Alireza,
MousaviBaygi Mohammad,
Adamowski Jan,
Martinez Christopher,
van der Ploeg Martine
Publication year - 2017
Publication title -
meteorological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.672
H-Index - 59
eISSN - 1469-8080
pISSN - 1350-4827
DOI - 10.1002/met.1661
Subject(s) - artificial neural network , environmental science , wavelet transform , frost (temperature) , meteorology , air temperature , surface air temperature , wavelet , computer science , precipitation , machine learning , artificial intelligence , geography
Soil temperature is a very important variable in agricultural meteorology and strongly influences agricultural activities and planning (e.g. the date and depth of sowing crops, frost protection). There are many physically based studies in the literature which model soil temperature, but few are easily applicable for use in the field. Simple and precise short‐term forecasting of soil temperature with minimum data requirements is the main goal of this study. The soil temperature at 0300, 0900 and 1500 GMT was forecast based only on surface air temperatures using artificial neural network ( ANN ) and wavelet transform artificial neural network ( WANN ) models. The hourly data were collected from the M ashhad synoptic station in K horasan R azavi province in Iran between 2010 and 2013. The results of this study showed that using a wavelet transform for preprocessing improved the accuracy of soil temperature forecasting. It was also found that changing the temporal increment in forecasting time did not have a noticeable effect on errors in the WANN models. WANN models can be used as accurate tools to forecast soil temperature 1–7 days ahead at depths of 5–30 cm.