Cover Image

Wavelet neural networks and gene expression programming models to predict short-term soil temperature at different depths

Soil temperature (ST), as one of the critical meteorological parameters, has great effects on many underground soil ecological processes. Due to the fact that accurate measuring of ST is costly because of launching field equipment, evolving predictive models to approximate ST is of great importance....

Full description

Saved in:
Bibliographic Details
Main Authors: Samadianfard, S., Asadi, E., Jarhan, Salar, Kazemi, Honeyeh, Kheshtgar, Salar, Kisi, O., Sajjadi, S., Abdul Manaf, Azizah
Format: Article
Published: Elsevier B. V. 2018
Subjects:
T Technology (General)
Online Access:http://eprints.utm.my/id/eprint/86713/
http://dx.doi.org/10.1016/j.still.2017.08.012
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utm.86713
record_format eprints
spelling my.utm.867132020-09-30T09:04:54Z http://eprints.utm.my/id/eprint/86713/ Wavelet neural networks and gene expression programming models to predict short-term soil temperature at different depths Samadianfard, S. Asadi, E. Jarhan, Salar Kazemi, Honeyeh Kheshtgar, Salar Kisi, O. Sajjadi, S. Abdul Manaf, Azizah T Technology (General) Soil temperature (ST), as one of the critical meteorological parameters, has great effects on many underground soil ecological processes. Due to the fact that accurate measuring of ST is costly because of launching field equipment, evolving predictive models to approximate ST is of great importance. Therefore, achieving accurate, reliable and easily attainable predictions of daily ST values is the main objective of the current research. To that end, the usefulness of three data-driven procedures containing artificial neural networks (ANN), wavelet neural networks (WNN) and gene expression programming (GEP) were examined for the estimation of ST at different soil depths at Tabriz synoptic station, north-west of Iran. In conformity with the correlation coefficients among ST and meteorological parameters, it was found that air temperature, Sunshine hours and radiation had the most and unquestionable effects on ST prediction at all considered depths. For evaluating the performance of these approaches, four different statistical error measures were used: coefficient of correlation (CC), mean absolute error (MAE), root mean squared error (RMSE) and Akaike's information criterion (AIC). Moreover, Taylor diagrams were employed for assessing the similarity between the observed and predicted ST values. Results revealed that the WNN in all considered depths had the best performance in ST prediction, but with increasing soil depth, the effect of meteorological parameters and estimation accuracy were reduced rapidly. As a conclusion, the lower values of RMSE and higher values of CC proved the effectiveness of WNN for predicting ST at the studied depths. Elsevier B. V. 2018-01 Article PeerReviewed Samadianfard, S. and Asadi, E. and Jarhan, Salar and Kazemi, Honeyeh and Kheshtgar, Salar and Kisi, O. and Sajjadi, S. and Abdul Manaf, Azizah (2018) Wavelet neural networks and gene expression programming models to predict short-term soil temperature at different depths. Soil & Tillage Research, 175 . pp. 37-50. ISSN 0167-1987 http://dx.doi.org/10.1016/j.still.2017.08.012
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic T Technology (General)
spellingShingle T Technology (General)
Samadianfard, S.
Asadi, E.
Jarhan, Salar
Kazemi, Honeyeh
Kheshtgar, Salar
Kisi, O.
Sajjadi, S.
Abdul Manaf, Azizah
Wavelet neural networks and gene expression programming models to predict short-term soil temperature at different depths
description Soil temperature (ST), as one of the critical meteorological parameters, has great effects on many underground soil ecological processes. Due to the fact that accurate measuring of ST is costly because of launching field equipment, evolving predictive models to approximate ST is of great importance. Therefore, achieving accurate, reliable and easily attainable predictions of daily ST values is the main objective of the current research. To that end, the usefulness of three data-driven procedures containing artificial neural networks (ANN), wavelet neural networks (WNN) and gene expression programming (GEP) were examined for the estimation of ST at different soil depths at Tabriz synoptic station, north-west of Iran. In conformity with the correlation coefficients among ST and meteorological parameters, it was found that air temperature, Sunshine hours and radiation had the most and unquestionable effects on ST prediction at all considered depths. For evaluating the performance of these approaches, four different statistical error measures were used: coefficient of correlation (CC), mean absolute error (MAE), root mean squared error (RMSE) and Akaike's information criterion (AIC). Moreover, Taylor diagrams were employed for assessing the similarity between the observed and predicted ST values. Results revealed that the WNN in all considered depths had the best performance in ST prediction, but with increasing soil depth, the effect of meteorological parameters and estimation accuracy were reduced rapidly. As a conclusion, the lower values of RMSE and higher values of CC proved the effectiveness of WNN for predicting ST at the studied depths.
format Article
author Samadianfard, S.
Asadi, E.
Jarhan, Salar
Kazemi, Honeyeh
Kheshtgar, Salar
Kisi, O.
Sajjadi, S.
Abdul Manaf, Azizah
author_facet Samadianfard, S.
Asadi, E.
Jarhan, Salar
Kazemi, Honeyeh
Kheshtgar, Salar
Kisi, O.
Sajjadi, S.
Abdul Manaf, Azizah
author_sort Samadianfard, S.
title Wavelet neural networks and gene expression programming models to predict short-term soil temperature at different depths
title_short Wavelet neural networks and gene expression programming models to predict short-term soil temperature at different depths
title_full Wavelet neural networks and gene expression programming models to predict short-term soil temperature at different depths
title_fullStr Wavelet neural networks and gene expression programming models to predict short-term soil temperature at different depths
title_full_unstemmed Wavelet neural networks and gene expression programming models to predict short-term soil temperature at different depths
title_sort wavelet neural networks and gene expression programming models to predict short-term soil temperature at different depths
publisher Elsevier B. V.
publishDate 2018
url http://eprints.utm.my/id/eprint/86713/
http://dx.doi.org/10.1016/j.still.2017.08.012
_version_ 1680321085254402048
score 13.222552

Similar Items