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Monthly Soil Temperature Modeling Using Gene Expression Programming

Year 2019, Volume: 8 Issue: 4, 1327 - 1337, 24.12.2019
https://doi.org/10.17798/bitlisfen.527053

Abstract

Soil
temperature is a critical variable controlling below-ground processes for
global and continental carbon budgets. However, there are an insufficient
number of climatic stations monitoring soil temperature. In this study, GEP
model was used for estimation of monthly soil temperature using air
temperature, depth, relative humidity and solar radiation data for the Antalya,
Isparta, and Burdur in Turkey. This model was tested using measured
meteorological data. The values of R2 between observed and predicted
soil temperatures ranged from 0.95 to 0.97. Predictions with GEP model show
good agreement with actual soil temperature measurements. New equations are
presented for calculation of soil temperatures at different depths. The
GEP-based formulations are very practical to predict soil temperature. Soil
temperature prediction with GEP model is helpful in various processes,
including agricultural decision, heating or cooling of buildings and
ground-source heat pump applications.

References

  • Gao Z., Horton R., Wang L., Lıu H., Wen J. 2008. An improved force-restore method for soil temperature prediction, European Journal of Soil Science, 59(5):972–981.
  • Citakoglu H. 2017. Comparison of artificial intelligence techniques for prediction of soil temperatures in Turkey, Theoretical and Applied Climatology, 130(1-2):545-556. Talaee P., H. 2014. Daily soil temperature modeling using neuro-fuzzy approach, Theoretical and Applied Climatology, 118(3):481- 489.
  • Behmanesh J., Mehdizadeh, S. 2017. Estimation of soil temperature using gene expression programming and artificial neural networks in a semiarid region, Environmental Earth Sciences, 76(2), 76.
  • Kermani M. 2013. Hydrometeorological Parameters in Prediction of Soil Temperature by Means of Artificial Neural Network: Case Study in Wyoming, Journal of Hydrologic Engineering, 18(6):707-718.
  • Kim S., Singh V. P. 2014. Modeling daily soil temperature using data-driven models and spatial distribution, Theoretical and Applied Climatology, 118(3):465–479.
  • Kisi O., Tombul M., Kermani M. Z. 2015. Modeling soil temperatures at different depths by using three different neural computing techniques, Theoretical and Applied Climatology, 121(1-2):377–387.
  • Mihalakakou G. 2002. On estimating soil surface temperature profiles, Energy and Buildings, 34(3):251-259.
  • Bilgili M. 2010. Prediction of soil temperature using regression and artificial neural network models, Meteorology and Atmospheric Physics, 110(1-2):59 –70.
  • Kisi O., Sanikhani H., Cobaner M. 2017. Soil temperature modeling at different depths using neuro-fuzzy, neural network, and genetic programming techniques, Theoretical and Applied Climatology, 129(3-4):833-848.
  • Wu W., Tang X. P., Guo N. J., Yang C., Lui H. B., Shang Y. F. 2013. Spatiotemporal modeling of monthly soil temperature using artificial neural networks, Theoretical and Applied Climatology, 113(3-4):481–494.
  • Ferreira C. 2006. Gene expression programming: mathematical modeling by an artificial intelligence (Vol. 21). Springer.
  • Ferreira C. 2002. Combinatorial Optimization by Gene Expression Programming: Inversion Revisited. In J. M. Santos and A. Zapico, eds., Proceedings of the Argentine Symposium on Artificial Intelligence,160–174, Santa Fe, Argentina.
  • Ferreira C. 2001. Gene Expression Programming: A New Adaptive Algorithm for Solving Problems, Complex Systems, 13(2): 87–129.
  • Ferreira C. 2006. Designing Neural Networks Using Gene Expression Programming. In A. Abraham, B. de Baets, M. Köppen, and B. Nickolay, eds., Applied Soft Computing Technologies: The Challenge of Complexity, 517–536, Springer-Verlag.
  • Talaee P., H. 2014. Daily soil temperature modeling using neuro-fuzzy approach, Theoretical and Applied Climatology, 118(3):481- 489.

Monthly Soil Temperature Modeling Using Gene Expression Programming

Year 2019, Volume: 8 Issue: 4, 1327 - 1337, 24.12.2019
https://doi.org/10.17798/bitlisfen.527053

Abstract

Toprak sıcaklığı, küresel ve karasal karbon bütçeleri
için yer altı süreçlerini kontrol eden kritik bir değişkendir. Ancak, toprak sıcaklığını
izleyen az sayıda iklim istasyonu vardır. Bu çalışmada, Antalya, Isparta ve
Burdur illeri için hava sıcaklığı, derinlik, bağıl nem ve güneş ışınımı
verileri yardımıyla aylık toprak sıcaklığının tahmini için GEP modeli
kullanılmıştır. Bu model ölçülen meteorolojik veriler kullanılarak test
edilmiştir. Ölçülen ve tahmin edilen toprak sıcaklıkları arasındaki R2
değerleri 0,95 ila 0,97 arasında değişmiştir. GEP modeli ile yapılan tahminler,
gerçek toprak sıcaklığı ölçümleriyle iyi bir uyum göstermektedir. Farklı
derinliklerde toprak sıcaklıklarının hesaplanması için yeni denklemler
sunulmuştur. GEP modelinden elde edilen denklemler, toprak sıcaklığını tahmin
etmek için çok pratiktir. GEP modeli ile toprak sıcaklığı tahmini, tarımsal
uygulamalar, binaların ısıtılması veya soğutulması ve toprak kaynaklı ısı
pompası uygulamaları gibi işlemlerde oldukça yardımcı olacaktır. 

References

  • Gao Z., Horton R., Wang L., Lıu H., Wen J. 2008. An improved force-restore method for soil temperature prediction, European Journal of Soil Science, 59(5):972–981.
  • Citakoglu H. 2017. Comparison of artificial intelligence techniques for prediction of soil temperatures in Turkey, Theoretical and Applied Climatology, 130(1-2):545-556. Talaee P., H. 2014. Daily soil temperature modeling using neuro-fuzzy approach, Theoretical and Applied Climatology, 118(3):481- 489.
  • Behmanesh J., Mehdizadeh, S. 2017. Estimation of soil temperature using gene expression programming and artificial neural networks in a semiarid region, Environmental Earth Sciences, 76(2), 76.
  • Kermani M. 2013. Hydrometeorological Parameters in Prediction of Soil Temperature by Means of Artificial Neural Network: Case Study in Wyoming, Journal of Hydrologic Engineering, 18(6):707-718.
  • Kim S., Singh V. P. 2014. Modeling daily soil temperature using data-driven models and spatial distribution, Theoretical and Applied Climatology, 118(3):465–479.
  • Kisi O., Tombul M., Kermani M. Z. 2015. Modeling soil temperatures at different depths by using three different neural computing techniques, Theoretical and Applied Climatology, 121(1-2):377–387.
  • Mihalakakou G. 2002. On estimating soil surface temperature profiles, Energy and Buildings, 34(3):251-259.
  • Bilgili M. 2010. Prediction of soil temperature using regression and artificial neural network models, Meteorology and Atmospheric Physics, 110(1-2):59 –70.
  • Kisi O., Sanikhani H., Cobaner M. 2017. Soil temperature modeling at different depths using neuro-fuzzy, neural network, and genetic programming techniques, Theoretical and Applied Climatology, 129(3-4):833-848.
  • Wu W., Tang X. P., Guo N. J., Yang C., Lui H. B., Shang Y. F. 2013. Spatiotemporal modeling of monthly soil temperature using artificial neural networks, Theoretical and Applied Climatology, 113(3-4):481–494.
  • Ferreira C. 2006. Gene expression programming: mathematical modeling by an artificial intelligence (Vol. 21). Springer.
  • Ferreira C. 2002. Combinatorial Optimization by Gene Expression Programming: Inversion Revisited. In J. M. Santos and A. Zapico, eds., Proceedings of the Argentine Symposium on Artificial Intelligence,160–174, Santa Fe, Argentina.
  • Ferreira C. 2001. Gene Expression Programming: A New Adaptive Algorithm for Solving Problems, Complex Systems, 13(2): 87–129.
  • Ferreira C. 2006. Designing Neural Networks Using Gene Expression Programming. In A. Abraham, B. de Baets, M. Köppen, and B. Nickolay, eds., Applied Soft Computing Technologies: The Challenge of Complexity, 517–536, Springer-Verlag.
  • Talaee P., H. 2014. Daily soil temperature modeling using neuro-fuzzy approach, Theoretical and Applied Climatology, 118(3):481- 489.
There are 15 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Araştırma Makalesi
Authors

Prof. Dr.arzu Şencan Şahin

Erkan Dikmen This is me

Kazım Kumaş

Publication Date December 24, 2019
Submission Date February 14, 2019
Acceptance Date October 16, 2019
Published in Issue Year 2019 Volume: 8 Issue: 4

Cite

IEEE P. D. Şencan Şahin, E. Dikmen, and K. Kumaş, “Monthly Soil Temperature Modeling Using Gene Expression Programming”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 8, no. 4, pp. 1327–1337, 2019, doi: 10.17798/bitlisfen.527053.

Bitlis Eren University
Journal of Science Editor
Bitlis Eren University Graduate Institute
Bes Minare Mah. Ahmet Eren Bulvari, Merkez Kampus, 13000 BITLIS