Prof. Dr.arzu ŞENCAN ŞAHİN, Erkan DİKMEN, Kazım KUMAŞ

Monthly Soil Temperature Modeling Using Gene Expression Programming

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.

Anahtar Kelimeler:

GEP, Toprak Sıcaklığı, Meteorolojik veri, Modelleme

Monthly Soil Temperature Modeling Using Gene Expression Programming

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.

Keywords:

GEP, soil temperature, meteorological data, modeling,

PDF

___

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.