Araştırma Makalesi
BibTex RIS Kaynak Göster

Yıl 2020, Cilt: 10 Sayı: 2, 242 - 251, 30.12.2020

Emrullah Acar

https://doi.org/10.36222/ejt.758602

Öz

Kaynakça

  • [1] S. Solomon, Intergovernmental Panel on Climate Change. Climate Change 2007: 8e Physical Science Basis: Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, NY, USA, 2007.
  • [2] S. Stisen, I. Sandholt, A. Nørgaard, R. Fensholt, and L. Eklundh, “Estimation of diurnal air temperature using MSG SEVIRI data in west Africa,” Remote Sensing of Environment, vol. 110, no. 2, pp. 262–274, 2007.
  • [3] Akyüz, A, Kumaş, K, Ayan, M, Güngöt, A. (2020). Antalya İli Meteorolojik Verileri Yardımıyla Hava Sıcaklığının Yapay Sinir Ağları Metodu ile Tahmini. Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 10 (1), 146-154. DOI: 10.17714/gumusfenbil.511481.
  • [4] Sri Sankari G. and Valarmathi, Dr.A., 2017. Weather Forecasting with Back Propagation of Neural Network using MATLAB. International Journal of Scientific Research in Computer Science. Engineering and Information Technology, 2(2), 2456-3307.
  • [5] C. J. Willmott and S. M. Robeson, “Climatologically aided interpolation (CAI) of terrestrial air temperature,” International Journal of Climatology, vol. 15, no. 2, pp. 221–229, 1995.
  • [6] Liu, H., Zhou, Q., Zhang, S., & Deng, X. (2019). Estimation of Summer Air Temperature over China Using Himawari-8 AHI and Numerical Weather Prediction Data. Advances in Meteorology, 2019.
  • [7] H. Nieto, I. Sandholt, I. Aguado, E. Chuvieco, and S. Stisen,“Air temperature estimation with MSG-SEVIRI data: calibration and validation of the TVX algorithm for the Iberian Peninsula,” Remote Sensing of Environment, vol. 115, no. 1,pp. 107–116, 2011.
  • [8] A. Benali, A. C. Carvalho, J. P. Nunes, N. Carvalhais, and A. Santos, “Estimating air surface temperature in Portugal using MODIS LST data,” Remote Sensing of Environment, vol. 124, no. 9, pp. 108–121, 2012.
  • [9] P. Noi, J. Degener, and M. Kappas, “Comparison of multiple linear regression, cubist regression, and random forest algorithms to estimate daily air surface temperature from dynamic combinations of MODIS LST data,” Remote Sensing,vol. 9, no. 5, p. 398, 2017.
  • [10] CATAL, C , ECE, K , ARSLAN, B , AKBULUT, A . "Benchmarking of Regression Algorithms and Time Series Analysis Techniques for Sales Forecasting". Balkan Journal of Electrical and Computer Engineering 7 (2019 ): 20-26.
  • [11]Akyüz, A. Ö., Kumaş, K., Ayan, M., & Güngör, A. Antalya İli Meteorolojik Verileri Yardımıyla Hava Sıcaklığının Yapay Sinir Ağları Metodu ile Tahmini. Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 10(1), 146-154.
  • [12] Bilgili, M., & Sahin, B. (2009). Prediction of long-term monthly temperature and rainfall in Turkey. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 32(1), 60-71.
  • [13] AKINCI, T , NOĞAY, H . (2019). APPLICATION OF DECISION TREE METHODS FOR WIND SPEED ESTIMATION. European Journal of Technique (EJT) , 9 (1) , 74-83 . DOI: 10.36222/ejt.558914
  • [14] Bechtel, B., Zakšek, K., Oßenbrügge, J., Kaveckis, G., & Böhner, J. (2017). Towards a satellite based monitoring of urban air temperatures. Sustainable cities and society, 34, 22-31.
  • [15] Yang, Y. Z., Cai, W. H., & Yang, J. (2017). Evaluation of MODIS land surface temperature data to estimate near-surface air temperature in Northeast China. Remote Sensing, 9(5), 410.
  • [16] Good, E. (2015). Daily minimum and maximum surface air temperatures from geostationary satellite data. Journal of Geophysical Research: Atmospheres, 120(6), 2306-2324.
  • [17] Zhang, H., Zhang, F., Ye, M., Che, T., & Zhang, G. (2016). Estimating daily air temperatures over the Tibetan Plateau by dynamically integrating MODIS LST data. Journal of Geophysical Research: Atmospheres, 121(19), 11-425.
  • [18] Xu, Y., Knudby, A., & Ho, H. C. (2014). Estimating daily maximum air temperature from MODIS in British Columbia, Canada. International Journal of Remote Sensing, 35(24), 8108-8121.
  • [19] Tabachnick, B. G., Fidell, L. S., & Ullman, J. B. (2007). Using multivariate statistics (Vol. 5). Boston, MA: Pearson.
  • [20] Sykes, A. O. (1993). An introduction to regression analysis.
  • [21] Uyanık, G. K., & Güler, N. (2013). A study on multiple linear regression analysis. Procedia-Social and Behavioral Sciences, 106(1), 234-240.
  • [22] Abraham, A., Cherukuri, A. K., Melin, P., & Gandhi, N. (Eds.). (2019). Intelligent Systems Design and Applications: 18th International Conference on Intelligent Systems Design and Applications (ISDA 2018) Held in Vellore, India, December 6-8, 2018, Volume 1 (Vol. 940). Springer.
  • [23] Allison, P. D. (1999). Multiple regression: A primer. Pine Forge Press.

AIR TEMPERATURE ESTIMATION FOR BATMAN CITY WITH SIMPLE AND MULTI-LINEAR REGRESSION MODELS UTILIZING METEOROLOGICAL PARAMETERS

Yıl 2020, Cilt: 10 Sayı: 2, 242 - 251, 30.12.2020

Emrullah Acar

https://doi.org/10.36222/ejt.758602

Öz

Determination of air temperature has a significant role in numerous activities such as agriculture, animal husbandry, industry, highway, airlines and railway transportation. In this study, the monthly average of 67 meteorological parameters, which affects the temperature between 2012 and 2017, has taken from Batman Provincial Directorate of Meteorology and the monthly average air temperature of 2017 has been estimated using the meteorological data from 2012-2016. The estimation process has been carried out using two separate scenarios. In the first scenario, each parameter such as monthly average soil temperature, pressure, water vapour pressure, wind speed and relative humidity have been used in the simple linear regression model as input separately and the monthly average temperature has been estimated. In the second scenario, all 67 parameters have been employed in multi linear regression model as inputs and monthly average temperature has been estimated by this way. As a result, very low root mean square error (RMSE) values has been observed in the range of RMSE= [3.30- 10-55] while very high correlation coefficient (R2) values has been computed in the range of R2= [0.10- 0.99].

Anahtar Kelimeler

Linear and Multi-Linear Regression Estimation Air Temperature Meteorology

Kaynakça

  • [1] S. Solomon, Intergovernmental Panel on Climate Change. Climate Change 2007: 8e Physical Science Basis: Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, NY, USA, 2007.
  • [2] S. Stisen, I. Sandholt, A. Nørgaard, R. Fensholt, and L. Eklundh, “Estimation of diurnal air temperature using MSG SEVIRI data in west Africa,” Remote Sensing of Environment, vol. 110, no. 2, pp. 262–274, 2007.
  • [3] Akyüz, A, Kumaş, K, Ayan, M, Güngöt, A. (2020). Antalya İli Meteorolojik Verileri Yardımıyla Hava Sıcaklığının Yapay Sinir Ağları Metodu ile Tahmini. Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 10 (1), 146-154. DOI: 10.17714/gumusfenbil.511481.
  • [4] Sri Sankari G. and Valarmathi, Dr.A., 2017. Weather Forecasting with Back Propagation of Neural Network using MATLAB. International Journal of Scientific Research in Computer Science. Engineering and Information Technology, 2(2), 2456-3307.
  • [5] C. J. Willmott and S. M. Robeson, “Climatologically aided interpolation (CAI) of terrestrial air temperature,” International Journal of Climatology, vol. 15, no. 2, pp. 221–229, 1995.
  • [6] Liu, H., Zhou, Q., Zhang, S., & Deng, X. (2019). Estimation of Summer Air Temperature over China Using Himawari-8 AHI and Numerical Weather Prediction Data. Advances in Meteorology, 2019.
  • [7] H. Nieto, I. Sandholt, I. Aguado, E. Chuvieco, and S. Stisen,“Air temperature estimation with MSG-SEVIRI data: calibration and validation of the TVX algorithm for the Iberian Peninsula,” Remote Sensing of Environment, vol. 115, no. 1,pp. 107–116, 2011.
  • [8] A. Benali, A. C. Carvalho, J. P. Nunes, N. Carvalhais, and A. Santos, “Estimating air surface temperature in Portugal using MODIS LST data,” Remote Sensing of Environment, vol. 124, no. 9, pp. 108–121, 2012.
  • [9] P. Noi, J. Degener, and M. Kappas, “Comparison of multiple linear regression, cubist regression, and random forest algorithms to estimate daily air surface temperature from dynamic combinations of MODIS LST data,” Remote Sensing,vol. 9, no. 5, p. 398, 2017.
  • [10] CATAL, C , ECE, K , ARSLAN, B , AKBULUT, A . "Benchmarking of Regression Algorithms and Time Series Analysis Techniques for Sales Forecasting". Balkan Journal of Electrical and Computer Engineering 7 (2019 ): 20-26.
  • [11]Akyüz, A. Ö., Kumaş, K., Ayan, M., & Güngör, A. Antalya İli Meteorolojik Verileri Yardımıyla Hava Sıcaklığının Yapay Sinir Ağları Metodu ile Tahmini. Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 10(1), 146-154.
  • [12] Bilgili, M., & Sahin, B. (2009). Prediction of long-term monthly temperature and rainfall in Turkey. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 32(1), 60-71.
  • [13] AKINCI, T , NOĞAY, H . (2019). APPLICATION OF DECISION TREE METHODS FOR WIND SPEED ESTIMATION. European Journal of Technique (EJT) , 9 (1) , 74-83 . DOI: 10.36222/ejt.558914
  • [14] Bechtel, B., Zakšek, K., Oßenbrügge, J., Kaveckis, G., & Böhner, J. (2017). Towards a satellite based monitoring of urban air temperatures. Sustainable cities and society, 34, 22-31.
  • [15] Yang, Y. Z., Cai, W. H., & Yang, J. (2017). Evaluation of MODIS land surface temperature data to estimate near-surface air temperature in Northeast China. Remote Sensing, 9(5), 410.
  • [16] Good, E. (2015). Daily minimum and maximum surface air temperatures from geostationary satellite data. Journal of Geophysical Research: Atmospheres, 120(6), 2306-2324.
  • [17] Zhang, H., Zhang, F., Ye, M., Che, T., & Zhang, G. (2016). Estimating daily air temperatures over the Tibetan Plateau by dynamically integrating MODIS LST data. Journal of Geophysical Research: Atmospheres, 121(19), 11-425.
  • [18] Xu, Y., Knudby, A., & Ho, H. C. (2014). Estimating daily maximum air temperature from MODIS in British Columbia, Canada. International Journal of Remote Sensing, 35(24), 8108-8121.
  • [19] Tabachnick, B. G., Fidell, L. S., & Ullman, J. B. (2007). Using multivariate statistics (Vol. 5). Boston, MA: Pearson.
  • [20] Sykes, A. O. (1993). An introduction to regression analysis.
  • [21] Uyanık, G. K., & Güler, N. (2013). A study on multiple linear regression analysis. Procedia-Social and Behavioral Sciences, 106(1), 234-240.
  • [22] Abraham, A., Cherukuri, A. K., Melin, P., & Gandhi, N. (Eds.). (2019). Intelligent Systems Design and Applications: 18th International Conference on Intelligent Systems Design and Applications (ISDA 2018) Held in Vellore, India, December 6-8, 2018, Volume 1 (Vol. 940). Springer.
  • [23] Allison, P. D. (1999). Multiple regression: A primer. Pine Forge Press.
Toplam 23 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Elektrik Mühendisliği
Bölüm Araştırma Makalesi
Yazarlar

Emrullah Acar 0000-0002-1897-9830

Yayımlanma Tarihi 30 Aralık 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 10 Sayı: 2

Kaynak Göster

APA Acar, E. (2020). AIR TEMPERATURE ESTIMATION FOR BATMAN CITY WITH SIMPLE AND MULTI-LINEAR REGRESSION MODELS UTILIZING METEOROLOGICAL PARAMETERS. European Journal of Technique (EJT), 10(2), 242-251. https://doi.org/10.36222/ejt.758602

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