The Analisys of Daily Temparature in Pekanbaru City Using Weibull, Gamma, and Nakagami Distribution

  IJMTT-book-cover
 
International Journal of Mathematics Trends and Technology (IJMTT)
 
© 2021 by IJMTT Journal
Volume-67 Issue-5
Year of Publication : 2021
Authors : Martha Sri Pramadani, Arisman Adnan, Rado Yendra
  10.14445/22315373/IJMTT-V67I5P508

MLA

MLA Style: Martha Sri Pramadani, Arisman Adnan, Rado Yendra "The Analisys of Daily Temparature in Pekanbaru City Using Weibull, Gamma, and Nakagami Distribution" International Journal of Mathematics Trends and Technology 67.5 (2021):82-85. 

APA Style: Martha Sri Pramadani, Arisman Adnan, Rado Yendra(2021). The Analisys of Daily Temparature in Pekanbaru City Using Weibull, Gamma, and Nakagami Distribution International Journal of Mathematics Trends and Technology, 82-85.

Abstract
Changes in temperature are one of the consequences of climate change. Ecosystems and various sectors of human activity are sensitive to high and low temperature, especially if it occurs over a long time. Pekanbaru city is a city in Riau Province, Indonesia, which has tropical climates while daily temperatures varying from 72 F-97 F. This study was focussed on reducing the effects of high temperatures, such as global changes through the behavior of daily temperature data. Main goal of this study is to find the best fitting distribution to the daily temperature measured for the years 1990-2020. The Weibull (W) , Gamma (G) and the Nakagami (N) distributions are fitted to data corresponding to the methods to describe the daily of temperature. Graphical tests of probability density function (pdf), cumulative distribution function (cdf), quantile function (qf) and numerical criteria of Relative Root Mean Square Error (RRMSE) and relative absolute square error (RASE) were used to select the best fit model. In most cases, graphical examine have the same result between G and N distribution but their RRMSE and RASE result different. Finally, we found that the N distribution is the most suitable distribution for modeling the daily temperature of Pekanbaru city.

Reference

[1] A. Munoz, E. F. Snchez-beda, and J. Marn, Short-term forecasting in power system: A guide tour, in Hanbook of power system II, Springer-Verlag, Berlin Hedelberg, (2010), 129-160.
[2] B. Lyon, Southern Africa summer drought and heat waves: Observations and coupled model behavior, Journal of Climate, 22(22) (2009), 6033-6046.
[3] D. Jaruskova dan M. Rencova, Analysis of annual maximal and minimal temperatures for some Eropean cities by change point methods, Envirometrics, 19 (2008), 221-233.
[4] F. W. zwiers dan V. V. Kharin, Change in extremes of the climate simulated by CCC under CO2 dobling, Journal of Climate, 11 (1997), 2200-2222. High resolution fiber distributed measurements with coherent OFDR, in Proc. ECOC’00, paper 11(3)(2000) 109-125.
[5] H. Hahn, S. Meyer-Nieberg, and S. Pickl, Electrical load forecasting methods: Tools for decision making, European Journal of Operational Research, 3 (2009), 902-907.
[6] H. N. Bystrom, Extreme value theory and extremely large electricity price changes, International Review of Economics and Finance, 1 (2005), 41-55.
[7] K. E. Trenbert and D. J. Shea, Relationships between precipitation and surface temperature, Res. Let., 32, L14703, doi:10.1029/2005GL022760, (2005).
[8] K. Pangaluru, I. Velicogna, T. C. Sutterley, Y. Mohajerani, E. Ciraci, J. Sumpolli, and S. V. B. Rao, Estimating change of temperatures and precipitation extremes in India using the Generalized Extreme Value (GEV) distribution, Hydrology and Earth System Sciences, (2018), 1-33.
[9] NOAA, National Centers for Environmental Information, https://www.ncei.noaa.gov accessed 22 Februari 2019.
[10] P. Guttorp and J. Xu, Climate change, trends in extremes, and model assessment for a long temperature time series from Sweden, Environmetrics, (2009), 456-463.
[11] Pekanbarau. Go. Id, Wilayah Geografis, 13 Februari 2021, https://www.pekanbaru.go.id/index.php/p/menu/profil-kota/wilayah-geografis accessed 18 Maret 2021.
[12] V. V. Kharin dan F. W. Zwiers, Estimating extremes in transient climate change simulations, Journal of Climate, 18 (2004), 1156-1173.
[13] V. V. Kharin, F. W. Zwiers, X. Zhang, and M. Wehner, Change in temperature and precipitation extremes in the CMIP5 ensemble, Journal of Climate, 119 (2013), 345-357.
[14] W. Steffen, L. Hughes, and S. Parkins, Heat waves: Hotter, longer, more often, Climate Council of Australia Limited, Second Major Technical Report of the Climate Council, Sydney, (2009).

Keywords : climate change, best fitting distribution, Weibull distribution, gamma distribution, Nakagami distribution