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

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


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.

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.


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