search for




 

The method of rainfall prediction for road weather service
Journal of the Korean Data & Information Science Society 2018;29:403-13
Published online March 31, 2018
© 2018 Korean Data and Information Science Society.

Miyeon Yang1 · Sanghoo Yoon2

1Department of Statistics, Daegu University
2Division of Mathematics and Big Data Science, Daegu University & Institute of Basic Science, Daegu University
Correspondence to: Assistant professor, Division of Mathematics and big data science, Daegu University, Gyeongbuk 68453, Korea. E-mail: statstar@daegu.ac.kr
Received February 17, 2018; Revised March 13, 2018; Accepted March 15, 2018.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
It is known that a driving condition is affected by weather. For example, rain causes black ice while a temperature is low. It is necessary to predict the amount of rainfall to the standard node link for safe driving. However, it is difficult to obtain the amount of rainfall on the road directly, because rain gauge sensors are not installed on road. This study is dealt with the method of producing rainfall collected from 190 AWS in Seoul. An inverse distance weighting method and kriging were considered as an interpolation method to predict rainfall amount. The prediction performance was evaluated by BIAS, RMSE, MAE, and correlation coefficient. In addition, the difference of predicted performance according to the spatial range and time resolution when the rain came. As a result, the inverse distance weighting method showed good performance when spatial range was local and time resolution were high, and ordinary kriging with nugget effect showed good performance when spatial range was global and time resolution were low.
Keywords : Inverse distance weight, kriging, spatial interpolation, standard node link.