Using multiple linear regression model to estimate thunderstorm activity

Wayan Suparta, W. S. Putro

Research output: Contribution to journalArticle

Abstract

This paper is aimed to develop a numerical model with the use of a nonlinear model to estimate the thunderstorm activity. Meteorological data such as Pressure (P), Temperature (T), Relative Humidity (H), cloud (C), Precipitable Water Vapor (PWV), and precipitation on a daily basis were used in the proposed method. The model was constructed with six configurations of input and one target output. The output tested in this work is the thunderstorm event when one-year data is used. Results showed that the model works well in estimating thunderstorm activities with the maximum epoch reaching 1000 iterations and the percent error was found below 50%. The model also found that the thunderstorm activities in May and October are detected higher than the other months due to the inter-monsoon season.

Original languageEnglish
Article number012023
JournalIOP Conference Series: Materials Science and Engineering
Volume185
Issue number1
DOIs
Publication statusPublished - 22 Mar 2017

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Thunderstorms
Linear regression
Steam
Water vapor
Numerical models
Atmospheric humidity
Temperature

ASJC Scopus subject areas

  • Materials Science(all)
  • Engineering(all)

Cite this

Using multiple linear regression model to estimate thunderstorm activity. / Suparta, Wayan; Putro, W. S.

In: IOP Conference Series: Materials Science and Engineering, Vol. 185, No. 1, 012023, 22.03.2017.

Research output: Contribution to journalArticle

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