Analysis of Rainfall Transition Probability Using Markov Chain Method
DOI:
https://doi.org/10.32734/jormtt.v7i2.21719Keywords:
Markov chain, rainfall, climatology, geophysicsAbstract
This research applies the Markov Chain model to examine daily rainfall data in Medan City. Markov chain is one of the methods used for forecasting in various fields, such as economics, industry, and climate. This research uses secondary data of daily rainfall intensity from the BMKG Station of the Center for Meteorology, Climatology and Geophysics Region I. The purpose of this research is to determine the transition probability (probability of transition). This study aims to determine the chance of transition (displacement) of daily rainfall intensity, There are four conditions of rainfall intensity that are categorized, namely no rain, light rain, moderate rain, and heavy rain. The Markov Chain method used is the Champman- Kolmogorov Equation and the steady state equation. The fixed probability of not raining is 59.16%, the fixed probability of light rain is 17.67%, the fixed probability of moderate rain is 16.28%, and the fixed probability of heavy rain is 6.86%.
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