Open AccessRainfall analysis in the Indian Ocean by using 6-States Markov Chain Model
Author(s) -
Miftahuddin,
Maulidawani,
Ichsan Setiawan,
Yopi Ilhamsyah,
Fadhli
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/429/1/012012
Subject(s) - markov chain , balance equation , stochastic matrix , mathematics , state (computer science) , continuous time markov chain , transition rate matrix , intensity (physics) , sea state , markov model , statistical physics , meteorology , environmental science , statistics , physics , thermodynamics , quantum mechanics , algorithm
In this study, we used Markov chain approach to analyze rainfall dataset from one buoy (4N90E) in the eastern Equatorial Indian Ocean. This study aims to determine the opportunity for transition (displacement) of daily rainfall intensity, where there are six states or conditions of rainfall intensity, i.e. no rain, very weak, weak rain, moderate rain, heavy rain, and very heavy rain. The Markov Chain method used is the Chapman-Kolmogorov equation and the steady state equation. The investigation of the 6-states in Markov chain model show that dynamic probability of transition state for rainfall data is reflexive properties majority. By using the model, it is concluded that the transition rate matrix of the largest transition probability in the area of 4N90E occurs at the transition from state-1 to state-1 as much is 0.72 and state-2 to state-2 is 0.60. The transition probability value becomes 0.5184 and 0.36 for the same state dominant of two periods P 2 . The use of the 1st order Markov chain is better than 2nd order.