Markov Chain Model for Daily Rainfall Modeling in Bengkulu City

Ramya Rachmawati; Firdaus; Ratna Widayati; Siska Yosmar; Risfa Fadila; Ajeng Siti Nurul Kharima

doi:10.33541/edumatsains.v10i4.8000

Authors

Ramya Rachmawati The University of Bengkulu
Firdaus
Ratna Widayati
Siska Yosmar
Risfa Fadila
Ajeng Siti Nurul Kharima

DOI:

https://doi.org/10.33541/edumatsains.v10i4.8000

Keywords:

Markov Chains, rainfall prediction, stochastic, weather

Abstract

Bengkulu City is a region in Indonesia that is particularly vulnerable to shifts in rainfall patterns, which can have significant impacts on the agricultural sector, water resource management, and disaster mitigation. The uncertainty in rainfall patterns often complicates long-term planning. Hence, it is necessary to adopt a statistical approach that can model and predict rainfall characteristics with greater accuracy. This research aims to develop a Markov Chain model to represent the daily rainfall regime in Bengkulu City. The daily rainfall data are categorized into rainfall intensity states, namely: no rain, light, moderate, heavy, or very heavy rainfall. By leveraging historical daily rainfall data, this model is expected to identify the transition probabilities between these states. Based on the obtained steady-state probabilities, it can be concluded that regardless of today’s rainfall condition in Bengkulu City, the long-term probabilities for tomorrow’s weather are as follows: 38% for no rain, 43% for light rain, 13.8% for moderate rain, 4.2% for heavy rain, and 1% for very heavy rain.

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