Markov Chain Application to Daily Rainfall Data in Semarang City

Authors

  • Nahda Maesya Tsani Universitas Negeri Padang
  • Dony Permana Universitas Negeri Padang
  • Yenni Kurniawati Universitas Negeri Padang
  • Admi Salma Universitas Negeri Padang

DOI:

https://doi.org/10.24036/ujsds/vol2-iss3/189

Keywords:

Markov Chains, Rainfall, Steady State

Abstract

Rainfall is a measure of the amount of water that falls on the earth's surface in a given period of time. High rainfall can cause flooding in certain areas, while low rainfall can leave areas vulnerable to drought. Semarang City is one of the largest cities in Java Island that is often hit by floods. Efforts can be made to anticipate the risk of flooding, one of which is by studying the pattern of rainfall. This study will determine the chances of rainfall transition in Semarang City in steady state conditions using Markov chains. The results are expected to be used to anticipate the risk of flooding in Semarang City. The probability of daily rainfall transition in Semarang City in each state for the next period of time is 90.5% chance of staying in the light rain state, 7.97% chance of staying in the medium rain state and 1.50% chance of staying in the heavy rain state.

Published

2024-08-24

How to Cite

Tsani, N. M., Permana, D., Kurniawati, Y., & Salma, A. (2024). Markov Chain Application to Daily Rainfall Data in Semarang City. UNP Journal of Statistics and Data Science, 2(3), 304–309. https://doi.org/10.24036/ujsds/vol2-iss3/189