A Hidden Markov Model for Forecasting Rainfall Data Availability at the Weather Station in West Sumatra

Abstract

Indonesia is a maritime continent in Southeast Asian, laying between Indian Ocean and Pacific Ocean. This position intensely affects the level of rainfall in Indonesia, especially West Sumatra. The availability of rainfall data can form a Markov chain which its state is not able to be observed directly (hidden), is called the Hidden Markov Model (HMM). The purposes of this research are to predict the hidden state of the availability of rainfall data using decoding problems and to find the best state sequence (optimal) by using Viterbi Algorithm, and also to predict probability for the availability of rainfall data in the future by using the Baum Welch Algorithm in the Hidden Markov Model. This research uses secondary data with a period of one day from the availability of rainfall data at the Minangkabau Meteorological Station, Padang Pariaman Climatology Station, and Silaing Bawah Geophysics Station from January 2018 to July 2019. The results of the prediction show that the Hidden Markov Model can be used to predict the probability of rainfall data availability. The results for the availability of the highest rainfall data for one day ahead is at the Padang Pariaman Climatology Station, with a probability of 0.36, followed by Minangkabau Meteorological Station is 0.35, and Silaing Bawah Geophysics station is 0.29. The result has shown for the next one day period the probability of rainfall data available from the three stations will be available following the Viterbi algorithm.