Probabilistic Prediction Model Using Bayesian Inference in Climate Field: A Systematic Literature

Evi Ardiyani; Sri Nurdiati; Ardhasena Sopaheluwakan; Mohamad Khoirun Najib; Fadillah Rohimahastuti

doi:10.31764/jtam.v7i3.13651

Probabilistic Prediction Model Using Bayesian Inference in Climate Field: A Systematic Literature

Evi Ardiyani, Sri Nurdiati, Ardhasena Sopaheluwakan, Mohamad Khoirun Najib, Fadillah Rohimahastuti

Abstract

Wildfires occur repeatedly every year and have a negative impact on natural ecosystems. Anticipation of wildfires is very necessary, therefore a prediction model is needed that can produce predictions with a good level of accuracy. One approach to develop probabilistic prediction models is Bayesian inference. The purpose of this research is to review the methods that can be used in developing probabilistic prediction models using the Bayesian approach. The methodology used is Systematic Literature Review (SLR) which can be used to provide a comprehensive review of Bayesian inference research in developing probabilistic prediction models. The research strategy used was the Boolean Technique applied to database sources including Scopus, IEEE Xplore, and ArXiv. The articles used have novelty and ease of explanation of Bayesian methods, especially predictions in the field of climate so that articles are selected based on inclusion and exclusion criteria. The results show that probabilistic models can provide more accurate results than deterministic models. The Bayesian Model Averaging (BMA) method is a widely used method because it is easy to implement and develop so that the prediction results can be more accurate. The development of probabilistic prediction models with a Bayesian approach has great potential to grow as seen from the development of the number of research publications over the past 5 years. The research position of probabilistic prediction models with Bayesian approaches in the field of climate is dominated by the research community in China with the main problems related to hydrology.

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Keywords

Probabilistics; Prediction; Bayesian; Climate.

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DOI: https://doi.org/10.31764/jtam.v7i3.13651