Probabilistic Hotspot Prediction Model Based on Bayesian Inference Using Precipitation, Relative Dry Spells, ENSO and IOD

• Published in: Atmosphere Vol. 14; no. 2; p. 286
• Main Authors: Ardiyani, Evi, Nurdiati, Sri, Sopaheluwakan, Ardhasena, Septiawan, Pandu, Najib, Mohamad Khoirun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2023
• Subjects: Accuracy; Bayesian; Bayesian analysis; Bayesian theory; Climate; Climate change; Climate models; Dry spells; El Nino; El Nino phenomena; El Nino-Southern Oscillation event; Emergency communications systems; Environmental aspects; Forest fires; Global warming; Hot spots; Indonesia; Influence; La Nina; Modelling; Performance evaluation; Precipitation; Precipitation (Meteorology); prediction; Prediction models; probabilistic; Probability theory; Southern Oscillation; Statistical inference; Temperature; Wildfires; Indonesia; Indian Ocean; Sumatra
• ISSN: 2073-4433; 2073-4433
• DOI: 10.3390/atmos14020286

Increasing global warming can potentially increase the intensity of ENSO and IOD extreme phenomena in the future, which could increase the potential for wildfires. This study aims to develop a hotspot prediction model in the Kalimantan region using climate indicators such as precipitation and its derivatives, ENSO and IOD. The hotspot prediction model was developed using Principal Model Analysis (PMA) as the initial model basis. The overall model performance is evaluated using the concept of Cross-Validation. Furthermore, the model’s performance will be improved using the Bayesian Inference principle so that the average performance increases from 28.6% to 61.1% based on the model’s coefficient of determination (R2). The character of each year in the model development process is also evaluated using the concept of cross validation. Since the climate indicator we used was integrated with the ENSO and IOD index, model performance is strongly influenced by the ENSO and IOD phenomena. To obtain better performance when estimating future forest fires (related to El Niño and positive IOD), years with a high number of hotspots and coinciding with the occurrence of El Niño and IOD are better used as early model years (PMA). However, the model tends to overestimate the hotspot value, especially with a lower strength El Niño and positive IOD. Therefore, years with a low number of hotspots, as in normal years and La Niña, are better used in the model performance improvement stage (Bayesian Inference) to correct the overestimation.