Affiliation:
1. Department of Mathematics, Mathematics and Natural Science Faculty, IPB University, Bogor 16680, Indonesia
2. Center for Applied Climate Services, Agency for Meteorology Climatology and Geophysics, Jakarta 10720, Indonesia
Abstract
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.
Funder
Directorate General of Higher Education, Ministry of Education, Culture, Research, and Technology
Subject
Atmospheric Science,Environmental Science (miscellaneous)
Reference69 articles.
1. Heterogeneous Correlation Map Between Estimated ENSO And IOD From ERA5 And Hotspot In Indonesia;Nurdiati;Jambura Geosci. Rev.,2021
2. Analysis of Forest Deforestation in Riau Province Using Polarimetric Method in Remote Sensing;Shafitri;J. Geod. Undip.,2018
3. Quantifying ENSO and IOD Impact to Hotspot in Indonesia Based on Heterogeneous Correlation Map (HCM);Dafri;J. Phys. Conf. Ser.,2021
4. Sills, E.O. (2015). REDD+ on the Ground: A Case Book of Subnational Initiatives across the Globe, Center for International Forestry Research (CIFOR).
5. (2022, November 30). Ministry of Environment and Forestry of The Republic of Indonesia Sipongi.menhlk.go.id. Available online: Sipongi.menhlk.go.id.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献