TY - GEN
T1 - Peatland Data Fusion for Forest Fire Susceptibility Prediction Using Machine Learning
AU - Hidayanto, Nurdeka
AU - Saputro, Adhi Harmoko
AU - Nuryanto, Danang Eko
N1 - Funding Information:
This project was supported by internal scholarship from the Agency for Meteorology, Climatology, and Geophysics (BMKG).
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Forest fires have been a severe hydrometeorological hazard during the dry season in Indonesia. Pulang Pisau Regency in Central Kalimantan has become one of the most forest fires affected areas during the 2015 El Nino event. Based on MODIS data, more than 120.000 hotspots have been recorded between 2014 and 2019. Previous studies concluded that peatlands act as contributing factor to forest fires in this country. This study proposed the peat-effect on the development of machine learning models for forest fire susceptibility (FFS), which can be alternative tool to support forest fire disaster management. In addition to the peat effect, such as elevation, slope, Normalized Difference Vegetation Index (NDVI), rainfall, distance from the road network, and distance from the residents also analyzed. Those variables were divided into training (2014-2018) and testing (2019). Random Forest (RF), Support Vector Classifications (SVC), and Gradient Boosting Classification (GBC) models were used to build the FFS map. The experiment results showed an increase in Area Under Curve (AUC) from 0.84-0.87 to 0.87-0.88 with the addition of the peat depth variable. The complete test resulted in the highest accuracy of 0.80 in the RF and SVC.
AB - Forest fires have been a severe hydrometeorological hazard during the dry season in Indonesia. Pulang Pisau Regency in Central Kalimantan has become one of the most forest fires affected areas during the 2015 El Nino event. Based on MODIS data, more than 120.000 hotspots have been recorded between 2014 and 2019. Previous studies concluded that peatlands act as contributing factor to forest fires in this country. This study proposed the peat-effect on the development of machine learning models for forest fire susceptibility (FFS), which can be alternative tool to support forest fire disaster management. In addition to the peat effect, such as elevation, slope, Normalized Difference Vegetation Index (NDVI), rainfall, distance from the road network, and distance from the residents also analyzed. Those variables were divided into training (2014-2018) and testing (2019). Random Forest (RF), Support Vector Classifications (SVC), and Gradient Boosting Classification (GBC) models were used to build the FFS map. The experiment results showed an increase in Area Under Curve (AUC) from 0.84-0.87 to 0.87-0.88 with the addition of the peat depth variable. The complete test resulted in the highest accuracy of 0.80 in the RF and SVC.
KW - Forest fire
KW - Machine learning
KW - Peatlands
KW - Prediction
KW - Susceptibility
UR - http://www.scopus.com/inward/record.url?scp=85126663896&partnerID=8YFLogxK
U2 - 10.1109/ISRITI54043.2021.9702762
DO - 10.1109/ISRITI54043.2021.9702762
M3 - Conference contribution
AN - SCOPUS:85126663896
T3 - 2021 4th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2021
SP - 544
EP - 549
BT - 2021 4th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2021
Y2 - 16 December 2021
ER -