<abstract xmlns="http://www.w3.org/1999/xhtml">

Rawa Aopa National Park has experienced a severe wildfire. These fires are affected by several factors, including topography, meteorology, vegetation, and source of fire. This study uses a Machine Learning approach based on re-sampling methods (e.g. crossvalidation, bootstrap, and random subsampling) to evaluate, and improve the performance of twelve basic Machine Learning algorithms: Generalized Linear Model, Support Vector Machine, Random Forest, Boosted Regression Trees, Classification And Regression Tree, Multivariate Adaptive Regression Splines, Mixture Discriminate Analysis, Flexible Discriminant Analysis, Maximum Entropy, Maximum Likelihood, Radial Basis Function, and Multi-Layer Perceptron, analyze the causes of wildfires, and the correlation between variables. The model is evaluated by Area Under Curve, Correlation, True Skill Statistics, and Deviance. The evaluation results show that Bt-RF has a good performance in predicting wildfire susceptibility in TNRAW with AUC=0.98, COR=0.96, TSS=0.97, and Deviance=0.15. An area of 644.88 km2 or the equivalent of 59.82% of the area is a wildfire susceptibility area with the concentration of fires occurring in the savanna ecosystem which is around 245.12 km2 or the equivalent of 88.95% of the jungle zone. Among the 17 parameters that cause fires, this area is strongly influenced by Maximum Temperature, Land Use Land Cover, and Distance from Road. There is a strong correlation between soil and distance from settlements = 0.96.
</abstract>

Rawa Aopa National Park has experienced a severe wildfire. These fires are affected by several factors, including topography, meteorology, vegetation, and source of fire. This study uses a Machine Learning approach based on re-sampling methods (e.g. crossvalidation, bootstrap, and random subsampling) to evaluate, and improve the performance of twelve basic Machine Learning algorithms: Generalized Linear Model, Support Vector Machine, Random Forest, Boosted Regression Trees, Classification And Regression Tree, Multivariate Adaptive Regression Splines, Mixture Discriminate Analysis, Flexible Discriminant Analysis, Maximum Entropy, Maximum Likelihood, Radial Basis Function, and Multi-Layer Perceptron, analyze the causes of wildfires, and the correlation between variables. The model is evaluated by Area Under Curve, Correlation, True Skill Statistics, and Deviance. The evaluation results show that Bt-RF has a good performance in predicting wildfire susceptibility in TNRAW with AUC=0.98, COR=0.96, TSS=0.97, and Deviance=0.15. An area of 644.88 km2 or the equivalent of 59.82% of the area is a wildfire susceptibility area with the concentration of fires occurring in the savanna ecosystem which is around 245.12 km2 or the equivalent of 88.95% of the jungle zone. Among the 17 parameters that cause fires, this area is strongly influenced by Maximum Temperature, Land Use Land Cover, and Distance from Road. There is a strong correlation between soil and distance from settlements = 0.96.

Spatial model of wildfire susceptibility using Machine Learning approaches on Rawa Aopa Watumohai National Park, Indonesia

Faculty of Mathematics and Natural Sciences, Universitas Indonesia

Faculty of Social Sciences, Universitas Negeri Malang

GeoScape

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Rawa Aopa National Park has experienced a severe wildfire. These fires are affected by several factors, including topography, meteorology, vegetation, and...

Spatial model of wildfire susceptibility using Machine Learning approaches on Rawa Aopa Watumohai National Park, Indonesia

Pubblicato online: 29 giu 2024

Pagine: 1 - 20

Ricevuto: 08 set 2023

Accettato: 29 mar 2024

DOI: https://doi.org/10.2478/geosc-2024-0001

Parole chiave
Wildfire susceptibility, Area under curve, Machine learning, Resampling method, Rawa Aopa Watumohai National Park, Conservation area

© 2024 Septianto Aldiansyah et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Spatial model of wildfire susceptibility using Machine Learning approaches on Rawa Aopa Watumohai National Park, Indonesia

Pubblicato online: 29 giu 2024

Pagine: 1 - 20

Ricevuto: 08 set 2023

Accettato: 29 mar 2024

DOI: https://doi.org/10.2478/geosc-2024-0001

Parole chiaveWildfire susceptibility, Area under curve, Machine learning, Resampling method, Rawa Aopa Watumohai National Park, Conservation area

© 2024 Septianto Aldiansyah et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
Wildfire susceptibility, Area under curve, Machine learning, Resampling method, Rawa Aopa Watumohai National Park, Conservation area