Polish State Forests [500 477, 500 446]

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Polish State Forests [500 477, 500 446]

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Modeling and Mapping of Forest Fire Occurrence in the Lower Silesian Voivodeship of Poland Based on Machine Learning Methods

Milanović, Slobodan; Kaczmarowski, Jan; Ciesielski, Mariusz; Trailović, Zoran; Mielcarek, Milosz; Szczygiel, Ryszard; Kwiatkowski, Miroslaw; Balazy, Radomir; Zasada, Michal; Milanović, Slađan D.

(2023)

TY  - JOUR
AU  - Milanović, Slobodan
AU  - Kaczmarowski, Jan
AU  - Ciesielski, Mariusz
AU  - Trailović, Zoran
AU  - Mielcarek, Milosz
AU  - Szczygiel, Ryszard
AU  - Kwiatkowski, Miroslaw
AU  - Balazy, Radomir
AU  - Zasada, Michal
AU  - Milanović, Slađan D.
PY  - 2023
UR  - https://omorika.sfb.bg.ac.rs/handle/123456789/1429
AB  - In recent years, forest fires have become an important issue in Central Europe. To model the probability of the occurrence of forest fires in the Lower Silesian Voivodeship of Poland, historical fire data and several types of predictors were collected or generated, including topographic, vegetation, climatic, and anthropogenic features. The main objectives of this study were to determine the importance of the predictors of forest fire occurrence and to map the probability of forest fire occurrence. The H2O driverless artificial intelligence (DAI) cloud platform was used to model forest fire probability. The gradient boosted machine (GBM) and random forest (RF) methods were applied to assess the probability of forest fire occurrence. Evaluation the importance of the variables was performed using the H2O platform permutation method. The most important variables were the presence of coniferous forest and the distance to agricultural land according to the GBM and RF methods, respectively. Model validation was conducted using receiver operating characteristic (ROC) analysis. The areas under the curve (AUCs) of the ROC plots from the GBM and RF models were 83.3% and 81.3%, respectively. Based on the results obtained, the GBM model can be recommended for the mapping of forest fire occurrence in the study area.
T2  - Forests
T1  - Modeling and Mapping of Forest Fire Occurrence in the Lower Silesian Voivodeship of Poland Based on Machine Learning Methods
IS  - 1
VL  - 14
DO  - 10.3390/f14010046
UR  - conv_1680
ER  - 
@article{
author = "Milanović, Slobodan and Kaczmarowski, Jan and Ciesielski, Mariusz and Trailović, Zoran and Mielcarek, Milosz and Szczygiel, Ryszard and Kwiatkowski, Miroslaw and Balazy, Radomir and Zasada, Michal and Milanović, Slađan D.",
year = "2023",
abstract = "In recent years, forest fires have become an important issue in Central Europe. To model the probability of the occurrence of forest fires in the Lower Silesian Voivodeship of Poland, historical fire data and several types of predictors were collected or generated, including topographic, vegetation, climatic, and anthropogenic features. The main objectives of this study were to determine the importance of the predictors of forest fire occurrence and to map the probability of forest fire occurrence. The H2O driverless artificial intelligence (DAI) cloud platform was used to model forest fire probability. The gradient boosted machine (GBM) and random forest (RF) methods were applied to assess the probability of forest fire occurrence. Evaluation the importance of the variables was performed using the H2O platform permutation method. The most important variables were the presence of coniferous forest and the distance to agricultural land according to the GBM and RF methods, respectively. Model validation was conducted using receiver operating characteristic (ROC) analysis. The areas under the curve (AUCs) of the ROC plots from the GBM and RF models were 83.3% and 81.3%, respectively. Based on the results obtained, the GBM model can be recommended for the mapping of forest fire occurrence in the study area.",
journal = "Forests",
title = "Modeling and Mapping of Forest Fire Occurrence in the Lower Silesian Voivodeship of Poland Based on Machine Learning Methods",
number = "1",
volume = "14",
doi = "10.3390/f14010046",
url = "conv_1680"
}
Milanović, S., Kaczmarowski, J., Ciesielski, M., Trailović, Z., Mielcarek, M., Szczygiel, R., Kwiatkowski, M., Balazy, R., Zasada, M.,& Milanović, S. D.. (2023). Modeling and Mapping of Forest Fire Occurrence in the Lower Silesian Voivodeship of Poland Based on Machine Learning Methods. in Forests, 14(1).
https://doi.org/10.3390/f14010046
conv_1680
Milanović S, Kaczmarowski J, Ciesielski M, Trailović Z, Mielcarek M, Szczygiel R, Kwiatkowski M, Balazy R, Zasada M, Milanović SD. Modeling and Mapping of Forest Fire Occurrence in the Lower Silesian Voivodeship of Poland Based on Machine Learning Methods. in Forests. 2023;14(1).
doi:10.3390/f14010046
conv_1680 .
Milanović, Slobodan, Kaczmarowski, Jan, Ciesielski, Mariusz, Trailović, Zoran, Mielcarek, Milosz, Szczygiel, Ryszard, Kwiatkowski, Miroslaw, Balazy, Radomir, Zasada, Michal, Milanović, Slađan D., "Modeling and Mapping of Forest Fire Occurrence in the Lower Silesian Voivodeship of Poland Based on Machine Learning Methods" in Forests, 14, no. 1 (2023),
https://doi.org/10.3390/f14010046 .,
conv_1680 .
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