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 .

TY  - JOUR
AU  - Milanović, Slobodan
AU  - Mihailović, Dragutin T.
AU  - Lakićević, Milena
AU  - Đurđević, Vladimir
AU  - Malinović-Milicević, Slavica
AU  - Milanović, Slađan D.
AU  - Trailović, Zoran
PY  - 2023
UR  - https://omorika.sfb.bg.ac.rs/handle/123456789/1438
AB  - The impact of climate change on insect pests is an emerging topic in forestry and forest science. This study investigates the relationships between two broadleaved forest pests - spongy moth (Lymantria dispar L.) and brown-tail moth (Euproctis chry-sorrhoea L.) - and oaks (Quercus sp.) as their hosts. Oak forests cover almost one-third of the total forest area of Serbia and are ecologicallyvery valuable, but at the same time vulnerable, as being affected in adverse ways by several primary pests and pathogens. Since 1862, Serbia experienced several extremely large outbreaks of spongy moth with more than a hundred thousand hectares completely defoliated each time, while brown-tail moth occurred periodically with a much lower spatial extent. The aim of this research was to investigate the effect of UV radiation (UVR) and air tempe-rature on spongy moth and brown-tail moth in Serbian forests. We used simulations of the coupled regional climate model EBU-POM (Eta Belgrade University-Princeton Ocean Model) for the A1B scenario for the period 2001-2030 as main input and diffe-rent statistical methods to explore relationships between observations of pest spread and climate change impacts. Our results suggest(i) increasing the areas affected by spongy moth due to its sensitivity on UVR in May, and(ii) altitudinal spreading of brown-tail moth population up to 800 - 1000 m.This research indicates that in situ forest observations in Serbia are not only affected by climate change, but also by the combined effect of climate on forest pests. For fur-ther research, we recommend exploring other forest stressors or dieback phenomena in European forests by applying the same or similar regional climate model dataset.
T2  - Austrian Journal of Forest Science
T1  - Impact of UV radiation and temperature on the spongy moth and the brown-tail moth in Serbia
EP  - 20
IS  - 1
SP  - 1
VL  - 140
UR  - conv_1697
ER  - 

@article{
author = "Milanović, Slobodan and Mihailović, Dragutin T. and Lakićević, Milena and Đurđević, Vladimir and Malinović-Milicević, Slavica and Milanović, Slađan D. and Trailović, Zoran",
year = "2023",
abstract = "The impact of climate change on insect pests is an emerging topic in forestry and forest science. This study investigates the relationships between two broadleaved forest pests - spongy moth (Lymantria dispar L.) and brown-tail moth (Euproctis chry-sorrhoea L.) - and oaks (Quercus sp.) as their hosts. Oak forests cover almost one-third of the total forest area of Serbia and are ecologicallyvery valuable, but at the same time vulnerable, as being affected in adverse ways by several primary pests and pathogens. Since 1862, Serbia experienced several extremely large outbreaks of spongy moth with more than a hundred thousand hectares completely defoliated each time, while brown-tail moth occurred periodically with a much lower spatial extent. The aim of this research was to investigate the effect of UV radiation (UVR) and air tempe-rature on spongy moth and brown-tail moth in Serbian forests. We used simulations of the coupled regional climate model EBU-POM (Eta Belgrade University-Princeton Ocean Model) for the A1B scenario for the period 2001-2030 as main input and diffe-rent statistical methods to explore relationships between observations of pest spread and climate change impacts. Our results suggest(i) increasing the areas affected by spongy moth due to its sensitivity on UVR in May, and(ii) altitudinal spreading of brown-tail moth population up to 800 - 1000 m.This research indicates that in situ forest observations in Serbia are not only affected by climate change, but also by the combined effect of climate on forest pests. For fur-ther research, we recommend exploring other forest stressors or dieback phenomena in European forests by applying the same or similar regional climate model dataset.",
journal = "Austrian Journal of Forest Science",
title = "Impact of UV radiation and temperature on the spongy moth and the brown-tail moth in Serbia",
pages = "20-1",
number = "1",
volume = "140",
url = "conv_1697"
}

Milanović, S., Mihailović, D. T., Lakićević, M., Đurđević, V., Malinović-Milicević, S., Milanović, S. D.,& Trailović, Z.. (2023). Impact of UV radiation and temperature on the spongy moth and the brown-tail moth in Serbia. in Austrian Journal of Forest Science, 140(1), 1-20.
conv_1697

Milanović S, Mihailović DT, Lakićević M, Đurđević V, Malinović-Milicević S, Milanović SD, Trailović Z. Impact of UV radiation and temperature on the spongy moth and the brown-tail moth in Serbia. in Austrian Journal of Forest Science. 2023;140(1):1-20.
conv_1697 .

Milanović, Slobodan, Mihailović, Dragutin T., Lakićević, Milena, Đurđević, Vladimir, Malinović-Milicević, Slavica, Milanović, Slađan D., Trailović, Zoran, "Impact of UV radiation and temperature on the spongy moth and the brown-tail moth in Serbia" in Austrian Journal of Forest Science, 140, no. 1 (2023):1-20,
conv_1697 .

TY  - JOUR
AU  - Milanović, Slobodan
AU  - Marković, Nenad
AU  - Pamučar, Dragan
AU  - Gigović, Ljubomir
AU  - Kostić, Pavle
AU  - Milanović, Slađan D.
PY  - 2021
UR  - https://omorika.sfb.bg.ac.rs/handle/123456789/1211
AB  - Forest fire risk has increased globally during the previous decades. The Mediterranean region is traditionally the most at risk in Europe, but continental countries like Serbia have experienced significant economic and ecological losses due to forest fires. To prevent damage to forests and infrastructure, alongside other societal losses, it is necessary to create an effective protection system against fire, which minimizes the harmful effects. Forest fire probability mapping, as one of the basic tools in risk management, allows the allocation of resources for fire suppression, within a fire season, from zones with a lower risk to those under higher threat. Logistic regression (LR) has been used as a standard procedure in forest fire probability mapping, but in the last decade, machine learning methods such as fandom forest (RF) have become more frequent. The main goals in this study were to (i) determine the main explanatory variables for forest fire occurrence for both models, LR and RF, and (ii) map the probability of forest fire occurrence in Eastern Serbia based on LR and RF. The most important variable was drought code, followed by different anthropogenic features depending on the type of the model. The RF models demonstrated better overall predictive ability than LR models. The map produced may increase firefighting efficiency due to the early detection of forest fire and enable resources to be allocated in the eastern part of Serbia, which covers more than one-third of the country's area.
T2  - Forests
T1  - Forest Fire Probability Mapping in Eastern Serbia: Logistic Regression versus Random Forest Method
IS  - 1
VL  - 12
DO  - 10.3390/f12010005
UR  - conv_1525
ER  - 

@article{
author = "Milanović, Slobodan and Marković, Nenad and Pamučar, Dragan and Gigović, Ljubomir and Kostić, Pavle and Milanović, Slađan D.",
year = "2021",
abstract = "Forest fire risk has increased globally during the previous decades. The Mediterranean region is traditionally the most at risk in Europe, but continental countries like Serbia have experienced significant economic and ecological losses due to forest fires. To prevent damage to forests and infrastructure, alongside other societal losses, it is necessary to create an effective protection system against fire, which minimizes the harmful effects. Forest fire probability mapping, as one of the basic tools in risk management, allows the allocation of resources for fire suppression, within a fire season, from zones with a lower risk to those under higher threat. Logistic regression (LR) has been used as a standard procedure in forest fire probability mapping, but in the last decade, machine learning methods such as fandom forest (RF) have become more frequent. The main goals in this study were to (i) determine the main explanatory variables for forest fire occurrence for both models, LR and RF, and (ii) map the probability of forest fire occurrence in Eastern Serbia based on LR and RF. The most important variable was drought code, followed by different anthropogenic features depending on the type of the model. The RF models demonstrated better overall predictive ability than LR models. The map produced may increase firefighting efficiency due to the early detection of forest fire and enable resources to be allocated in the eastern part of Serbia, which covers more than one-third of the country's area.",
journal = "Forests",
title = "Forest Fire Probability Mapping in Eastern Serbia: Logistic Regression versus Random Forest Method",
number = "1",
volume = "12",
doi = "10.3390/f12010005",
url = "conv_1525"
}

Milanović, S., Marković, N., Pamučar, D., Gigović, L., Kostić, P.,& Milanović, S. D.. (2021). Forest Fire Probability Mapping in Eastern Serbia: Logistic Regression versus Random Forest Method. in Forests, 12(1).
https://doi.org/10.3390/f12010005
conv_1525

Milanović S, Marković N, Pamučar D, Gigović L, Kostić P, Milanović SD. Forest Fire Probability Mapping in Eastern Serbia: Logistic Regression versus Random Forest Method. in Forests. 2021;12(1).
doi:10.3390/f12010005
conv_1525 .

Milanović, Slobodan, Marković, Nenad, Pamučar, Dragan, Gigović, Ljubomir, Kostić, Pavle, Milanović, Slađan D., "Forest Fire Probability Mapping in Eastern Serbia: Logistic Regression versus Random Forest Method" in Forests, 12, no. 1 (2021),
https://doi.org/10.3390/f12010005 .,
conv_1525 .