Forest Fire Probability Mapping in Eastern Serbia: Logistic Regression versus Random Forest Method

Milanović, Slobodan; Marković, Nenad M.; Pamučar, Dragan; Gigović, Ljubomir; Kostić, Pavle; Milanović, Svetlana

doi:10.3390/f12010005

Cited by 79 publications

(51 citation statements)

References 87 publications

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“…Although in [49] RF models were more efficient than LR for forest fire probability mapping, in our study, XGBoost presented an even higher performance. RF and LR hardly achieved an accuracy of 60-70%, whereas XGBoost achieved an accuracy of 70-80% (Figure 5).…”

Section: Lag and Machine Learning Algorithm Selectioncontrasting

confidence: 56%

Near Real-Time Fire Detection and Monitoring in the MATOPIBA Region, Brazil

Pletsch

Körting

Morita³

et al. 2022

Remote Sensing

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MATOPIBA is an agricultural frontier, where fires are essential for its biodiversity maintenance. However, the increase in its recurrence and intensity, as well as accidental fires can lead to socioeconomic and environmental losses. Due to this dual relationship with fire, near real-time (NRT) fire management is required throughout the region. In this context, we developed, to the best of our knowledge, the first Machine Learning (ML) algorithm based on the GOES-16 ABI sensor able to detect and monitor Active Fires (AF) in NRT in MATOPIBA. To do so, we analyzed the best combination of three ML algorithms and how long it takes to consider a historical time series able to support accurate AF predictions. We used the most accurate combination for the final model (FM) development. The results show that the FM ensures an overall accuracy rate of approximately 80%. The FM potential is remarkable not only for single detections but also for a consecutive sequence of positive predictions. Roughly, the FM achieves an accuracy rate peak after around 20 h of consecutive AF detections, but there is an important trade-off between the accuracy and the time required to assemble more fire indications, which can be decisive for firefighters in real life.

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Section: Lag and Machine Learning Algorithm Selectioncontrasting

confidence: 56%

Near Real-Time Fire Detection and Monitoring in the MATOPIBA Region, Brazil

Pletsch

Körting

Morita³

et al. 2022

Remote Sensing

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“…LR is primarily used for binary dependent variables, however, the model can be extended to scenarios involving 3 or more dependent variables (ordinal, multinomial). LR integrates a logistic distribution function (also known as a sigmoid function) which models the probability ratio directly [39]. Logistic regression can mathematically be defined as finding the optimized β parameters of the observed values a and b which is given as:…”

Section: Logistic Regressionmentioning

confidence: 99%

Malicious Traffic Detection in IoT and Local Networks Using Stacked Ensemble Classifier

Petinrin¹,

Saeed²,

Li³

et al. 2022

Computers, Materials &Amp; Continua

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Malicious traffic detection over the internet is one of the challenging areas for researchers to protect network infrastructures from any malicious activity. Several shortcomings of a network system can be leveraged by an attacker to get unauthorized access through malicious traffic. Safeguard from such attacks requires an efficient automatic system that can detect malicious traffic timely and avoid system damage. Currently, many automated systems can detect malicious activity, however, the efficacy and accuracy need further improvement to detect malicious traffic from multi-domain systems. The present study focuses on the detection of malicious traffic with high accuracy using machine learning techniques. The proposed approach used two datasets UNSW-NB15 and IoTID20 which contain the data for IoT-based traffic and local network traffic, respectively. Both datasets were combined to increase the capability of the proposed approach in detecting malicious traffic from local and IoT networks, with high accuracy. Horizontally merging both datasets requires an equal number of features which was achieved by reducing feature count to 30 for each dataset by leveraging principal component analysis (PCA). The proposed model incorporates stacked ensemble model extra boosting forest (EBF) which is a combination of tree-based models such as extra tree classifier, gradient boosting classifier, and random forest using a stacked ensemble approach. Empirical results show that EBF performed significantly better and achieved the highest accuracy score of 0.985 and 0.984 on the multi-domain dataset for two and four classes, respectively.

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“…Some new technologies have been used to simulate the fire spread to get a better simulation result, and machine learning based models have been in use for a long time [26,27]. Milanović [28] determined the main explanatory variables for forest fire occurrence for Logistic Regression (LR) and Random Forest (RF), and they mapped the probability of forest fire occurrence in Eastern Serbia based on these models. However, LR and RF models are more likely to produce under-fitting.…”

Section: Introductionmentioning

confidence: 99%

Simulating Forest Fire Spread with Cellular Automation Driven by a LSTM Based Speed Model

Zhang

et al. 2022

Fire

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The simulation of forest fire spread is a key problem for the management of fire, and Cellular Automata (CA) has been used to simulate the complex mechanism of the fire spread for a long time. The simulation of CA is driven by the rate of fire spread (ROS), which is hard to estimate, because some input parameters of the current ROS model cannot be provided with a high precision, so the CA approach has not been well applied yet in the forest fire management system to date. The forest fire spread simulation model LSTM-CA using CA with LSTM is proposed in this paper. Based on the interaction between wind and fire, S-LSTM is proposed, which takes full advantage of the time dependency of the ROS. The ROS estimated by the S-LSTM is satisfactory, even though the input parameters are not perfect. Fifteen kinds of ROS models with the same structure are trained for different cases of slope direction and wind direction, and the model with the closest case is selected to drive the transmission between the adjacent cells. In order to simulate the actual spread of forest fire, the LSTM-based models are trained based on the data captured, and three correction rules are added to the CA model. Finally, the prediction accuracy of forest fire spread is verified though the KAPPA coefficient, Hausdorff distance, and horizontal comparison experiments based on remote sensing images of wildfires. The LSTM-CA model has good practicality in simulating the spread of forest fires.

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Forest Fire Probability Mapping in Eastern Serbia: Logistic Regression versus Random Forest Method

Cited by 79 publications

References 87 publications

Near Real-Time Fire Detection and Monitoring in the MATOPIBA Region, Brazil

Near Real-Time Fire Detection and Monitoring in the MATOPIBA Region, Brazil

Malicious Traffic Detection in IoT and Local Networks Using Stacked Ensemble Classifier

Simulating Forest Fire Spread with Cellular Automation Driven by a LSTM Based Speed Model

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