A Bayesian network model for prediction and analysis of possible forest fire causes

Sevinç, Volkan; Küçük, Ömer; Goltas, Merih

doi:10.1016/j.foreco.2019.117723

Cited by 131 publications

(72 citation statements)

References 35 publications

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“…During morning, evening, or night, the temperature is low and the relative humidity in the air is high, meaning forest fires are more unlikely. At the same time, precipitation is also related to humidity and directly affects the occurrence of fires [21,51]. Human factors also play an important factor contributing to the occurrence of forest fires.…”

Section: Discussionmentioning

confidence: 99%

“…With the continuous development of artificial intelligence technology, the use of machine learning algorithms to build forest fire prediction models has drawn increasing attention from the scientific community [18][19][20][21][22][23][24][25][26]. Artificial neural networks are a highly nonlinear dynamic system, which can approximate and simulate any nonlinear function of nonlinear dynamic phenomena such as forest fires with strong fault tolerances [27].…”

Section: Introductionmentioning

confidence: 99%

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Application of the Artificial Neural Network and Support Vector Machines in Forest Fire Prediction in the Guangxi Autonomous Region, China

Zhong-ke

Chen

et al. 2020

Discrete Dynamics in Nature and Society

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The study of forest fire prediction is of great environmental and scientific significance. China’s Guangxi Autonomous Region has a high incidence rate of forest fires. At present, there is little research on forest fires in this area. The application of the artificial neural network and support vector machines (SVM) in forest fire prediction in this area can provide data for forest fire prevention and control in Guangxi. In this paper, based on Guangxi’s 2010–2018 satellite monitoring hotspot data, meteorology, terrain, vegetation, infrastructure, and socioeconomic data, the researchers determined the main forest fire driving factors in Guangxi. They used feature selection and backpropagation neural networks and radial basis SVM to build forest fire prediction models. Finally, the researchers use the accuracy, precision, and area under the characteristic curve (ROC-AUC) and other indicators to evaluate the predictive performance of the two models. The results showed that the prediction accuracy of the BP neural network and SVM is 92.16% and 89.89%, respectively. As both results are over 85%, the requirements of prediction accuracy is met. These results can be used for forest fire prediction in the Guangxi Autonomous Region. Specifically, the accuracy of the BP neural network was 0.93, which was higher than that of the SVM model (0.89); the recall of the SVM model was 0.84, which was lower than the BANN model (0.92), and the AUC value of the SVM model was 0.95, which was lower than the BP neural network model. The obtained results confirm that the BP neural network model can provide more prediction accuracy than support vector machines and is therefore more suitable for forest fire prediction in Guangxi, China. This research provides the necessary theoretical basis and data support for application in the field of forestry of the Guangxi Autonomous Region, China.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of the Artificial Neural Network and Support Vector Machines in Forest Fire Prediction in the Guangxi Autonomous Region, China

Zhong-ke

Chen

et al. 2020

Discrete Dynamics in Nature and Society

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show abstract

“…BNs are a class of Probabilistic Graphical Models (PGM) that integrate probability and graph theory to represent stochastic and causal relationships among variables in a system [66,67]. BNs consist of two main components (i) a directed acyclic graph (DAG) and (ii) local probability distributions (LPD) or the network parameters [68]. The DAG is a map that describes the causal relationships among the system variables, also known as nodes.…”

Section: Bayesian Network Modelingmentioning

confidence: 99%

Bayesian Network Analysis of Lysine Biosynthesis Pathway in Rice

Lahiri¹,

Rastogi²,

Datta³

et al. 2021

Preprint

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Lysine is the first limiting essential amino acid in rice because it is present in the lowest quantity compared to all the other amino acids. Amino acids are the building block of proteins and play an essential role in maintaining the human body’s healthy functioning. Rice is a staple food for large proportion of the global population, thus increasing the lysine content in rice will improve its nutritional value. In this paper, we studied the lysine biosynthesis pathway in rice (Oryza Sativa) to identify the regulators of the lysine reporter gene LYSA (LOC_Os02g24354). Genetically intervening at the regulators has the potential to increase the overall lysine content in rice. We modeled the lysine biosynthesis pathway in rice seedlings under normal and saline (NaCl) stress conditions using Bayesian networks. We estimated the model parameters using experimental data and identified the gene DAPF(LOC_Os12g37960) as a positive regulator of the lysine reporter gene LYSA under both normal and saline stress conditions. Based on this analysis, we conclude that the gene DAPF is a potent candidate for genetic intervention. Upregulating DAPF using methods such as CRISPR-Cas9 has the potential to upregulate the lysine reporter gene LYSA and increase the overall lysine content in rice.

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“…The development of artificial intelligence has led researchers to focus on building a forest fire prediction model using machine learning algorithms [26][27] [28][29] [30][31] [32] [33][34] [35]. Researchers have used machine learning models to predict wildfires with greater accuracy and faster automation.…”

Section: Introductionmentioning

confidence: 99%

Chinese forest fire occurrence prediction based on machine learning methods

Feng

Zhao

et al. 2021

Preprint

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Forest fires can cause serious harm. Scientifically predicting forest fires is an important basis for preventing them. Currently, there is little research on the prediction of long time-series forest fires in China. Choosing a suitable forest fire prediction model and predicting the probability of Chinese forest fire occurrence are of great importance to China’s forest fire prevention and control work. Based on fire hotspot, meteorological, terrain, vegetation, infrastructure, and socioeconomic data collected from 2003 to 2016, we used a random forest model as a feature-selection method to identify 13 major drivers of forest fires in China. The forest fire prediction models developed in this study are based on four machine-learning algorithms: an artificial neural network, a radial basis function network, a support-vector machine, and a random forest. The models were evaluated using the five performance indicators of accuracy, precision, recall, f1 value, and area under the curve. We used the optimal model to obtain the probability of forest fire occurrence in various provinces in China and created a spatial distribution map of the areas with high incidences of forest fires. The results showed that the prediction accuracy of the four forest fire prediction models was between 75.8% and 89.2%, and the area under the curve value was between 0.840 and 0.960. The random forest model had the highest accuracy (89.2%) and area under the curve value (0.96); thus, it was used as the optimal model to predict the probability of forest fire occurrence in China. The prediction results indicate that the areas with high incidences of forest fires are mainly concentrated in north-eastern China (Heilongjiang Province and northern Inner Mongolia Autonomous Region) and south-eastern China (including Fujian Province and Jiangxi Province). In areas at high risk of forest fire, management departments can improve forest fire prevention and control by establishing watch towers and using other monitoring equipment. This study helps in understanding the main drivers of forest fires in China, provides a reference for the selection of high-precision forest fire prediction models, and provides a scientific basis for China’s forest fire prevention and control work.

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A Bayesian network model for prediction and analysis of possible forest fire causes

Cited by 131 publications

References 35 publications

Application of the Artificial Neural Network and Support Vector Machines in Forest Fire Prediction in the Guangxi Autonomous Region, China

Application of the Artificial Neural Network and Support Vector Machines in Forest Fire Prediction in the Guangxi Autonomous Region, China

Bayesian Network Analysis of Lysine Biosynthesis Pathway in Rice

Chinese forest fire occurrence prediction based on machine learning methods

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