scholarly journals Wildfire Risk Assessment of Transmission-Line Corridors Based on Naïve Bayes Network and Remote Sensing Data

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 634
Author(s):  
Weijie Chen ◽  
You Zhou ◽  
Enze Zhou ◽  
Zhun Xiang ◽  
Wentao Zhou ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Transmission Line ◽  
Naive Bayes ◽  
Southern China ◽  
Naïve Bayes ◽  
Assessment Model ◽  
Related Factors ◽  
Wildfire Risk ◽  
Wildfire Occurrence ◽  
Bayes Network

Considering the complexity of the physical model of wildfire occurrence, this paper develops a method to evaluate the wildfire risk of transmission-line corridors based on Naïve Bayes Network (NBN). First, the data of 14 wildfire-related factors including anthropogenic, physiographic, and meteorologic factors, were collected and analyzed. Then, the relief algorithm is used to rank the importance of factors according to their impacts on wildfire occurrence. After eliminating the least important factors in turn, an optimal wildfire risk assessment model for transmission-line corridors was constructed based on the NBN. Finally, this model was carried out and visualized in Guangxi province in southern China. Then a cost function was proposed to further verify the applicability of the wildfire risk distribution map. The fire events monitored by satellites during the first season in 2020 shows that 81.8% of fires fall in high- and very-high-risk regions.

scholarly journals Wildfire Risk Assessment and Zoning by Integrating Maxent and GIS in Hunan Province, China

Forests ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1299
Author(s):  
Xuhong Yang ◽  
Xiaobin Jin ◽  
Yinkang Zhou
Keyword(s):  
Risk Assessment ◽  
High Risk ◽  
Environmental Variables ◽  
Climatic Conditions ◽  
Assessment Model ◽  
Hunan Province ◽  
Practical Significance ◽  
Wildfire Risk ◽  
Forest Density ◽  
Wildfire Occurrence

Forest wildfire is an important threat and disturbance facing natural forest ecosystems. Conducting wildfire risk assessments and zoning studies are of great practical significance in guiding wildfire prevention, curbing fire occurrence, and mitigating the environmental effects of wildfire. Taking Hunan Province, China as the case area, this study used remotely sensed high-temperature fire data as the wildfire sample. Twelve factors related to topography, climatic conditions, vegetation attributes, and human activities were used as environmental variables affecting wildfire occurrence. Then, a Maxent wildfire risk assessment model was constructed with GIS, which analyzed the contribution, importance, and response of environmental variables to wildfire in Hunan Province. The results show that (1) the Maxent model has high applicability and feasibility when applied to wildfire risk assessment after a test of wildfire sample sites; (2) the importance of meteorological conditions and vegetation status variables to wildfire are 54.64% and 25.40%, respectively, and their contribution to wildfire are 43.03% and 34.69%, respectively. The interaction between factors can enhance or weaken the contribution of factors on wildfire. (3) The mechanism for the effects of environmental variables on wildfire is not linear as generally believed; temperature, aridity, land use type, GDP, distance from the road, and population density have a nonlinear positive correlation with the probability of wildfire occurrence. Elevation, slope, precipitation, wind speed, and vegetation cover within the suitable interval positively contribute to the probability of wildfire, while the environmental conditions outside the suitable interval curb the occurrence of wildfire. The response of wildfire probability to forest density is U-shaped, which means either too high or too low will promote the occurrence of wildfire. (4) There is geographical variation of wildfire risk in Hunan Province. The areas at high risk and below account for 74.48% of the total area, while the areas at significantly high risk and above account for a relatively low proportion, 25.52%.

Modelling pathogen log10 reduction values achieved by activated sludge treatment using naïve and semi naïve Bayes network models

2015 ◽  
Vol 85 ◽  
pp. 304-315 ◽  
Author(s):  
Guido Carvajal ◽  
David J. Roser ◽  
Scott A. Sisson ◽  
Alexandra Keegan ◽  
Stuart J. Khan
Keyword(s):  
Activated Sludge ◽  
Naive Bayes ◽  
Network Models ◽  
Naïve Bayes ◽  
Log10 Reduction ◽  
Sludge Treatment ◽  
Bayes Network

scholarly journals Machine Learning Algorithms for Biological Targets: Investigating the Error Tolerance in Various Computational Methods

2019 ◽  
Author(s):  
Thomas M. Kaiser ◽  
Pieter B. Burger
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Naive Bayes ◽  
Probabilistic Neural Network ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Learning Models ◽  
Bayes Network ◽  
Insight Into ◽  
Machine Learning Models

Machine learning continues to make strident advances in the prediction of desired properties concerning drug development. Problematically, the efficacy of machine learning in these arenas is reliant upon highly accurate and abundant data. These two limitations, high accuracy and abundance, are often taken together; however, insight into the dataset accuracy limitation of contemporary machine learning algorithms may yield insight into whether non-bench experimental sources of data may be used to generate useful machine learning models where there is a paucity of experimental data. We took highly accurate data across six kinase types, one GPCR, one polymerase, a human protease, and HIV protease, and intentionally introduced error at varying population proportions in the datasets for each target. With the generated error in the data, we explored how the retrospective accuracy of a Naïve Bayes Network, a Random Forest Model, and a Probabilistic Neural Network model decayed as a function of error. Additionally, we explored the ability of a training dataset with an error profile resembling that produced by the Free Energy Perturbation method (FEP+) to generate machine learning models with useful retrospective capabilities. The categorical error tolerance was quite high for a Naïve Bayes Network algorithm averaging 39% error in the training set required to lose predictivity on the test set. Additionally, a Random Forest tolerated a significant degree of categorical error introduced into the training set with an average error of 29% required to lose predictivity. However, we found the Probabilistic Neural Network algorithm did not tolerate as much categorical error requiring an average of 20% error to lose predictivity. Finally, we found that a Naïve Bayes Network and a Random Forest could both use datasets with an error profile resembling that of FEP+. This work demonstrates that computational methods of known error distribution like FEP+ may be useful in generating machine learning models not based on extensive and expensive in vitro-generated datasets.

scholarly journals Students' learning style detection using tree augmented naive Bayes

2018 ◽  
Vol 5 (7) ◽  
pp. 172108 ◽  
Author(s):  
Ling Xiao Li ◽  
Siti Soraya Abdul Rahman
Keyword(s):  
Learning Styles ◽  
Bayesian Network ◽  
Learning Style ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Online Learning Environment ◽  
Detection Accuracy ◽  
Naive Bayesian ◽  
Bayes Network ◽  
Naïve Bayesian

Students are characterized according to their own distinct learning styles. Discovering students' learning style is significant in the educational system in order to provide adaptivity. Past researches have proposed various approaches to detect the students’ learning styles. Among all, the Bayesian network has emerged as a widely used method to automatically detect students' learning styles. On the other hand, tree augmented naive Bayesian network has the ability to improve the naive Bayesian network in terms of better classification accuracy. In this paper, we evaluate the performance of the tree augmented naive Bayesian in automatically detecting students’ learning style in the online learning environment. The experimental results are promising as the tree augmented naive Bayes network is shown to achieve higher detection accuracy when compared to the Bayesian network.

scholarly journals A Network-Based Approach to Investigate the Pattern of Syndrome in Depression

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Jianglong Song ◽  
Xi Liu ◽  
Qingqiong Deng ◽  
Wen Dai ◽  
Yibo Gao ◽  
...  
Keyword(s):  
Naive Bayes ◽  
Permutation Test ◽  
Naïve Bayes ◽  
Global Network ◽  
Syndrome Differentiation ◽  
Support Vector ◽  
Dynamic Interactions ◽  
Yin Deficiency ◽  
Bayes Network ◽  
The Difference

In Traditional Chinese Medicine theory, syndrome is essential to diagnose diseases and treat patients, and symptom is the foundation of syndrome differentiation. Thus the combination and interaction between symptoms represent the pattern of syndrome at phenotypic level, which can be modeled and analyzed using complex network. At first, we collected inquiry information of 364 depression patients from 2007 to 2009. Next, we learned classification models for 7 syndromes in depression using naïve Bayes, Bayes network, support vector machine (SVM), and C4.5. Among them, SVM achieves the highest accuracies larger than 0.9 except for Yin deficiency. Besides, Bayes network outperforms naïve Bayes for all 7 syndromes. Then key symptoms for each syndrome were selected using Fisher’s score. Based on these key symptoms, symptom networks for 7 syndromes as well as a global network for depression were constructed through weighted mutual information. Finally, we employed permutation test to discover dynamic symptom interactions, in order to investigate the difference between syndromes from the perspective of symptom network. As a result, significant dynamic interactions were quite different for 7 syndromes. Therefore, symptom networks could facilitate our understanding of the pattern of syndrome and further the improvement of syndrome differentiation in depression.

COMPARING NAÏVE-BAYES NETWORK STRUCTURES OVER MULTIPLE DATASET

2015 ◽  
Author(s):  
HARUNA CHIROMA ◽  
ABDULSALAM YA'U GITAL ◽  
ADAMU I. ABUBAKAR ◽  
SANAH ABDULLAHI MUAZ ◽  
JAAFAR Z. MAITAMA ◽  
...  
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Network Structures ◽  
Bayes Network ◽  
Multiple Dataset
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