A Fuzzy Inference System Using Gaussian Distribution Curves for Forest Fire Risk Estimation

Iliadis, Lazaros; Skopianos, Stergios; Tachos, Stavros; Spartalis, Stefanos

doi:10.1007/978-3-642-16239-8_49

Cited by 17 publications

(10 citation statements)

References 8 publications

(10 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also some cities like Greek adopted fuzzy logic system to predict the forest fire danger which offers 65% of accuracy detection [7].…”

Section: Validity and Accuracy Of Classical Modelsmentioning

confidence: 99%

“…For this reason, scientists worked hardly to predict forest fire in an accurate way by breaking down the non-linear relationship between fire occurrence and surrounding environment. Significant high accuracy performance results were reported using data mining tools such as: neural network [5], [6], fuzzy logic [5], and decision tree techniques [7].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An analytical review on the most widely used meteorological models in forest fire prediction

Hamadeh

Daya

Hilal

et al. 2015

2015 Third International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE)

View full text Add to dashboard Cite

At the beginning of 20 th century, scientists started to develop mathematical models in order to predict the probability of occurrence of forest fires. Meteorological parameters, such as daily temperature and humidity, were mainly used. In this paper, we review the seven most usable fire prediction indices in the world, that are Angstrom, Keetch-Byram, Modified Keetch-Byram, Canadian fire weather index, Nesterov, Modified Netserov, and Baumgartner Index. A comparative study including the mathematical equations, properties, characteristics, performanceand field of application of each model is presented. The different developed models were optimized to the local characteristics of the place of study. The problematic of suitability and compliance of indices in other regions with different conditions is discussed. Recent initiatives are finally presented. Keywords-forest fire prediction; fire weather indices; limitations and transferabilityI.

show abstract

“…Also some cities like Greek adopted fuzzy logic system to predict the forest fire danger which offers 65% of accuracy detection [7].…”

Section: Validity and Accuracy Of Classical Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An analytical review on the most widely used meteorological models in forest fire prediction

Hamadeh

Daya

Hilal

et al. 2015

2015 Third International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE)

View full text Add to dashboard Cite

show abstract

“…Recently, FRBSs have also been applied to related classification problems, such as credit scoring (Jiao et al 2007;Hajek and Olej 2008), financial performance evaluation (Ammar et al 2001), road maintenance (Hudec and Vujosevic 2010), forest fire risk estimation (Iliadis et al 2010), and so on. The general structure of an FRBS contains the fuzzification process using input membership functions, the construction of the base of if-then rules or automatic extraction of if-then rules from the input data, the application of operators (and, or, not) in rules, implication and aggregation within rules, and the defuzzification process of obtained outputs to crisp values.…”

Section: Adaptive Fuzzy Rule-based Systemsmentioning

confidence: 99%

Credit rating analysis using adaptive fuzzy rule-based systems: an industry-specific approach

Hájek

2011

Cent Eur J Oper Res

View full text Add to dashboard Cite

This paper presents an analysis of credit rating using fuzzy rule-based systems. The disadvantage of the models used in previous studies is that it is difficult to extract understandable knowledge from them. The root of this problem is the use of natural language that is typical for the credit rating process. This problem can be solved using fuzzy logic, which enables users to model the meaning of natural language words. Therefore, the fuzzy rule-based system adapted by a feed-forward neural network is designed to classify US companies (divided into the finance, manufacturing, mining, retail trade, services, and transportation industries) and municipalities into the credit rating classes obtained from rating agencies. Features are selected using a filter combined with a genetic algorithm as a search method. The resulting subsets of features confirm the assumption that the rating process is industry-specific (i.e. specific determinants are used for each industry). The results show that the credit rating classes assigned to bond issuers can be classified with high classification accuracy using low numbers of features, membership functions, and if-then rules. The comparison of selected fuzzy rule-based classifiers indicates that it is possible to increase classification performance by using different classifiers for individual industries.

show abstract

“…More specifically the following stochastic research efforts have been proposed recently for modeling drought [8] and some research has been done in the direction of evaluating existing models [9]. Fuzzy modeling efforts have been done recently towards forest fire risk classification in Greece [10][11][12][13][14]. Finally a limited number of Soft Computing Approaches applied in drought modeling has been published in the literature [15].…”

Section: Literature Reviewmentioning

confidence: 99%

“…A special case of fuzzy relation namely the Minimum T-Norm was applied in order to perform the fuzzy conjunction. The following function 3 presents the Minimum T-Norm approach where μΑ(Χ) and μB(X) are the DOM of element X to the fuzzy sets A and B respectively ( [12], [13], [17], [18]). …”

Section: Fuzzy Degree Membership Functionsmentioning

confidence: 99%

Fuzzy Modeling of the Climate Change Effect to Drought and to Wild Fires in Cyprus

Papakonstantinou

Iliadis

Pimenidis

et al. 2011

Engineering Applications of Neural Networks

View full text Add to dashboard Cite

Abstract. This is an intelligent modeling of the evolution of drought and forest fires, due to climate change in Cyprus. Original annual wild fire data records ) and data regarding meteorological parameters were used. A flexible modeling approach was proposed towards the determination of drought risk indices in all of the country. Cyprus was divided in eight polygons corresponding to eight meteorological stations. A Fuzzy Inference Rule Based System (FIRBS) was developed to produce the drought risk indices vectors for the forest regions under study. An analysis of the spatial distribution of the heat index vectors was performed. Forest fires distribution through the island was addressed. All of the results were stored by using an ArcGIS, (version 9.3) spatial data base that enables more comprehensive presentation of the most risky areas. There is a significant increase of drought in the island and this has a serious effect in the problems of forest fires and heat indices.

show abstract

A Fuzzy Inference System Using Gaussian Distribution Curves for Forest Fire Risk Estimation

Cited by 17 publications

References 8 publications

An analytical review on the most widely used meteorological models in forest fire prediction

An analytical review on the most widely used meteorological models in forest fire prediction

Credit rating analysis using adaptive fuzzy rule-based systems: an industry-specific approach

Fuzzy Modeling of the Climate Change Effect to Drought and to Wild Fires in Cyprus

Contact Info

Product

Resources

About