An intelligent system employing an enhanced fuzzy c-means clustering model: Application in the case of forest fires

Iliadis, Lazaros; Vangeloudh, M.; Spartalis, Stefanos

doi:10.1016/j.compag.2009.07.008

Cited by 28 publications

(24 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…Semi-Triangular and Triangular membership functions (functions 1 and 2 respectively) were used to determine the degree of membership (DOM) of each area under study to each corresponding fuzzy risk set ( [10], [12], [16]). The choice of these functions was based on the fact that they offer only one single peak point where the DOM equals 1 and thus they distinguish the areas in a more straightforward manner.…”

Section: Fuzzy Degree Membership Functionsmentioning

confidence: 99%

See 1 more Smart Citation

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

Section: Literature Reviewmentioning

confidence: 99%

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

show abstract

“…Clustering methods are highly recommended for defining MZs (YAN et al, 2007;ILIADIS et al, 2010) and include the use of several attributes such as electrical conductivity, elevation, slope and soil texture, and nitrogen alone and in combination. Although any attribute may be related to crop yield, for DOERGE (2000), the ideal attribute is the correlation of predictable spatial information sources with yield.…”

Section: Introductionmentioning

confidence: 99%

“…Although any attribute may be related to crop yield, for DOERGE (2000), the ideal attribute is the correlation of predictable spatial information sources with yield. Clustering techniques for MZ generation include algorithms such as K-Means and Fuzzy C-Means (ILIADIS et al, 2010;VALENTE et al, 2012 andLI et al, 2013), which offer good results (VITHARANAet al, 2008;MORARI et al, 2009;MORAL et al, 2010;RODRIGUES JUNIOR et al, 2011;DAVATGAR et al, 2012;KWEON, 2012;BANSOD & PANDEY, 2013), which permit the automatic division of the studied field. In this approach, different data sources that are related to crop development factors can be used to generate MZs.…”

Section: Introductionmentioning

confidence: 99%

Redundant variables and the quality of management zones

et al. 2016

View full text Add to dashboard Cite

Precision agriculture (PA) allows farmers to identify and address variations in an agriculture field. Management zones (MZs) make PA more feasible and economical. The most important method for defining MZs is a fuzzy C-means algorithm, but selecting the variable for use as the input layer in the fuzzy process is problematic. BAZZI et al. (2013) used Moran's bivariate spatial autocorrelation statistic to identify variables that are spatially correlated with yield while employing spatial autocorrelation. BAZZI et al. (2013) proposed that all redundant variables be eliminated and that the remaining variables would be considered appropriate on the MZ generation process. Thus, the objective of this work, a study case, was to test the hypothesis that redundant variables can harm the MZ delineation process. BAZZI This work was conducted in a 19.6-ha commercial field, and 15 MZ designs were generated by a fuzzy C-means algorithm and divided into two to five classes. Each design used a different composition of variables, including copper, silt, clay, and altitude. Some combinations of these variables produced superior MZs. None of the variable combinations produced statistically better performance that the MZ generated with no redundant variables. Thus, the other redundant variables can be discredited. The design with all variables did not provide a greater separation and organization of data among MZ classes and was not recommended.KEYWORDS: precision agriculture, spatial correlation, relative efficiency. VARIÁVEIS REDUNDANTES E A QUALIDADE DE ZONAS DE MANEJORESUMO: A agricultura de precisão proporciona aos agricultores identificar e tratar de forma adequada as variações encontradas na área agrícola. As zonas de manejo (ZMs) permitem a implantação da agricultura de precisão de forma viável e relativamente mais econômica. A forma mais importante para definir ZMs é usando o algoritmo fuzzy C-means. Um problema consiste em como selecionar a variável a ser usada como layer de entrada no processo fuzzy. Assim, o objetivo deste trabalho, foi testar a hipótese de que variáveis redundates podem prejudicar o processo de delineamento de ZMs. Este trabalho foi desenvolvido em uma área de 19,6 ha e 15 agrupamentos de ZMs foram gerados por meio do o algoritmo fuzzy C-means, dividindo-se em duas a cinco classes. Cada agrupamento usou uma composição diferente de variáveis, que são os atributos cobre, silte, argila, e altitude. Foi encontrado que algumas combinações dessas variáveis produziu melhores ZMs. Nenhuma combinação de variáveis produziu desempenho estatisticamente melhor que a ZM gerada apenas com as variáveis não redundantes. Assim, as variáveis redundantes podem ser descartadas. O agrupamento com todas as variáveis não forneceu maior separação e organização dos dados entre as classes de ZM, não sendo recomendado. PALAVRAS-CHAVE: agricultura de precisão, correlação espacial, eficiência relativa.

show abstract

“…The goal is to identify the presence and the degree of lung pathology caused by idiopathic pulmonary fibrosis (IPF), which is a chronic, progressive and usually lethal lung disorder of unknown etiology [1], whose variability concerning the severity of the lesions it incurs in the lung is great when assessed by microscopic histological images [12]. Fuzzy c-means clustering (and more over an improved version of fuzzy c-means) has also been applied in environmental cases [10] The Fuzzy c-means clustering method was applied to digital images of sections, captured using a Nikon ECLIPSE E800 microscope and a Nikon digital camera DXM1200 at a magnification of 4x. Each image was partitioned into windows of specified size and features were extracted for each window.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Detection of Fibrosis in Microscopy Images Using Fractals and Fuzzy c-Means Clustering

Tasoulis¹,

Plagianakos²

2012

IFIP Advances in Information and Communication Technology

View full text Add to dashboard Cite

Abstract. The advances in improved fluorescent probes and better cameras in collaboration with the advent of computers in imaging and image analysis, assist the task of diagnosis in many fields of biologic and medical research. In this paper, we introduce a computer-assisted image characterization tool based on a Fuzzy clustering method for the quantification of degree of Idiopathic Pulmonary Fibrosis (IPF) in medical images. The implementation of this algorithmic strategy is very promising concerning the issue of the automated assessment of microscopic images of lung fibrotic regions.

show abstract

An intelligent system employing an enhanced fuzzy c-means clustering model: Application in the case of forest fires

Cited by 28 publications

References 10 publications

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

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

Redundant variables and the quality of management zones

Unsupervised Detection of Fibrosis in Microscopy Images Using Fractals and Fuzzy c-Means Clustering

Contact Info

Product

Resources

About