Result: Predicting air pollution using fuzzy membership grade Kriging

Title:
Predicting air pollution using fuzzy membership grade Kriging
Authors:
DANNI GUO, RENKUAN GUO, THIART, Christien
Source:
Extracting information from spatial datasetsComputers, environment and urban systems. 31(1):33-51
Publisher Information:
Oxford: Elsevier Science, 2007.
Publication Year:
2007
Physical Description:
print, 3/4 p
Original Material:
INIST-CNRS
Subject Terms:
Environment, Environnement, Civil engineering, Génie civil, Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Batiment. Travaux publics, Buildings. Public works, Généralités, General, Pollution à l'intérieur du bâtiment, Pollution indoor buildings, Calcul des constructions. Sollicitations, Structural analysis. Stresses, Méthodes de calcul. Tables. Abaques, Computation methods. Tables. Charts, Pollution, Pollution atmosphérique, Atmospheric pollution, Analyse donnée, Data analysis, Análisis datos, Analyse spatiale, Spatial analysis, Análisis espacial, Concentration, Concentración, Etude méthode, Method study, Estudio método, Krigeage, Kriging, Pollution air, Air pollution, Contaminación aire, Prévision, Forecasting, Previsión, Statistique, Statistics, Estadística, Système flou, Fuzzy system, Sistema difuso, Système information géographique, Geographic information system, Sistema información geográfica, Direct fuzzy Kriging, Fuzzy membership grade Kriging, Fuzzy membership, GIS, PM10
Subject Geographic:
Amérique du Nord, North America, America del norte, Amérique, America, Etats Unis, United States, Estados Unidos, Californie, California
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Statistical Sciences, University of Cape Town, Private Bag, Rhodes Gift, Rondebosch 7701, Cape Town, South Africa
ISSN:
0198-9715
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=18493921
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Building. Public works. Transport. Civil engineering

Pollution
Accession Number:
edscal.18493921
Database:
PASCAL Archive

Further Information

A practical situation often facing us is that fuzzy spatial data are recorded as crisp real-valued numbers, e.g., a PM10 record is 15.1, but we do know that it is an imprecise and vague observation. A new spatial analysis technique - fuzzy membership grade Kriging with semi-statistical membership, proposed by Guo has been developed to address fuzzy spatial data recorded as crisp numbers. In this paper, we will explain fuzzy membership grade Kriging, its root, its theory and its implementations. As an illustration, we will use PM10 data of California, USA. Three sample membership functions are extracted from the data itself: linear, quadratic and hyperbolic tangent and applied to the PM10 data. The predicted membership grades are also transformed back into PM10 concentrations by using inverse functions in order to identify areas being dangerous to human health. Finally, we implement our new fuzzy membership grade Kriging in GIS.