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Titolo:
Classification of rainfall variability by using artificial neural networks
Autore:
Michaelides, S; Pattichis, CS; Kleovoulou, G;
Indirizzi:
Meteorol Serv, CY-1418 Nicosia, Cyprus Meteorol Serv Nicosia Cyprus CY-1418 eorol Serv, CY-1418 Nicosia, Cyprus Univ Cyprus, Dept Comp Sci, Nicosia, Cyprus Univ Cyprus Nicosia CyprusUniv Cyprus, Dept Comp Sci, Nicosia, Cyprus
Titolo Testata:
INTERNATIONAL JOURNAL OF CLIMATOLOGY
fascicolo: 11, volume: 21, anno: 2001,
pagine: 1401 - 1414
SICI:
0899-8418(200109)21:11<1401:CORVBU>2.0.ZU;2-F
Fonte:
ISI
Lingua:
ENG
Soggetto:
GALILEE ISRAEL; PREDICTION; IMAGERY; ANALOGS; MODELS;
Keywords:
artificial neural networks; cluster analysis; Mediterranean; rainfall variability; self-organizing feature maps; weather variability;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Physical, Chemical & Earth Sciences
Citazioni:
33
Recensione:
Indirizzi per estratti:
Indirizzo: Michaelides, S Meteorol Serv, CY-1418 Nicosia, Cyprus Meteorol Serv Nicosia Cyprus CY-1418 1418 Nicosia, Cyprus
Citazione:
S. Michaelides et al., "Classification of rainfall variability by using artificial neural networks", INT J CLIM, 21(11), 2001, pp. 1401-1414

Abstract

In this paper, the usefulness of artificial neural networks (ANNs) as a suitable tool for the study of the medium and long-term climatic variability is examined. A method for classifying the inherent variability of climatic data, as represented by the rainfall regime, is investigated. The rainfall recorded at a climatological station in Cyprus over a long time period has been used in this paper as the input for various ANN and cluster analysis models. The analysed rainfall data cover the time span 1917-1995. Using these values, two different procedures were followed for structuring the input vectors for training the ANN models: (a) each 1-year subset consisting of the 12 monthly elements, and (b) each 2-year subset consisting of the 24 monthly elements. Several ANN models with a varying number of output nodes have been trained, using an unsupervised learning paradigm, namely, the Kohonen's self-organizing feature maps algorithm. For both the 1- and 2-year subsets, 16 classes were empirically considered as the optimum for computing the prototype classes of weather variability for this meteorological parameter. The classification established by using the ANN methodology is subsequently compared with the classification generated by using cluster analysis, based on the agglomerative hierarchical clustering algorithm. To validate the classification results, the rainfall distributions for the more recent years 1996, 1997 and 1998 were utilized. The respective 1- and 2-year distributions for these years were assigned to particular classes for both the ANNand cluster analysis procedures. Compared with cluster analysis, the ANN models were more capable of detecting even minor characteristics in the rainfall waveshapes investigated, and they also performed a more realistic categorization of the available data. It is suggested that the proposed ANN methodology can be applied to more climatological parameters, and with longer cycles. Copyright (C) 2001 Royal Meteorological Society.

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Documento generato il 20/01/20 alle ore 04:45:28