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dc.contributor.authorUdomboso, C. G.-
dc.contributor.authorAmahia, G. N.-
dc.contributor.authorDontwi, I. K.-
dc.identifier.otherJournal of Science Research 13, pp. 133-142-
dc.description.abstractThis research combines the efficiency of the artificial neural network and wavelet transform in modelling rainfall. The data used were decomposed into continuous wavelet signals on a scale of 48. Each of the decomposed series was subjected to correlation test with the original data. Instead of using all the series, ten series were selected on the basis of high correlation with die original data. These series included CWT 1, CWT 2, CWT 4, CWT 3, CWT 6, CWT 8, CWT 5, CWT 10, CWT 12, and CWT 7 (according to rank). The analysis showed that except in extremely rare cases, all the series performed optimally compared to the original data. The result of the study has been able to show' that using the continuous w'avelet transform in the ANN technique, a better performance of the network is observed.en_US
dc.subjectArtificial neural networken_US
dc.subjectRainfall modellingen_US
dc.subjectContinuous wavelet transformen_US
dc.titleOn the level of precision of the wavelet neural network in rainfall analysisen_US
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