This paper combines discrete wavelet transform (DWT) with artificial intelligence algorithm in order to develop a new unsupervised method for fast detecting, localizing, and classifying flood events in real-world stage-discharge data time series. Localization is performed through a simple hill-climbing search algorithm initialized by the position of the highest DWT coefficients. The proposed method does not require any a priori information such as catchment characteristics or alert flood thresholds.
Scheda prodotto non validato
Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo
Titolo: | Wavelet-based automated localization and classification of peaks in streamflow data seriee |
Autori: | |
Data di pubblicazione: | 2012 |
Rivista: | |
Abstract: | This paper combines discrete wavelet transform (DWT) with artificial intelligence algorithm in order to develop a new unsupervised method for fast detecting, localizing, and classifying flood events in real-world stage-discharge data time series. Localization is performed through a simple hill-climbing search algorithm initialized by the position of the highest DWT coefficients. The proposed method does not require any a priori information such as catchment characteristics or alert flood thresholds. |
Handle: | http://hdl.handle.net/11567/294954 |
Appare nelle tipologie: | 01.01 - Articolo su rivista |
File in questo prodotto:
Non ci sono file associati a questo prodotto.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.