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EnglishThis 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.
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| 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 |
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