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.
Wavelet-based automated localization and classification of peaks in streamflow data seriee
TARAMASSO, ANGELA CELESTE;
2012-01-01
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.
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