Abstract: Similarity mining of hydrological time series is an importance aspect of hydrological time series mining.It will be of great importance in flood forecasting and flood control scheduling.According to the characteristics of hydrological data,this paper proposed a DTW clustering-based similarity mining method over hydrological time series.Firstly,on the premise of wavelet denoising,feature point segmentation and semantic classification,hierarchical cluster analysis is used to the classified sub-sequences based on DTW distance and the sub-sequences are symbolized.Then,candidate sets of time series are filtered according to the edit distance between symbol sequences.Finally,the similar hydrological time series are got precisely from the candidate sets by DTW exact matching.Experiments on the water level of Chuhe Liuhe station show that the proposed method can narrow the candidate sets effectively and improve the efficiency of searching for semantic similarity of hydrological time series.

Key words: Hydrological time series,Semantic similar,DTW distance,Hierarchical clustering,Edit distance