Similarity search and pattern discovery in hydrological time series data mining

The rapid development of data mining provides a new method for water resource management, hydrology and hydroinformatics research. In the paper, based on data mining theory and technology, we analyse hydrological daily discharge time series of the Shaligunlanke Station in the Tarim River Basin in China from the year 1961 to 2000. Firstly, according to the four monthly statistics, namely mean monthly discharge, monthly maximum discharge, monthly amplitude and monthly standard deviation, K-mean clustering was used to segment the annual process of the daily discharge. The clustering result showed that the annual process of the daily discharge can be divided into five segments: snowmelt period I (April), snowmelt period II (May), rainfall period I (June-August), rainfall period II (September) and dry period (October-December and January-March). Secondly, dynamic time warping (DTW), which is a different distance metric method from the traditional Euclidian distance metric, was used to look for similarities in the discharge process. On the basis of the similarity matrix, the similar discharge processes can be mined in each period. Thirdly, agglomerative hierarchical clustering was used to cluster and discover the discharge patterns in terms of the autoregressive model. It was found that the discharge had a close relationship with the temperature and the precipitation, and the discharge processes were more similar under the same climatic condition. Our study shows that data mining is a feasible and efficient approach to discover the hidden information in the historical hydrological data and mining the implicative laws under the hydrological process. Copyright (C) 2010 John Wiley & Sons, Ltd.