Research And Application Of Hydrological Time Series Data Mining Algorithms

Hydrological Time Series Data Mining, namely applying Data Mining techniques to hydrological time series, can offer new analysis methods and scientific decision supports to solve outstanding problems in the field of hydrology, by extracting useful information and knowledge from large hydrologic data with high-efficiency algorithms of Data Mining according to the feather of data and information demands in the field of hydrology. However the research and application of its algorithms is still in its beginning stage. In this paper, Based on analysis of Data Mining techniques and the feather of hydrological time series the Research is made on Data Mining algorithms applied to hydrological time series of Pattern Representation, Similarity Measure, Classification and Prediction, which are validated and evaluated by hydrologic data of actual measurement. The main contents and achievements are as follows:1. Taking local extreme points and feathers of Hydrological Time Series as the breakthrough point, a Pattern Representation method based on the factor feathers of hydrological time series is put forward, which can solve unfitness of the piecewise linear Pattern Representation algorithm for a variety of reasons such as short-term frequent fluctuations, much more local extreme points, unequal time intervals corresponding to data points and so on. The experiment shows that this method is simple, high-efficiency and adaptive.2. A improved Dynamic Time Warping formula, Adaptive Segmented Dynamic Time Warping(ASDTW), is put forward and then the algorithms of factor-feather-based hydrological time series Similarity Measure are formed completely. The experiment shows that the method has its unique advantages in regardless of wholly controlling of Pattern trends or fitting error of primary time series, and therefore it more fits the characteristic features of hydrological time series.3. It is discussed how to classify hydrological time series with Data Mining algorithms. And algorithms of Model Trees and Support Vector Regression are improved according to feathers of hydrological time series. Support Vector Regression and Model Trees are fused and are applied to equal-interval hydrological processes Data Mining. Classical algorithms of Instance-based Learning is improved in three directions-samples extracting, disturbances controlling and attributes weighting-and combined with Adaptive Segmented Dynamic Time Warping(ASDTW) in order that a Data Mining Model of unequal-interval hydrologic factors Series extracted is established.4. The algorithm of Model Trees based on Support Vector Regression is applied to practice in a watershed with hydrology stations as study area. A Data Mining predicting model is made and compared with Xin'anjiang model. By contrast, it can be shown that the former has its merits such as less data input, simpler process, smaller amount of maintenance, etc, and ensures its accuracy at the same time.5. A Data Mining Model of hydrologic factors series extracted, in view of hydrologic factors series extracted in hydrological databases, is made passing through an continuous procedure from data preparing, data preprocessing, instances initializing, samples choosing, attributes weighting, and similarity measure. Comparing with the traditional method of flood forecasting Rainfall-runoff Experience Correlation, the former is simple, rapid, easy-maintenance and reliable and so it is of practical value.The main innovations in this paper are as follows:Adaptive Segmented Dynamic Time Warping (ASDTW) formula is put forward based on DTW according to feathers of Hydrological data. An improved algorithm of Instance-based Learning combined with ASDTW solves the problem of Data Mining for hydrologic factors series extracted. For the first time, Model Trees are fused with Support Vector Regression and applied to daily runoff predicting successfully, which enrich techniques of hydrological time series Data Mining.