Storm Flood Pattern Library in Middle and Small Rivers Based on Pattern Mining

doi:10.3969/j.issn.1006-2475.2018.12.007

Abstract

Abstract: The traditional neural network prediction method has been applied successfully in the field of hydrology. However, when flood forecasting is carried out in some data deficient areas, because of the lack of training samples, the model parameters are difficult to meet the requirements, so the prediction results using these methods are often not satisfied. In this paper, a new idea of constructing storm flood pattern library applicable to the basin to be forecasted is proposed, and the historical hydrology data of the basin is excavated and processed in a symbolic mode. Then, by analyzing the hydrological time series of frequent patterns and flood flow, the construction of storm flood pattern library of small and medium-sized rivers is completed. The experimental results show that the model mining method in this paper is used to build the pattern library of the rainstorm flood in the middle and small rivers, and the pattern library is used to quickly forecast the trend of future flood flow process, which has the accuracy and applicability of the basin.

Key words: pattern library, pattern miming, prediction model, hydrological data

CLC Number:

TP391.4

FENG Jun， GUO Tao， CHEN Zhi-fei. Storm Flood Pattern Library in Middle and Small Rivers Based on Pattern Mining[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2018.12.007.

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