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Flood Forecasting Based on Clustering Analysis

Author: ZuoGuiQing
Tutor: LiuGuoHua
School: Zhejiang University
Course: Hydraulic Structure Engineering
Keywords: Cluster analysis Unsupervised Feature Selection Flood Forecasting Data Mining Classification rate given
CLC: TV124
Type: Master's thesis
Year: 2010
Downloads: 171
Quote: 0
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Abstract


Hydrology are the subjects of a study of the laws of nature , is very dependent on its observed sample . Looking for regularity in the samples from these observations is the basic way to solve hydrological problems . Hydrological forecast is the direct product of in-depth understanding of hydrological phenomena . There is an urgent need to practice to get flood peak appears to reduce the loss of life and property brought by the flood . In order to improve the accuracy of flood forecasting , the introduction of feature selection in data mining and cluster analysis . Classification rate given is an important means to improve the accuracy of flood forecasting , flood sample has a decisive influence on the rate of flood forecasting model parameters given . Rainfall is a direct cause of floods generated , it is necessary to cluster analysis to improve the classification rate given accuracy of the the flood rains extract representative features . Selected features , the inevitable existence of redundant features , interfere with the clustering effect . To this end, the proposed flood characteristics based on unsupervised clustering analysis method . Based on the the Mitra algorithm for feature selection , and k-means and FCM clustering method of flood cluster analysis . Mitra, algorithm can effectively remove redundant features and k -means and FCM clustering results are in good agreement with the distinctive characteristics of various types of flood . In this paper, in order to the flood forecasting Shaxikou system as an example , Xin'anjiang hydrological model and the Muskingum flow algorithm forecast reservoir inflow . Classification calibration parameters given the parameters of the model in the case of various types of flood . The selection of the parameters of each classification of the corresponding property flood forecasting, and comprehensive rate given the parameters of the forecast results were compared , and part to improve the accuracy of flood forecasting , to a lesser extent .

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CLC: > Industrial Technology > Hydraulic Engineering > The basic science of water resources project > Engineering and hydrology > Hydrological forecasting
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