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首页> 外文期刊>Journal of hydrometeorology >Streamflow Hydrograph Classification Using Functional Data Analysis
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Streamflow Hydrograph Classification Using Functional Data Analysis

机译：利用功能数据分析进行水文水位分类

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摘要

Classification of streamflow hydrographs plays an important role in a large number of hydrological and hydraulic studies. For instance, it allows decisions to be made regarding the implementation of hydraulic structures and characterization of different flood types, leading to a better understanding of extreme flow behavior. The employed hydrograph classification methods are generally based on a finite number of hydrograph characteristics and do not include all the available information contained in a discharge time series. In this paper, two statistical techniques from the theory of functional data classification are adapted and applied for the analysis of flood hydrographs. Functional classification directly employs all data of a discharge time series and thus contains all available information on shape, peak, and timing. This potentially allows a better understanding and treatment of floods as well as other hydrological phenomena. The considered functional methodology is applied to streamflow datasets from the province of Quebec, Canada. It is shown that classes obtained using functional approaches have merit and can lead to better representation than those obtained using a multidimensional hierarchical classification method. The considered methodology has the advantage of using all of the information contained in the hydrograph, thus reducing the subjectivity that is inherent in multidimensional analysis of the type and number of characteristics to be used and consequently diminishing the associated uncertainty.
机译：流水线图的分类在大量水文和水力研究中起着重要作用。例如，它允许对水力结构的实施和不同洪水类型的特性进行决策，从而更好地了解极端流动行为。所采用的水位图分类方法通常基于有限数量的水位图特征，并且不包括排放时间序列中包含的所有可用信息。本文采用了功能数据分类理论中的两种统计技术，并将其应用于洪水水文分析。功能分类直接使用放电时间序列的所有数据，因此包含有关形状，峰值和时间的所有可用信息。这有可能使人们更好地理解和处理洪水以及其他水文现象。所考虑的功能方法论已应用于来自加拿大魁北克省的流量数据集。结果表明，与使用多维层次分类方法获得的类相比，使用功能方法获得的类具有优点，并且可以带来更好的表示。所考虑的方法具有使用水位图中包含的所有信息的优势，从而减少了多维分析中要使用的特征的类型和数量固有的主观性，从而减少了相关的不确定性。

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