Overview of Machine Learning Methods for Analyzing and Clustering of Flood Wave Data

The design hydrograph, which incorporates peak flow, duration, and volume of flood waves, is essential for designing hydraulic structures and flood protection systems. In many cases, the shape of the hydrograph, in addition to its peak value, plays a significant role in accurately representing flood behavior. The paper provides an overview of various machine learning techniques for hydrograph classification and clustering, including unsupervised cluster analysis, K means clustering, fuzzy c-means, and parameter-based classification. Additionally, it presents advanced methods such as bivariate copula analysis and functional data representation for modeling flood wave shapes. These approaches provide a more flexible and data-driven framework for analyzing flood events, offering advantages over traditional methods. As an example, the paper presents results from K-means shape-based clustering of flood waves represented by Bspline functions on Kupa and Krapina River catchments. The paper also highlights the potential for applying these techniques to different catchments and their broader applicability in hydraulic design.