Using data mining to investigate interaction between channel characteristics and hydraulic geometry channel types

Abstract

Data was mined for the purpose of extracting data from an online source to compute and classify hydraulic geometry as well as providing additional data (channel stability, material, and evenness) for pattern discovery. Hydraulic geometry, the relationships between a stream's geometry (width and depth) and flow (velocity and discharge), is applicable to flood prediction, water resources management, and modeling point sources of pollution. Although data to compute hydraulic geometry and additional channel data are freely available online, a systematic data mining approach is seldom if ever used for classification of hydraulic geometry and discernment of regional trends encompassing multi-state areas. In this paper, a method for computing and classifying hydraulic geometry from mined channel flow and geometry data from several states was introduced. Additional channel characteristics (stability, evenness, and material) were also mined. Channels were mapped by stability and a scatterplot matrix revealed no anomalies in the hydraulic geometry of individual channel sections. To assess the quality of data output, statistical analyses were conducted to show that our mined data were comparable to data from the literature as indicated by Euclidean distances between multivariate means, histograms of frequency distributions of hydraulic exponents, and Spearman's rank order correlation applied to channel types. Channels exhibited significant interaction between stability and material, between stability and evenness, but not between material and evenness. Boundary lines through the classification diagram were effective in discriminating stability and material but not evenness.