Model generalization of two different drainage patterns by self-organizing maps - Sen, Alper; Gokgoz, Turkay; Sester, Monika (TAYLOR & FRANCIS INC, 2014)
In this study, we develop a new method using self-organizing maps (SOMs) for the selection of hydrographic model generalization. The most suitable attributes of the stream objects are used as input variables to the SOM. The attributes were weighted using Pearson's chi-square independence test. We used the Radical Law to determine how many features should be selected, and an incremental approach was developed to determine which clusters should be selected from the SOM. Two drainage patterns (dendritic and modified basic) were obtained from the National Hydrography Datasets of United States Geological Survey at 1: 24,000-scale (high resolution) and used in order to derive stream networks at 1: 100,000-scale (medium resolution). The 1: 100,000-scale stream networks, derived in accordance with the proposed approach, are similar to those in the original maps in both quantity and visual aspects. Stream density and pattern were maintained in each subunit, and continuous and semantically correct networks were obtained.
Keywords: hydrographic model generalization; selection; self-organizing maps; clustering; chi-square; neural networks; Geography