Modeling hydrologic and geomorphic hazards across post-fire landscapes using a self-organizing map approach

Abstract

Few studies attempt to model the range of possible post-fire hydrologic and geomorphic hazards because of the sparseness of data and the coupled, nonlinear, spatial, and temporal relationships among landscape variables. In this study, a type of unsupervised artificial neural network, called a self-organized map (SOM), is trained using data from 540 burned… (More)
DOI: 10.1016/j.envsoft.2011.07.001

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