Shared near neighbours neural network model: a debris flow warning system

The main purpose of this study is to develop a new type of artificial neural network based model for constructing a debris flow warning system. The Chen-Eu-Lan river basin, which is located in Central Taiwan, is assigned as the study area. The creek is one of the most well-known debris flow areas where several damaging debris flows have been reported in the last two decades. The hydrological and geological data, which might have great influence on the occurrence of debris flows, are first collected and analysed, then, the shared near neighbours neural network (SNN + NN) is presented to construct the debris flow warning system for the watershed. SNN is an unsupervised learning method that has the advantage of dealing with non-globular clusters, besides presenting computational efficiency. By using SNN, the compiled hydro-geological data set can easily and meaningfully be clustered into several categories. These categories can then be identified as 'occurrence' or 'no-occurrence' of debris flows. To improve the effectiveness of the debris flow warning system, a neural network framework is designed to connect all the, clusters produced by the SNN method, whereas the connected weights of the network are adjusted through a supervised learning method. This framework is used and its applicability and practicability for debris flow warning are investigated. The results demonstrate that the proposed SNN + NN model is an efficient and accurate tool for the development of a debris flow warning system. Copyright (c) 2007 John Wiley & Sons, Ltd.