The use of Kohonen neural networks for runoff-erosion modeling.

Purpose: We investigated the application of Kohonen Neural Networks (KNNs) in order to estimate sediment yield based on runoff and climatological data in a semiarid region of Brazil. Accurate estimations of sediment yield are essential to improve the management of soil erosion in semiarid areas, where large quantities of sediments tend to be produced only periodically. Materials and methods: The case study is an erosion plot within the São João do Cariri Experimental Basin, which is located in the semiarid portion of Paraíba State, Brazil. KNNs are unsupervised neural networks capable of reducing a multidimensional data set to a bidimensional matrix of features, which can be used for analysis and prediction purposes. A total of 60 rainfall events, which occurred between 1999 and 2002, were used to calibrate and test the model. The application of a multivariate linear regression (MLR) model was also carried out. Results and discussion: Statistical indexes were used as criteria for evaluating the performance of the KNN and MLR models for the test data set. The correlation and relative bias of the KNN model estimations with those from observed data were 0.90 and −4.39 %, respectively. A correlation of 0.70 and a relative bias of 15.63 % were found from the comparison of sediment yields obtained by the MLR model with those of the observed data. Analysis of the outcomes indicates that the KNN model, which is capable of detecting and extracting nonlinear trends, produced more reliable results than the regression model. Conclusions: The KNN model results appear to be superior to those generated by the MLR model and suggest that the developed methodology may be applied to similar case studies. [ABSTRACT FROM AUTHOR]