Camilo A. S. de Farias

The use of Kohonen neural networks for runoff–erosion modeling

PurposeWe 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 methodsThe 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 discussionStatistical 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 −4.39xa0%, respectively. A correlation of 0.70 and a relative bias of 15.63xa0% 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.ConclusionsThe 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.

Sequential Prediction of Daily Groundwater Levels by a Neural Network Model Based on Weather Forecasts

This paper investigates the implementation of an Artificial Neural Network (ANN) model for sequential prediction of daily groundwater levels based on precipitation forecasts. The basic principle of the ANN-based procedure consists of relating previous daily groundwater levels and daily precipitation forecasts in order to predict daily groundwater levels up to seven days ahead. The daily precipitation values up to one week ahead are assumed to be deterministic since meteorological short-range forecasts are generally available. The methodology is applied to the groundwater system of Matsuyama City, Japan. Insufficiency of water is a periodical problem in this city and thus accurate predictions of groundwater levels are very important to improve the water resources management in the region. The excellent accuracy obtained by the ANN model indicates that it is very efficient for the multi-step-ahead prediction of daily groundwater levels. As conclusion, this methodology may provide trustworthy data for the application of models to the sustainable management of Matsuyama’s groundwater system.

Runoff-erosion modeling at micro-watershed scale: a comparison of self-organizing maps structures

BackgroundIn the last decades, everal runoff-erosion models have been proposed to estimate soil erosion, which may lead to loss of fertile land and increase sedimentation and pollution in water bodies. Physically-based erosion models are usually used for such purpose, but a major problem concerning their use is the difficulty to directly measure parameters in the field. This problem can be overcome by exploring empirical models, such as so-called Self-Organizing Maps (SOM). An SOM is a type of Artificial Neural Network (ANN) based on a competitive learning approach for clustering and modeling a variety of databases. Since studies on soil erosion modeling based on SOM are very incipient, we compared some structures of SOM with the purpose of estimating sediment yield based on runoff and climatological data at the micro-watershed scale. The case study was a micro-watershed within the Sumé Experimental Basin, which is located in a semiarid region of Brazil. Different from the conventional ANN, SOM-based models represent a multidimensional data set by means of a bidimensional matrix of features, which may be applied for analysis and estimation purposes. In order to calibrate and validate the proposed SOM structures, we used data from 117 rainfall events that occurred between 1985 and 1991.ResultsAnalyses of the results indicate that all SOM structures were efficiently calibrated with NASH coefficients (Nash & Sutcliffe 1970) varying from 0.88 to 0.90. The SOM structure with 6u2009×u20098 neurons was the most effective for estimating sediment yields when considering the validation data set (NASHu2009=u20090.73). The generated maps showed that sediment yields were directly related to runoff and rainfall intensity and inversely correlated to average vegetation heights. The dry period length did not seem to influence the production of sediments.ConclusionsSOM were shown to be very practical and meant to be applied to specific locations. This type of methodology also demands long term data and dynamic recalibration with up-to-date information in order to account for changes in the watershed.