Estimating monthly total nitrogen concentration in streams by using artificial neural network

Abstract

Artificial Neural Network (ANN) is a flexible and popular tool for predicting the non-linear behavior in the environmental system Here the feed-forward ANN model was used to investigate the relationship among the land use fertilizer and hydrometerological conditions in 59 river basins over Japan and then applied to estimate the monthly river total nitrogen concentration (TNC) It was shown by the sensitivity analysis that precipitation temperature river discharge forest area and urban area have high relationships with TNC The ANN structure having eight inputs and one hidden layer with seven nodes gives the best estimate of TNC The 1 1 scatter plots of predicted versus measured TNC were closely aligned and provided coefficients of errors of 0 98 and 0 93 for ANNs calibration and validation respectively From the results obtained the ANN model gave satisfactory predictions of stream TNC and appears to be a useful tool for prediction of TNC in Japanese streams It indicates that the ANN model was able to provide accurate estimates of nitrogen concentration in streams Its application to such environmental data will encourage further studies on prediction of stream TNC in ungauged rivers and provide a useful tool for water resource and environment managers to obtain a quick preliminary assessment of TNC variations (C) 2010 Elsevier Ltd All rights reserved