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

Bin He, Taikan Oki, Fubao Sun, Daisuke Komori, Shinjiro Kanae, Yi Wang, Hyungjun Kim, Dai Yamazaki

Research output: Contribution to journalArticlepeer-review

56 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)172-177
Number of pages6
JournalJournal of Environmental Management
Issue number1
Publication statusPublished - 2011 Jan
Externally publishedYes


  • Artificial neural network
  • Land use
  • Nitrogen concentration
  • Stream water

ASJC Scopus subject areas

  • Environmental Engineering
  • Waste Management and Disposal
  • Management, Monitoring, Policy and Law


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