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Related Experiment Video

Updated: Jun 8, 2026

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds
12:50

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Published on: September 26, 2017

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

Bin He1, Taikan Oki, Fubao Sun

  • 1Center for Promotion of Interdisciplinary Education and Research, Educational Unit for Adaptation and Resilience for Sustainable Society, Kyoto University, Japan. hebin@flood.dpri.kyoto-u.ac.jp

Journal of Environmental Management
|September 28, 2010
PubMed
Summary

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Artificial Neural Networks (ANNs) accurately predict stream total nitrogen concentration (TNC) in Japan. Key factors influencing TNC include land use, fertilizer, and weather conditions, making ANNs valuable for water resource management.

Area of Science:

  • Environmental Science
  • Water Quality Monitoring
  • Computational Hydrology

Background:

  • Predicting non-linear environmental system behavior is challenging.
  • Total Nitrogen Concentration (TNC) is a critical water quality parameter.
  • Understanding factors influencing TNC is vital for water resource management.

Purpose of the Study:

  • To investigate the relationships between land use, fertilizer, hydrometeorological conditions, and TNC in Japanese river basins.
  • To apply a feed-forward Artificial Neural Network (ANN) model for estimating monthly river TNC.
  • To assess the predictive accuracy and utility of the ANN model for TNC estimation.

Main Methods:

  • Utilized a feed-forward Artificial Neural Network (ANN) model.
  • Analyzed data from 59 river basins across Japan.

Related Experiment Videos

Last Updated: Jun 8, 2026

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds
12:50

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Published on: September 26, 2017

  • Performed sensitivity analysis to identify key influencing factors on TNC.
  • Calibrated and validated the ANN model using measured TNC data.

    • Main Results:

    • Precipitation, temperature, river discharge, forest area, and urban area showed significant relationships with TNC.
    • An ANN structure with eight inputs and a hidden layer of seven nodes provided the best TNC estimates.
    • The ANN model achieved high accuracy, with coefficients of error of 0.98 for calibration and 0.93 for validation.
    • Predicted TNC values closely aligned with measured values.

    Conclusions:

    • The ANN model demonstrated satisfactory predictive performance for stream TNC in Japanese streams.
    • ANNs are a useful tool for predicting TNC, even in ungauged rivers.
    • The model provides a valuable resource for water managers to assess TNC variations.