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Related Concept Videos

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Nitrogen is an essential element in biological systems, forming a crucial component of proteins, nucleic acids, and other cellular constituents. Many bacteria and archaea acquire nitrogen in the form of nitrate (NO₃⁻) or ammonia (NH₃), which are then assimilated into biomolecules through specific enzymatic pathways.Assimilatory Nitrate ReductionWhen nitrate enters the cell, it undergoes a two-step reduction process known as assimilatory nitrate reduction. Initially, the enzyme...
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Related Experiment Video

Updated: Dec 7, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

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Optimised neural network model for river-nitrogen prediction utilizing a new training approach.

Pavitra Kumar1, Sai Hin Lai1, Nuruol Syuhadaa Mohd1

  • 1Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia.

Plos One
|September 28, 2020
PubMed
Summary

This study developed optimal artificial neural network models to predict nitrate-nitrogen and ammonia-nitrogen in Malaysian rivers. The models achieved high accuracy, offering a promising solution for managing water quality and preventing pollution.

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Last Updated: Dec 7, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.8K

Area of Science:

  • Environmental Science
  • Water Resource Management
  • Artificial Intelligence

Background:

  • Rising nitrogenous compounds (nitrate-nitrogen, ammonia-nitrogen) in rivers threaten water quality, causing eutrophication and 'blue baby syndrome'.
  • Agricultural and industrial activities are primary drivers of increased nitrogen pollution in river systems.
  • Existing prediction models often lack comprehensive testing of various architectures and specific hydrological conditions.

Purpose of the Study:

  • To develop and select an optimal artificial neural network (ANN) model for predicting monthly average nitrate-N and ammonia-N concentrations.
  • To train and evaluate different ANN architectures, including General Regression Neural Network (GRNN), Multilayer Neural Network, and Radial Basis Function Neural Network (RBFNN).
  • To tailor models for specific hydrological conditions, using data from the Langat River, Malaysia.

Main Methods:

  • Training multiple ANN models with various internal parameters, input variables, and architectures.
  • Utilizing hydrological data from 1981 to 2017 for the Langat River, Selangor, Malaysia.
  • Selecting the optimum model based on regression values, error metrics, and predicted versus observed value plots.

Main Results:

  • Developed ANN models demonstrating high predictive accuracy for nitrate-N and ammonia-N.
  • Achieved a minimum overall regression value of 0.92 for the optimum models.
  • The selected models provide promising results for forecasting nitrogenous compound concentrations.

Conclusions:

  • Optimal ANN models can effectively predict nitrogenous compounds in river water.
  • The developed approach offers a robust method for water quality management and pollution control.
  • This study highlights the importance of model selection and training for specific environmental conditions.