Review of nitrogen compounds prediction in water bodies using artificial neural networks and other models

Pavitra Kumar, Sai Hin Lai, Jee Khai Wong, Nuruol Syuhadaa Mohd, Md Rowshon Kamal, Haitham Abdulmohsin Afan, Ali Najah Ahmed, Mohsen Sherif, Ahmed Sefelnasr, Ahmed El-Shafie

Research output: Contribution to journalReview articlepeer-review

27 Citations (Scopus)


The prediction of nitrogen not only assists in monitoring the nitrogen concentration in streams but also helps in optimizing the usage of fertilizers in agricultural fields. A precise prediction model guarantees the delivering of better-quality water for human use, as the operations of various water treatment plants depend on the concentration of nitrogen in streams. Considering the stochastic nature and the various hydrological variables upon which nitrogen concentration depends, a predictive model should be efficient enough to account for all the complexities of nature in the prediction of nitrogen concentration. For two decades, artificial neural networks (ANNs) and other models (such as autoregressive integrated moving average (ARIMA) model, hybrid model, etc.), used for predicting different complex hydrological parameters, have proved efficient and accurate up to a certain extent. In this review paper, such prediction models, created for predicting nitrogen concentration, are critically analyzed, comparing their accuracy and input variables. Moreover, future research works aiming to predict nitrogen using advanced techniques and more reliable and appropriate input variables are also discussed.

Original languageEnglish
Article number4359
JournalSustainability (Switzerland)
Issue number11
Publication statusPublished - Jun 1 2020


  • Neural network
  • Nitrogen compound
  • Nitrogen prediction
  • Prediction models

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Renewable Energy, Sustainability and the Environment
  • Environmental Science (miscellaneous)
  • Energy Engineering and Power Technology
  • Management, Monitoring, Policy and Law


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