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Review of Nitrogen Compounds Prediction in Water Bodies Using Artificial Neural Networks and Other Models

Author

Listed:
  • Pavitra Kumar

    () (Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Sai Hin Lai

    () (Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Jee Khai Wong

    () (Department of Civil Engineering, College of Engineering, University Tenaga Nasional (UNITEN), Jalan Ikram-UNITEN, Kajang 43000, Selangor, Malaysia
    Institute for Sustainable Energy (ISE), University Tenaga Nasional (UNITEN), Kajang 43000, Selangor, Malaysia)

  • Nuruol Syuhadaa Mohd

    () (Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Md Rowshon Kamal

    () (Department of Biological and Agricultural Engineering, Faculty of Engineering, University Putra Malaysia, Selangor 43400, Malaysia)

  • Haitham Abdulmohsin Afan

    () (Department of Civil Engineering, Al-Maaref University College, Ramadi 31001, Iraq)

  • Ali Najah Ahmed

    () (Institute for Energy Infrastructure (IEI), University Tenaga Nasional (UNITEN), Kajang 43000, Selangor, Malaysia)

  • Mohsen Sherif

    () (Civil and Environmental Eng. Dept., College of Engineering, United Arab Emirates University, Al Ain 15551, UAE
    National Water Center, United Arab Emirate University, Al Ain P.O. Box 15551, UAE)

  • Ahmed Sefelnasr

    () (National Water Center, United Arab Emirate University, Al Ain P.O. Box 15551, UAE)

  • Ahmed El-Shafie

    () (Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

Abstract

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.

Suggested Citation

  • 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, 2020. "Review of Nitrogen Compounds Prediction in Water Bodies Using Artificial Neural Networks and Other Models," Sustainability, MDPI, Open Access Journal, vol. 12(11), pages 1-26, May.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:11:p:4359-:d:363124
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    References listed on IDEAS

    as
    1. Sarangi, A. & Bhattacharya, A.K., 2005. "Comparison of Artificial Neural Network and regression models for sediment loss prediction from Banha watershed in India," Agricultural Water Management, Elsevier, vol. 78(3), pages 195-208, December.
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    More about this item

    Keywords

    nitrogen compound; nitrogen prediction; prediction models; neural network;

    JEL classification:

    • Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics
    • Q0 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General
    • Q2 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation
    • Q3 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation
    • Q5 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics
    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products

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