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 and Ahmed El-Shafie
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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
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
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

Sustainability, 2020, vol. 12, issue 11, 1-26

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.

Keywords: nitrogen compound; nitrogen prediction; prediction models; neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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