Next Article in Journal
Determinants of Farming Households’ Credit Accessibility in Rural Areas of Vietnam: A Case Study in Haiphong City, Vietnam
Next Article in Special Issue
Estimation of Daily Stage–Discharge Relationship by Using Data-Driven Techniques of a Perennial River, India
Previous Article in Journal
Method for Quantifying Supply and Demand of Construction Minerals in Urban Regions—A Case Study of Hanoi and Its Hinterland
Previous Article in Special Issue
Modeling and Uncertainty Analysis of Groundwater Level Using Six Evolutionary Optimization Algorithms Hybridized with ANFIS, SVM, and ANN
Review

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

1
Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
2
Department of Civil Engineering, College of Engineering, University Tenaga Nasional (UNITEN), Jalan Ikram-UNITEN, Kajang 43000, Selangor, Malaysia
3
Institute for Sustainable Energy (ISE), University Tenaga Nasional (UNITEN), Kajang 43000, Selangor, Malaysia
4
Department of Biological and Agricultural Engineering, Faculty of Engineering, University Putra Malaysia, Selangor 43400, Malaysia
5
Department of Civil Engineering, Al-Maaref University College, Ramadi 31001, Iraq
6
Institute for Energy Infrastructure (IEI), University Tenaga Nasional (UNITEN), Kajang 43000, Selangor, Malaysia
7
Civil and Environmental Eng. Dept., College of Engineering, United Arab Emirates University, Al Ain 15551, UAE
8
National Water Center, United Arab Emirate University, Al Ain P.O. Box 15551, UAE
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(11), 4359; https://doi.org/10.3390/su12114359
Received: 22 April 2020 / Revised: 21 May 2020 / Accepted: 22 May 2020 / Published: 26 May 2020
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. View Full-Text
Keywords: nitrogen compound; nitrogen prediction; prediction models; neural network nitrogen compound; nitrogen prediction; prediction models; neural network
Show Figures

Figure 1

MDPI and ACS Style

Kumar, P.; Lai, S.H.; Wong, J.K.; Mohd, N.S.; Kamal, M.R.; Afan, H.A.; Ahmed, A.N.; Sherif, M.; Sefelnasr, A.; El-Shafie, A. Review of Nitrogen Compounds Prediction in Water Bodies Using Artificial Neural Networks and Other Models. Sustainability 2020, 12, 4359. https://doi.org/10.3390/su12114359

AMA Style

Kumar P, Lai SH, Wong JK, Mohd NS, Kamal MR, Afan HA, Ahmed AN, Sherif M, Sefelnasr A, El-Shafie A. Review of Nitrogen Compounds Prediction in Water Bodies Using Artificial Neural Networks and Other Models. Sustainability. 2020; 12(11):4359. https://doi.org/10.3390/su12114359

Chicago/Turabian Style

Kumar, Pavitra, Sai H. Lai, Jee K. Wong, Nuruol S. Mohd, Md R. Kamal, Haitham A. Afan, Ali N. Ahmed, Mohsen Sherif, Ahmed Sefelnasr, and Ahmed El-Shafie. 2020. "Review of Nitrogen Compounds Prediction in Water Bodies Using Artificial Neural Networks and Other Models" Sustainability 12, no. 11: 4359. https://doi.org/10.3390/su12114359

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop