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    Topic review

    Nitrogen Compounds Prediction

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    Submitted by: Pavitra Kumar

    Definition

    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

    1. Introduction

    Human activities have provoked serious effects on nutrient cycle, streams ecological functioning, and water quality [1][2][3]. Presently, agriculture production consummately depends on the amount of fertilizers and pesticides used. Fertilizers mainly contain nitrogen compared with other chemicals. Crops require nitrogen for their growth and for the production of fruits or grains. Some agricultural specialists have also recommended using the fertilizers that carry a higher percentage of nitrogen [4]. However, only 40%–70% of nitrogen compounds applied as fertilizers are absorbed by the crops. The remaining nitrogen compounds either percolate downward with water to join groundwater or flow along with the runoff water to join the streams [5][6]. In both cases, the nitrogen concentration in water escalates, which can affect human health [7][8][9]. If pesticides and fertilizers are added to the fields at a high rate, there is more chance for nitrate to percolate to the aquifer, increasing the nitrate level in groundwater [10][11][12]. In warmer countries, the loss of total nitrogen is more, as mineralization rate is probably higher due to the higher temperature; thus, the percolation of total nitrogen is increased [13].

    The major proportion of the surplus nitrogen is transported by the runoff water to the streams, and consequently, nitrogen compounds such as ammonia-nitrogen, nitrite, and nitrate, are escalated in the streams. A surfeit of nitrogen in streams seems to be deleterious for both human beings and aquatic lives. In water bodies, it may lead to the magnification of aquatic plants and algae, which can result in the depletion of dissolved oxygen and hinder the contact of water with air and light. The presence of such excess nitrogen in drinking water reduces the amount of oxygen transported in the blood [5]. Mostly, treatment plants are not designed for the full removal of nitrogen compounds from river water. In China, sewage treatment systems remove total nitrogen by 40%–70% [14][15]. In Malaysia, sewage treatment plants are not designed for ammonia removal [16]. Recently, several water treatment plants have been forced to shut down when, after testing the samples, it was found that ammonia-nitrogen pollution has crossed the acceptable limit in different rivers in Malaysia. The abrupt closure of the water treatment plant affects the water supply to the consumers. Thus, adding additional pressure on the government for arranging alternate source of water supply.

    The lack of monitoring systems leads to an abrupt increase in pollution, which can result in the closure of the water treatment plants. Monitoring systems should contain a proper predictive system: which works based on the historical data; and a treatment system: that deals with the nitrogen pollutant, should be developed in treatment plants. Predictive systems could provide the daily data of pollutants and thus save the daily effort of quantifying such data in the laboratory. Moreover, predictive systems would create an alert for nitrogen surge in rivers before it actually happens. Hence, the government would have ample time to optimize various nitrogen inputs in the rivers. Different river basins require separate predictive model, trained on historical data of basin’s parameters because a model well-trained on historical data of one particular basin, not necessarily will perform with same accuracy on different basins. Hence, the government require separate predictive model for each basin. Also, to consider the upcoming seasonal changes the predictive models need to be re-trained with the real-time data quarterly or yearly basis. Observing the increased pollution of nitrogen in rivers, this topic becomes important to be evaluated.

    ANN models have been utilized for developing better-precision water quality predictive models [17][18][19][20][21]. The computational intelligence, among which ANN is one, has become a fast-evolving area [22]. The applications of ANN are not limited to water quality prediction. According to He, Oki, Sun, Komori, Kanae, Wang, Kim and Yamazaki [18], ANNs have been successfully used for reservoir operations [23][24][25][26][27], water resources management [28][29], and hydrological processes [30][31]. Application in water resources management includes river flow forecasting [32][33], rainfall-runoff modeling [31][34], and water quality predictions [35][36][37][38]. The present study is confined to water quality predictive systems only.

    2. Effects of Nitrogen

    Nitrogen, if present in river water causes different disorders, which are deleterious for both human and aquatic animals. Nitrogen present in streams are mainly found in three compound states: ammonia, nitrate, and nitrite. Some amounts of ammonia present in the river water get converted to nitrate depending on the dissolved oxygen concentration in the water [39]. As stated earlier, nitrate is not much deleterious, but if present in surplus amount, it starts converting into nitrite, which is very harmful even in minute concentration. The Environmental Protection Agency has set standards that state that for water that is to be distributed for public use, the maximum acceptable nitrate concentration is 10 mg/l [5][25] and that for nitrite is 1 mg/l.

    There are two major effects of ammonia on whole ecosystem: eutrophication of marine and terrestrial ecosystems [40][41] and increase in the acidity of water bodies [42]. Excessive nutrients such as nitrogen and phosphorus when present in water bodies lead to the growth of algae on the top surface of water; this process is termed as eutrophication. Excess grown algae cover the whole water surface, blocking the contact of water from sunlight and air. Additionally, the algae growth decreases the oxygen level in water body, which affects the aquatic lives. Stream eutrophication was recognized as a major problem years ago, and the United States along with other countries commenced nutrient control measures in rivers [43][44].

    Streams may get acidified due to the presence of surfeit ammonia. The most common form of ammonia, ammonium sulphate, leads to formation of considerable amount of acid, as hydrogen ions are released during nitrification. Also, nitrite ions present in the streams lead to the formation of nitric acid under different situations along with sulfate ions, consequently acidifying the stream water [45]. Acidic stream water is not even suitable for reuse to satisfy human water requirements. As stated by Gündüz [46], one day, reuse of treated water would be a reality for the rural population, and this would result in serious problems such as human health issues. Compared with urban areas, agricultural areas are more susceptible to health risks by the presence of nitrate-nitrogen in groundwater [47][48].

    Nitrite has been found to be more toxic than nitrate and if present in drinking water can cause human health problems such as liver damage and in worst cases can lead to various types of cancer [49] and two types of birth defects [50][51]. Nitrite present in surplus quantity in drinking water will eventually lower the ability of bloodstreams to carry oxygen, leading to the lack of oxygen in the body. Infants and young livestock are lamentably affected, as this causes “blue baby syndrome” [49]. The reaction of nitrites with amines either enzymatically or chemically leads to the formation of potent carcinogenic nitrosamines [49][52].

    Consumption of nitrates leads to various tumors in the human body [49][53]. In the digestive system, nitrate leads to the formation of N-nitroso compounds [49][54], which are considered to be carcinogenic. Iodine uptakes can be restricted by nitrates, causing thyroid-related problems [49].

    3. ANN

    ANN is a black-box computational model [55] that contains interconnected network-like structures passing values to other nodes of the connections. It contains an input layer, hidden layers as required, and an output layer. It is well known for its capability of predicting the non-linear variables [56]. ANN forms the same structure as neurons in the human brain [6][20][57]. It functions like a biological neuron, receiving the input as stimulus, evaluating the stimulus, and then providing the output as the response to the stimulus. Figure. 1 represents a simple example of the neural network. The inputs are fed to the nodes in the input layer, and those nodes pass the values of input data to the nodes in hidden layer 1 via interconnecting links. As the values are passed from input nodes to the following nodes, it is multiplied with the weights and then passed to the corresponding layer through a transfer function [58]. Likewise, it is passed up to the output layer, where the error is calculated using target vector. Based on this error, weights get adjusted to obtain the exact weighted combination of the input data for forecasting the target vector.

    Figure 1. Basic structure of neural network.

    The major advantage of application of ANN model, over traditional model, such as statistical model, is that it learns itself the complexity of nature, without being explicitly transformed into mathematical form [59][60]. Statistical models, have a limitation of assuming additional information to derive a sharp conclusion [61]. The major disadvantage of ANN is that it is susceptible to overfitting. Overfitting is the state in training, beyond which, training error decreases but the model starts losing its ability of generalizing the relation between input and output for the new data set i.e. the testing set data. This results in increasing the testing error and decreasing the overall performance of the model. There are several ways to prevent the model from overfitting, among which well-known method is early-stopping; in which training process is stopped early. But if the training is stopped too early then model fails to learn important information. Hence, training should be stopped accordingly to learn all important information without overfitting.

    Many types of ANNs feature different concepts of data processing. Each type is designed differently to obtain a more precise output with less data processing time. This is achieved by changing the networks architecture. According to Jain, et al. [62], based on the network connection pattern, i.e., their architecture, ANN is classified into two categories:

    3.1. Feed-Forward Neural Networks (FFNNs)

    FFNN has the simplest network connection pattern in which data flow in the forward direction only, starting from the input layer to hidden layers, and then to the output layer. No loops are formed in the paths of the data flow. As shown in Figure. 2, FFNN is classified into three subcomponents: single-layer perceptron, multilayer perceptron, and radial basis function neural network (RBFNN). Single-layer perceptron, which consists of one layer, i.e., the output layer, is the simplest form of neural network. It is mainly used for classifying the linearly separable cases that use binary targets. The connection patterns of multilayer perceptron and RBFNN are the same: an input layer, as many hidden layers as required, and an output layer. The only difference between these two is the use of the data processing function. Multilayer perceptron utilizes either threshold function or sigmoidal function [63] in each of its computational units, whereas RBFNN utilizes radial basis function as the activation function in each unit of its hidden layers. The table 1 presents the advantages and disadvantages of different models of FFNN. These models are generally used for time series prediction, system control, and data classification.

    3.2. Recurrent or Feedback Neural Networks

    Recurrent or feedback neural networks experience the backward flow of data in some computational cells. The data flow is not unidirectional; loops within the cells transfer back the feedback of the errors encountered in computations, with reference to the target values. The feedback of errors helps in updating the weights of the corresponding inputs. As shown in Figure. 2, feedback neural network is classified into four subcomponents: adaptive resonance theory model, Hopfield networks, Kohonen’s networks, and competitive networks. Table 1 presents their advantages and disadvantages. These networks form very complex architectures, composed of a number of loops. These networks are utilized for complex computations, such as speech recognition, image processing, robotics, and process controls. This study is limited to the review of the FFNN.

    Figure 2. Classification of neural network [62].

    Table 1. Advantages and disadvantages of different ANN models

    Main Type Model Name Advantages Disadvantages
    FFNN
    (Feed-Forward Neural Network)
    Single Layer Perceptron
    • Less computation—time

    Easy to setup
    • Can only be used in linearly separable data
    Multi-Layer Perceptron
    • Can be used for complex problems
    • Need more time for training
    • Can get stuck in local minima
    RBFNN
    • Less susceptible to be stuck in local minima
    • Can tolerate with input noise
    • Classification is slow, as the network have to calculate the radial basis function for each input vector during classification
    Recurrent/Feedback Network ART (Adaptive Resonance Theory) model
    • Can be integrated with other models to enhance the performance
    • Some ART models are inconsistent. They depend upon the order of the training data
    Hopfield Network
    • No training needed
    • Handles a smaller number of memories.
    • More number of patterns results in spurious output
    Kohonen’s SOM
    • Provides deterministic and reproducible results
    • Simplicity of computation
    • Performance depends on initialization
    Competitive Network
    • Groups the similar pattern based on the data correlation
    • Susceptible to stability issue

    4. Hybrid Model

    Hybrid model is the combination of different models to solve a computational task. The need of hybridization aroused when the learning models were observed to be very efficient in some cases and inefficient in most of the cases [64]. The main aim of Hybridization is to resolve the limitations of individual model by fusion of decision making models with learning models [65]. The main advantage of hybrid model is that it provides better results in comparison to the standalone model. The decision making model integrated in hybrid model provides good start with selected initial values of the internal parameters of learning models. Hence, increasing the productivity of the learning model. The disadvantages of the hybrid models are: overall training process is time consuming, complex architecture and training requires modern computational resources. Some of the examples of hybrid models are [66]:

    • ANN and genetic algorithm

    • ANN and fruit fly optimization algorithm

    • ANN and firefly algorithm

    • ANN and artificial immune systems

    • ANN and particle swarm-optimization algorithm

    The following authors have developed various predictive models for nitrogen prediction in water bodies:

    Table 2. A summary of studies that utilize ANN model for nitrogen prediction, including their specific area, location, and methods used.

      Authors Specific Area Location Method
    1 Anctil, Filion and Tournebize [57] Streams Melarchez, France Stacked multilayer perceptron
    2 He, Oki, Sun, Komori, Kanae, Wang, Kim and Yamazaki [18] Streams Japan Feed-forward model
    3 Holmberg, Forsius, Starr and Huttunen [19] Streams Finland Backpropagation algorithm
    4 Lek, Guiresse and Giraudel [20] Streams The United States Multilayer feed-forward
    5 Suen and Eheart [25] Streams Illinois, The United States Backpropagation and radial basis
    6 Sharma, Negi, Rudra and Yang [6] Drainage water Canada Fast backpropagation and self-organizing radial basis
    7 Wang et al. [67] Groundwater Australia 13 machine learning models
    8 Zhang et al. [68] Lake China ARIMA, radial basis, and hybrid
    9 Markus et al. [69] Streams Illinois Backpropagation, Evolutionary Polynomial Regression (EPR), and Naïve Bayes Model (NBM)
    10 Amiri and Nakane [70] Stream Japan Backpropagation and Multiple Linear Regression (MLR)
    11 Zeleňáková et al. [71] Streams Slovakia Dimensional analysis

    This entry is adapted from 10.3390/su12114359

    References

    1. Kelly O. Maloney; Donald E. Weller; Anthropogenic disturbance and streams: land use and land-use change affect stream ecosystems via multiple pathways. Freshwater Biology 2011, 56, 611-626, 10.1111/j.1365-2427.2010.02522.x.
    2. Fidelis Kilonzo; Frank O. Masese; Ann Van Griensven; Willy Bauwens; Joy Obando; Piet N.L. Lens; Spatial–temporal variability in water quality and macro-invertebrate assemblages in the Upper Mara River basin, Kenya. Physics and Chemistry of the Earth, Parts A/B/C 2014, 67, 93-104, 10.1016/j.pce.2013.10.006.
    3. Suzanne Jacobs; Lutz Breuer; K. Butterbach‐Bahl; David E Pelster; Mariana C. Rufino; Land use affects total dissolved nitrogen and nitrate concentrations in tropical montane streams in Kenya. Science of The Total Environment 2017, 603, 519-532, 10.1016/j.scitotenv.2017.06.100.
    4. Hessong, A. The Composition of Fertilizers. Availabe online: http://homeguides.sfgate.com/composition-fertilizers-48898.html(accessed on 26-06-2019).
    5. F. Salehi; S. O. Prasher; S. Amin; A. Madani; S. J. Jebelli; H. S. Ramaswamy; C. Tan; Craig F. Drury; PREDICTION OF ANNUAL NITRATE-N LOSSES IN DRAIN OUTFLOWS WITH ARTIFICIAL NEURAL NETWORKS. Transactions of the ASAE 2000, 43, 1137-1143, 10.13031/2013.3006.
    6. V Sharma; S.C. Negi; R.P Rudra; Simon X. Yang; Neural networks for predicting nitrate-nitrogen in drainage water. Agricultural Water Management 2003, 63, 169-183, 10.1016/s0378-3774(03)00159-8.
    7. L Fewtrell; Drinking-Water Nitrate, Methemoglobinemia, and Global Burden of Disease: A Discussion. Environmental Health Perspectives 2004, 112, 1371-1374, 10.1289/ehp.7216.
    8. Gallo, E.L.; Meixner, T.; Aoubid, H.; Lohse, K.A.; Brooks, P.D; Combined impact of catchment size, land cover, and precipitation on streamflow and total dissolved nitrogen: A global comparative analysis. Global Biogeochemical Cycles 2015, 29, 1109-1121, .
    9. Ward, M.H.; deKok, T.M.; Levallois, P.; Brender, J.; Gulis, G.; Nolan, B.T.; VanDerslice, J; International Society for Environmental, E. Workgroup report: Drinking-water nitrate and health--recent findings and research needs. Environmental Health Perspect 2005, 113, 1607-1614, .
    10. Reddy, A.G.S.; Niranjan Kumar, K.; Subba Rao, D.; Sambashiva Rao, S; Assessment of nitrate contamination due to groundwater pollution in north eastern part of Anantapur District, A.P. India. Environmental Monitoring and Assessment 2009, 148, 463-476, .
    11. Hamed, Y.; Awad, S.; Ben Sâad, A; Nitrate contamination in groundwater in the Sidi Aïch–Gafsa oases region, Southern Tunisia. Environmental Earth Sciences 2013, 70, 2335-2348, .
    12. Omid Rahmati; Aliakbar Nazari Samani; Nariman Mahmoodi; Mohammad Mahdavi; Assessment of the Contribution of N-Fertilizers to Nitrate Pollution of Groundwater in Western Iran (Case Study: Ghorveh–Dehgelan Aquifer). Water Quality, Exposure and Health 2015, 7, 143-151, 10.1007/s12403-014-0135-5.
    13. A. Räike; O.-P. Pietiläinen; S. Rekolainen; P. Kauppila; H. Pitkänen; J. Niemi; A. Raateland; J. Vuorenmaa; Trends of phosphorus, nitrogen and chlorophyll a concentrations in Finnish rivers and lakes in 1975–2000. Science of The Total Environment 2003, 310, 47-59, 10.1016/s0048-9697(02)00622-8.
    14. Yong Qiu; Han-Chang Shi; Miao He; Nitrogen and Phosphorous Removal in Municipal Wastewater Treatment Plants in China: A Review. International Journal of Chemical Engineering 2010, 2010, 1-10, 10.1155/2010/914159.
    15. Wangshou Zhang; Dennis Swaney; Xuyong Li; B. Hong; Robert Howarth; S. H. Ding; Anthropogenic point-source and non-point-source nitrogen inputs into Huai River basin and their impacts on riverine ammonia–nitrogen flux. Biogeosciences 2015, 12, 4275-4289, 10.5194/bg-12-4275-2015.
    16. Indah Water, M. Ammonia. Availabe online: https://www.iwk.com.my/do-you-know/ammonia (accessed on 06-07-2019).
    17. Shoshana Fogelman; Michael Blumenstein; Huijun Zhao; Estimation of chemical oxygen demand by ultraviolet spectroscopic profiling and artificial neural networks. Neural Computing and Applications 2005, 15, 197-203, 10.1007/s00521-005-0015-9.
    18. Bin He; Taikan Oki; Fubao Sun; Daisuke Komori; Shinjiro Kanae; Yi Wang; Hyungjun Kim; Dai Yamazaki; Estimating monthly total nitrogen concentration in streams by using artificial neural network. Journal of Environmental Management 2011, 92, 172-177, 10.1016/j.jenvman.2010.09.014.
    19. Maria Holmberg; Martin Forsius; Michael Starr; Markus Huttunen; An application of artificial neural networks to carbon, nitrogen and phosphorus concentrations in three boreal streams and impacts of climate change. Ecological Modelling 2006, 195, 51-60, 10.1016/j.ecolmodel.2005.11.009.
    20. Sovan Lek; Predicting stream nitrogen concentration from watershed features using neural networks. Water Research 1999, 33, 3469-3478, 10.1016/s0043-1354(99)00061-5.
    21. A. Sarangi; A.K. Bhattacharya; Comparison of Artificial Neural Network and regression models for sediment loss prediction from Banha watershed in India. Agricultural Water Management 2005, 78, 195-208, 10.1016/j.agwat.2005.02.001.
    22. Mohammed Ehteram; Hojat Karami; Sayed-Farhad Mousavi; Ahmed El-Shafie; Zahra Amini; Mohammed Ehtram; Optimizing dam and reservoirs operation based model utilizing shark algorithm approach. Knowledge-Based Systems 2017, 122, 26-38, 10.1016/j.knosys.2017.01.026.
    23. Pedro A. Aguilera; Antonia Garrido Frenich; J A Torres; H Castro; J L Martinez Vidal; M Canton; Application of the Kohonen neural network in coastal water management: methodological development for the assessment and prediction of water quality.. Water Research 2001, 35, 4053-4062, 10.1016/s0043-1354(01)00151-8.
    24. Li-Chiu Chang; Li-Chiu Chang; Intelligent control for modelling of real-time reservoir operation. Hydrological Processes 2001, 15, 1621-1634, 10.1002/hyp.226.
    25. Jian-Ping Suen; J. Wayland Eheart; Evaluation of Neural Networks for Modeling Nitrate Concentrations in Rivers. Journal of Water Resources Planning and Management 2003, 129, 505-510, 10.1061/(asce)0733-9496(2003)129:6(505).
    26. Zaheer, I.; Bai, C.-G; Application of artificial neural network for water quality management. Lowland Technology International 2003, 5, 10-15, .
    27. Gokmen Tayfur; Dorota Swiatek; Andrew Wita; Vijay P. Singh; Case Study: Finite Element Method and Artificial Neural Network Models for Flow through Jeziorsko Earthfill Dam in Poland. Journal of Hydraulic Engineering 2005, 131, 431-440, 10.1061/(asce)0733-9429(2005)131:6(431).
    28. D. Mazvimavi; A.M.J. Meijerink; Hubert H. G. Savenije; A. Stein; Prediction of flow characteristics using multiple regression and neural networks: A case study in Zimbabwe. Physics and Chemistry of the Earth, Parts A/B/C 2005, 30, 639-647, 10.1016/j.pce.2005.08.003.
    29. Bin He; Keiji Takase; Application of the Artificial Neural Network Method to Estimate the Missing Hydrologic Data. JOURNAL OF JAPAN SOCIETY OF HYDROLOGY AND WATER RESOURCES 2006, 19, 249-257, 10.3178/jjshwr.19.249.
    30. Cigizoglu, H.K.; Alp, M. Rainfall-Runoff Modelling Using Three Neural Network Methods. In Proceedings of ICAISC: International Conference on Artificial Intelligence and Soft Computing, Zakopane, Poland, June 7-11; pp. 166-171.
    31. Riad, S.; Mania, J.; Bouchaou, L.; Najjar, Y; Rainfall-runoff model usingan artificial neural network approach. Mathematical and Computer Modelling 2004, 40, 839-846, .
    32. A Y Shamseldin; A. E. Nasr; K. M. O’Connor; Comparison of different forms of the Multi-layer Feed-Forward Neural Network method used for river flow forecasting. Hydrology and Earth System Sciences 2002, 6, 671-684, 10.5194/hess-6-671-2002.
    33. R. Teschl; W. L. Randeu; A neural network model for short term river flow prediction. Natural Hazards and Earth System Sciences 2006, 6, 629-635, 10.5194/nhess-6-629-2006.
    34. Wu, C.L.; Chau, K.W; Rainfall–runoff modeling using artificial neural network coupled with singular spectrum analysis. Journal of Hydrology 2011, 399, 394-409, .
    35. Gazzaz, N.M.; Yusoff, M.K.; Aris, A.Z.; Juahir, H.; Ramli, M.F; Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors. Mar Pollut Bull 2012, 64, 2409-2420, .
    36. Khalil, B.; Ouarda, T.B.M.J.; St-Hilaire, A; Estimation of water quality characteristics at ungauged sites using artificial neural networks and canonical correlation analysis. Journal of Hydrology 2011, 405, 277-287, .
    37. Sundarambal Palani; Shie-Yui Liong; Pavel Tkalich; An ANN application for water quality forecasting. Marine Pollution Bulletin 2008, 56, 1586-1597, 10.1016/j.marpolbul.2008.05.021.
    38. Kunwar P. Singh; Ankita Basant; Amrita Malik; Gunja Jain; Artificial neural network modeling of the river water quality—A case study. Ecological Modelling 2009, 220, 888-895, 10.1016/j.ecolmodel.2009.01.004.
    39. M. Nuruzzaman; Abdullah Al Mamun; M. N. B. Salleh; Determining ammonia nitrogen decay rate of Malaysian river water in a laboratory flume. International Journal of Environmental Science and Technology 2017, 15, 1249-1256, 10.1007/s13762-017-1482-0.
    40. Nancy N. Rabalais; R. Eugene Turner; Donald Scavia; Beyond Science into Policy: Gulf of Mexico Hypoxia and the Mississippi River. BioScience 2002, 52, 129, 10.1641/0006-3568(2002)052[0129:bsipgo]2.0.co;2.
    41. N.N. Rabalais; Oxygen depletion in the gulf of Mexico adjacent to the Mississippi river. Gayana (Concepción) 2006, 70, 225-245, 10.4067/s0717-65382006000300015.
    42. Hessen, D.O.; Hindar, A.; Holtan, G; The Significance of Nitrogen Runoff for Eutrophication of Freshwater and Marine Recipients. Royal Swedish Academy of Science 1997, 26, 312-320, .
    43. Walter Dodds; Val H. Smith Department of Ecology and Evolutionary Biology University of Kansas Lawrence KS USA; Nitrogen, phosphorus, and eutrophication in streams. Inland Waters 2016, 6, 155-164, 10.5268/IW-6.2.909.
    44. Walter K. Dodds; Eugene B. Welch; Establishing nutrient criteria in streams. Journal of the North American Benthological Society 2000, 19, 186-196, 10.2307/1468291.
    45. Peter S. Murdoch; John Stoddard; The role of nitrate in the acidification of streams in the Catskill Mountains of New York. Water Resources Research 1992, 28, 2707-2720, 10.1029/92wr00953.
    46. Orhan Gunduz; Water Quality Perspectives in a Changing World. Water Quality, Exposure and Health 2015, 7, 1-3, 10.1007/s12403-015-0161-y.
    47. Xiaosi Su; Huang Wang; Yuling Zhang; Health Risk Assessment of Nitrate Contamination in Groundwater: A Case Study of an Agricultural Area in Northeast China. Water Resources Management 2013, 27, 3025-3034, 10.1007/s11269-013-0330-3.
    48. Song He; Jianhua Wu; Hydrogeochemical Characteristics, Groundwater Quality, and Health Risks from Hexavalent Chromium and Nitrate in Groundwater of Huanhe Formation in Wuqi County, Northwest China. Exposure and Health 2019, 11, 125-137, 10.1007/s12403-018-0289-7.
    49. Fahim Hossain; Ni-Bin Chang; Marty Wanielista; Zhemin Xuan; Ammarin Daranpob; Nitrification and Denitrification in a Passive On-site Wastewater Treatment System with a Recirculation Filtration Tank. Water Quality, Exposure and Health 2010, 2, 31-46, 10.1007/s12403-010-0022-7.
    50. G Gulis; Monika Czompolyova; James R. Cerhan; An Ecologic Study of Nitrate in Municipal Drinking Water and Cancer Incidence in Trnava District, Slovakia. Environmental Research 2002, 88, 182-187, 10.1006/enrs.2002.4331.
    51. Jie Chen; Hao Wu; Hui Qian; Yanyan Gao; Assessing Nitrate and Fluoride Contaminants in Drinking Water and Their Health Risk of Rural Residents Living in a Semiarid Region of Northwest China. Exposure and Health 2016, 9, 183-195, 10.1007/s12403-016-0231-9.
    52. Sawyer CN; McCarty PL; Parkin GF. Chemistry for environmental engineering and science. 5th ed.; McGraw Hill: New York, NY, USA, 2003; p. 667.
    53. Şükrü Aslan; A. Türkman; Biological denitrification of drinking water using various natural organic solid substrates. Water Science and Technology 2004, 48, 489-495, 10.2166/wst.2004.0898.
    54. Claudio Della Rocca; V Belgiorno; Sureyya Meric; Cotton-supported heterotrophic denitrification of nitrate-rich drinking water with a sand filtration post-treatment. Water SA 2007, 31, 229, 10.4314/wsa.v31i2.5177.
    55. Seyed Ahmad Akrami; Ahmed El-Shafie; Othman Jaafar; Improving Rainfall Forecasting Efficiency Using Modified Adaptive Neuro-Fuzzy Inference System (MANFIS). Water Resources Management 2013, 27, 3507-3523, 10.1007/s11269-013-0361-9.
    56. Farzad, F.; El-Shafie, A.H. Performance Enhancement of Rainfall Pattern – Water Level Prediction Model Utilizing Self-Organizing-Map Clustering Method. Water Resour Manage 2016, 10.1007/s11269-016-1556-7, doi:10.1007/s11269-016-1556-7.
    57. Anctil, F.; Filion, M.; Tournebize, J; A neural network experiment on the simulation of daily nitrate-nitrogen and suspended sediment fluxes from a small agricultural catchment. Ecological Modelling 2009, 220, 879-887, .
    58. El-Shafie, A.H.; El-Shafie, A.; Mazoghi, H.G.E.; Shehata, A.; Taha, M.R; Artificial neural network technique for rainfall forecasting applied to Alexandria, Egypt. International Journal of the Physical Sciences 2011, 6, 1306-1316, .
    59. Raju, M.M.; Srivastava, R.K.; Bisht, D.C.S.; Sharma, H.C.; Kumar, A; Development of Artificial Neural-Network-Based Models for the Simulation of Spring Discharge. Advances in Artificial Intelligence 2011, 2011, 1-11, .
    60. Amr H. El Shafie; A. Almukhtar; Mohd. R. Taha; Hasan G. El Mazoghi; A. Shehata; Ahmed El-Shafie; Radial basis function neural networks for reliably forecasting rainfall. Journal of Water and Climate Change 2012, 3, 125-138, 10.2166/wcc.2012.017.
    61. Yu Xie; Values and limitations of statistical models. Research in Social Stratification and Mobility 2011, 29, 343-349, 10.1016/j.rssm.2011.04.001.
    62. A.K. Jain; Jianchang Mao; K.M. Mohiuddin; Artificial neural networks: a tutorial. Computer 1996, 29, 31-44, 10.1109/2.485891.
    63. A. El-Shafie; A. Noureldin; M. R. Taha; A. Hussain; Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia. Hydrology and Earth System Sciences Discussions 2011, 8, 6489-6532, 10.5194/hessd-8-6489-2011.
    64. Cyril Voyant; Marc Muselli; Christophe Paoli; Marie-Laure Nivet; Numerical weather prediction (NWP) and hybrid ARMA/ANN model to predict global radiation. Energy 2012, 39, 341-355, 10.1016/j.energy.2012.01.006.
    65. Grosan, C.; Abraham, A. Intelligent Systems A Modern Approach; Springer-Verlag: Berlin/Heidelberg, Germany, 2011; Vol. 17.
    66. Seyedeh Narjes Fallah; Ravinesh C. Deo; Mohammad Shojafar; Mauro Conti; Shahaboddin Shamshirband; Computational Intelligence Approaches for Energy Load Forecasting in Smart Energy Management Grids: State of the Art, Future Challenges, and Research Directions. Energies 2018, 11, 596, 10.3390/en11030596.
    67. Benya Wang; Carolyn E. Oldham; Matthew R. Hispey; Comparison of Machine Learning Techniques and Variables for Groundwater Dissolved Organic Nitrogen Prediction in an Urban Area. Procedia Engineering 2016, 154, 1176-1184, 10.1016/j.proeng.2016.07.527.
    68. Zhang, L.; Zhang, G.X.; Li, R.R; Water Quality Analysis and Prediction Using Hybrid Time Series and Neural Network Models. J. Agr. Sci. Tech. 2016, 18, 975-983, .
    69. Momcilo Markus; Mohamad I. Hejazi; Peter Bajcsy; Orazio Giustolisi; Dragan Savic; Prediction of weekly nitrate-N fluctuations in a small agricultural watershed in Illinois. Journal of Hydroinformatics 2010, 12, 251-261, 10.2166/hydro.2010.064.
    70. Amiri, B.J.; Nakane, K; Comparative prediction of stream water total nitrogen from land cover using artificial neural network and multiple linear regression approaches. Polish Journal of Environmental Studies 2009, 18, 151-160, .
    71. M. Zeleňáková; M. Čarnogurská; M. Šlezingr; D. Słyś; Model based on dimensional analysis for prediction of nitrogen and phosphorus concentration in the River Laborec. Hydrology and Earth System Sciences Discussions 2012, 9, 5611-5634, 10.5194/hessd-9-5611-2012.
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