Evaluation of Neural Networks for Modeling Nitrate Concentrations in Rivers

Suen, Jian Ping; Eheart, J. Wayland

doi:10.1061/(asce)0733-9496(2003)129:6(505)

Cited by 92 publications

(37 citation statements)

References 9 publications

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“…Most of these models are complex and need several different input data which are not easily accessible, making it a very expensive and time-consuming process (Suen et al 2003). Artificial neural networks (ANNs) are flexible modeling tools with the capability of learning the mathematical mapping between input and output variables of nonlinear systems and generalizing the processes of control, classification, and prediction.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China

Wen

Fang

Diao

et al. 2012

Environ Monit Assess

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Identification and quantification of dissolved oxygen (DO) profiles of river is one of the primary concerns for water resources managers. In this research, an artificial neural network (ANN) was developed to simulate the DO concentrations in the Heihe River, Northwestern China. A three-layer back-propagation ANN was used with the Bayesian regularization training algorithm. The input variables of the neural network were pH, electrical conductivity, chloride (Cl . Cl − was found to be least effective variables on the proposed model. The identified ANN model can be used to simulate the water quality parameters.

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Section: Introductionmentioning

confidence: 99%

Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China

Wen

Fang

Diao

et al. 2012

Environ Monit Assess

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“…Since the "universal approximator demonstration" at the end of the 1980's (Cybenko, 1989;Hornik et al, 1989;Irie and Miyake, 1988), artificial neural networks (ANN) have probably become the most typical machine learning algorithm and have been used in many different fields like physics, chemistry, medicine, ecology and hydrology (Cote et al, 1995;Faraggi and Simon, 1995;Kralisch et al, 2003;Lek et al, 1999;Lischeid, 2001;Smits et al, 1992;Suen and Eheart, 2003;Telszewski et al, 2009). Artificial Neural Networks have been widely used to model complex nonlinear relationships, particularly when the functional form of the relations between the variables involved is unknown.…”

Section: F Oehler Et Al: Generalized Simplified Denitrification Modelmentioning

confidence: 99%

The use of machine learning algorithms to design a generalized simplified denitrification model

2010

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Abstract. We propose to use machine learning (ML) algorithms to design a simplified denitrification model. Boosted regression trees (BRT) and artificial neural networks (ANN) were used to analyse the relationships and the relative influences of different input variables towards total denitrification, and an ANN was designed as a simplified model to simulate total nitrogen emissions from the denitrification process. To calibrate the BRT and ANN models and test this method, we used a database obtained collating datasets from the literature. We used bootstrapping to compute confidence intervals for the calibration and validation process. Both ML algorithms clearly outperformed a commonly used simplified model of nitrogen emissions, NEMIS, which is based on denitrification potential, temperature, soil water content and nitrate concentration. The ML models used soil organic matter % in place of a denitrification potential and pH as a fifth input variable. The BRT analysis reaffirms the importance of temperature, soil water content and nitrate concentration. Generalization, although limited to the data space of the database used to build the ML models, could be improved if pH is used to differentiate between soil types. Further improvements in model performance and generalization could be achieved by adding more data.

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“…For instance, in the same Xuxi River, Amos T. Kabo-Bah developed a mathematical expressions to estimate the fate of ammonia nitrogen (NH 3 -N) 5 . Unfortunately, most of these models require large amounts of data that are not easily accessible, making it a very expensive and time consuming process 11 .…”

Section: Introductionmentioning

confidence: 99%

Estimation of Ammonia-Nitrogen (Nh3-N) Using an Artificial Neural Networks Under Bacterial Technology

Ruben¹,

Xie²,

Samuel³

2016

Orient. J. Chem

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An Artificial Neural Network (ANN) was developed to estimate the NH 3 -N under the bacterial technology in Xuxi River, China. Eight water quality variables such as Dissolved Oxygen (DO), Chemical Oxygen Demand (COD), Total Nitrogen (TN), Total Phosphorus (TP), Suspended Sediment (SS), Temperature, Transparency, and Ammonia Nitrogen (NH 3 -N) were used as inputs for the network. The observed and the predicted NH 3 -N of the trained networks showed a good fit after the training with a coefficient of correlation (r) and a root mean square error (RMSE) of 0.91 and 2.61 respectively. Sensitivity analysis was used to determine the influence of input variables on the dependent variable; TN, Transparency, DO, and TP have proven to be the most effective inputs. Their training's results showed a coefficient of correlation (r = 0.9295) and a (RMSE = 1.2081) which is more accurate than the prediction with eight inputs variables.

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Evaluation of Neural Networks for Modeling Nitrate Concentrations in Rivers

Cited by 92 publications

References 9 publications

Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China

Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China

The use of machine learning algorithms to design a generalized simplified denitrification model

Estimation of Ammonia-Nitrogen (Nh3-N) Using an Artificial Neural Networks Under Bacterial Technology

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