Model based on dimensional analysis for prediction of nitrogen and phosphorus concentration in the River Laborec

Zeleňáková, Martina; Čarnogurská, Mária; Šlezingr, Miloslav; Słyś, D.

doi:10.5194/hessd-9-5611-2012

Cited by 2 publications

(3 citation statements)

References 36 publications

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“…Some of those minor time gaps were covered with interpolated values, thus introducing some amount of uncertainty in model inputs. Average uncertainty in the prediction can be calculated using the following equation [ 56 ]: where: σ = average uncertainty percentage, n = number of data points, x = observed data points, and y = predicted data points…”

Section: Discussionmentioning

confidence: 99%

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Optimised neural network model for river-nitrogen prediction utilizing a new training approach

et al. 2020

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In the past few decades, there has been a rapid growth in the concentration of nitrogenous compounds such as nitrate-nitrogen and ammonia-nitrogen in rivers, primarily due to increasing agricultural and industrial activities. These nitrogenous compounds are mainly responsible for eutrophication when present in river water, and for 'blue baby syndrome' when present in drinking water. High concentrations of these compounds in rivers may eventually lead to the closure of treatment plants. This study presents a training and a selection approach to develop an optimum artificial neural network model for predicting monthly average nitrate-N and monthly average ammonia-N. Several studies have predicted these compounds, but most of the proposed procedures do not involve testing various model architectures in order to achieve the optimum predicting model. Additionally, none of the models have been trained for hydrological conditions such as the case of Malaysia. This study presents models trained on the hydrological data from 1981 to 2017 for the Langat River in Selangor, Malaysia. The model architectures used for training are General Regression Neural Network (GRNN), Multilayer Neural Network and Radial Basis Function Neural Network (RBFNN). These models were trained for various combinations of internal parameters, input variables and model architectures. Post-training, the optimum performing model was selected based on the regression and error values and plot of predicted versus observed values. Optimum models provide promising results with a minimum overall regression value of 0.92.

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Section: Discussionmentioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Optimised neural network model for river-nitrogen prediction utilizing a new training approach

et al. 2020

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“…The goodness of fit of the models was evaluated by regression of observed versus predicted and scatter plot. Comparison of the results for both the models showed that the backpropagation model (R 2 = 0.94) predicted the results more precisely than the multiple regression model (R 2 = 0.85) Zele ňáková, Čarnogurská, Šlezingr and Słyś [90] predicted nitrogen and phosphorus concentrations in river Laborec in Slovakia, employing dimensional analysis method. They used Buckingham theorem to develop a prediction model utilizing important variables such as stream discharge, area of catchment, stream velocity, temperatures of air and water, and pollutant concentration.…”

Section: Nitrogen and Phosphorus Concentrationmentioning

confidence: 95%

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

et al. 2020

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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.

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Model based on dimensional analysis for prediction of nitrogen and phosphorus concentration in the River Laborec

Cited by 2 publications

References 36 publications

Optimised neural network model for river-nitrogen prediction utilizing a new training approach

Optimised neural network model for river-nitrogen prediction utilizing a new training approach

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

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