Artificial Neural Network for Prediction of Total Nitrogen and Phosphorus in US Lakes

AbstractModeling is an important aspect of water quality management because it saves material and labor costs. The nonlinearity of water quality variables due to the complex chemical and physical processes in a body of water makes the modeling process difficult. This study used an artificial neural...

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Bibliographic Details
Published in:Journal of environmental engineering (New York, N.Y.) N.Y.), 2019-06, Vol.145 (6)
Main Authors: Sinshaw, Tadesse A, Surbeck, Cristiane Q, Yasarer, Hakan, Najjar, Yacoub
Format: Article
Language:English
Subjects:
Artificial neural networks
Complex variables
Computer applications
Data processing
Datasets
Labor costs
Lakes
Mathematical models
Model accuracy
Model testing
Neural networks
Nitrogen
Nonlinear systems
Nonlinearity
Nutrient concentrations
Nutrients
Organic chemistry
Parameter sensitivity
pH effects
Phosphorus
Predictions
Quality management
Regression analysis
Regression models
Sensitivity analysis
Software
Statistical analysis
Technical Papers
Turbidity
Water management
Water quality
Water quality management
Water quality measurements
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Summary:AbstractModeling is an important aspect of water quality management because it saves material and labor costs. The nonlinearity of water quality variables due to the complex chemical and physical processes in a body of water makes the modeling process difficult. This study used an artificial neural network (ANN) approach, a powerful computational tool for nonlinear relationships, to develop a model that estimates the summer concentration of total nitrogen (TN) and total phosphorus (TP) in US lakes using interrelated and easily measurable water quality parameters. Two ANN models, using regional and national data sets, and one linear regression model were trained, tested, and validated using three inputs (pH, conductivity, and turbidity) that were statistically correlated with the outputs. The prediction accuracy of the ANN models consistently outperformed the linear regression model. The statistical accuracy of the ANN models for regional data sets was superior to that of the national data set. A sensitivity analysis showed that pH was the most predictive parameter for nutrients. These results indicate that the ANN modeling technique can be a screening tool for an overall estimation of nutrient concentrations in regional lakes.
ISSN:0733-9372
1943-7870
DOI:10.1061/(ASCE)EE.1943-7870.0001528