Modelling of dissolved oxygen content using artificial neural networks: Danube River, North Serbia, case study
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2013
Authors
Antanasijević, Davor
Pocajt, Viktor

Povrenović, Dragan

Perić-Grujić, Aleksandra

Ristić, Mirjana

Article (Published version)

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The aims of this study are to create an artificial neural network (ANN) model using non-specific water quality parameters and to examine the accuracy of three different ANN architectures: General Regression Neural Network (GRNN), Backpropagation Neural Network (BPNN) and Recurrent Neural Network (RNN), for prediction of dissolved oxygen (DO) concentration in the Danube River. The neural network model has been developed using measured data collected from the Bezdan monitoring station on the Danube River. The input variables used for the ANN model are water flow, temperature, pH and electrical conductivity. The model was trained and validated using available data from 2004 to 2008 and tested using the data from 2009. The order of performance for the created architectures based on their comparison with the test data is RNN gt GRNN gt BPNN. The ANN results are compared with multiple linear regression (MLR) model using multiple statistical indicators. The comparison of the RNN model wit...h the MLR model indicates that the RNN model performs much better, since all predictions of the RNN model for the test data were within the error of less than +/- 10 %. In case of the MLR, only 55 % of predictions were within the error of less than +/- 10 %. The developed RNN model can be used as a tool for the prediction of DO in river waters.
Keywords:
Modelling of dissolved oxygen / Modelling of water quality / Artificial neural network / Multiple linear regressionSource:
Environmental Science and Pollution Research, 2013, 20, 12, 9006-9013Publisher:
- Springer Heidelberg, Heidelberg
Funding / projects:
DOI: 10.1007/s11356-013-1876-6
ISSN: 0944-1344
PubMed: 23764983
WoS: 000327498600068
Scopus: 2-s2.0-84891145294
Institution/Community
Tehnološko-metalurški fakultetTY - JOUR AU - Antanasijević, Davor AU - Pocajt, Viktor AU - Povrenović, Dragan AU - Perić-Grujić, Aleksandra AU - Ristić, Mirjana PY - 2013 UR - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/2434 AB - The aims of this study are to create an artificial neural network (ANN) model using non-specific water quality parameters and to examine the accuracy of three different ANN architectures: General Regression Neural Network (GRNN), Backpropagation Neural Network (BPNN) and Recurrent Neural Network (RNN), for prediction of dissolved oxygen (DO) concentration in the Danube River. The neural network model has been developed using measured data collected from the Bezdan monitoring station on the Danube River. The input variables used for the ANN model are water flow, temperature, pH and electrical conductivity. The model was trained and validated using available data from 2004 to 2008 and tested using the data from 2009. The order of performance for the created architectures based on their comparison with the test data is RNN gt GRNN gt BPNN. The ANN results are compared with multiple linear regression (MLR) model using multiple statistical indicators. The comparison of the RNN model with the MLR model indicates that the RNN model performs much better, since all predictions of the RNN model for the test data were within the error of less than +/- 10 %. In case of the MLR, only 55 % of predictions were within the error of less than +/- 10 %. The developed RNN model can be used as a tool for the prediction of DO in river waters. PB - Springer Heidelberg, Heidelberg T2 - Environmental Science and Pollution Research T1 - Modelling of dissolved oxygen content using artificial neural networks: Danube River, North Serbia, case study EP - 9013 IS - 12 SP - 9006 VL - 20 DO - 10.1007/s11356-013-1876-6 ER -
@article{ author = "Antanasijević, Davor and Pocajt, Viktor and Povrenović, Dragan and Perić-Grujić, Aleksandra and Ristić, Mirjana", year = "2013", abstract = "The aims of this study are to create an artificial neural network (ANN) model using non-specific water quality parameters and to examine the accuracy of three different ANN architectures: General Regression Neural Network (GRNN), Backpropagation Neural Network (BPNN) and Recurrent Neural Network (RNN), for prediction of dissolved oxygen (DO) concentration in the Danube River. The neural network model has been developed using measured data collected from the Bezdan monitoring station on the Danube River. The input variables used for the ANN model are water flow, temperature, pH and electrical conductivity. The model was trained and validated using available data from 2004 to 2008 and tested using the data from 2009. The order of performance for the created architectures based on their comparison with the test data is RNN gt GRNN gt BPNN. The ANN results are compared with multiple linear regression (MLR) model using multiple statistical indicators. The comparison of the RNN model with the MLR model indicates that the RNN model performs much better, since all predictions of the RNN model for the test data were within the error of less than +/- 10 %. In case of the MLR, only 55 % of predictions were within the error of less than +/- 10 %. The developed RNN model can be used as a tool for the prediction of DO in river waters.", publisher = "Springer Heidelberg, Heidelberg", journal = "Environmental Science and Pollution Research", title = "Modelling of dissolved oxygen content using artificial neural networks: Danube River, North Serbia, case study", pages = "9013-9006", number = "12", volume = "20", doi = "10.1007/s11356-013-1876-6" }
Antanasijević, D., Pocajt, V., Povrenović, D., Perić-Grujić, A.,& Ristić, M.. (2013). Modelling of dissolved oxygen content using artificial neural networks: Danube River, North Serbia, case study. in Environmental Science and Pollution Research Springer Heidelberg, Heidelberg., 20(12), 9006-9013. https://doi.org/10.1007/s11356-013-1876-6
Antanasijević D, Pocajt V, Povrenović D, Perić-Grujić A, Ristić M. Modelling of dissolved oxygen content using artificial neural networks: Danube River, North Serbia, case study. in Environmental Science and Pollution Research. 2013;20(12):9006-9013. doi:10.1007/s11356-013-1876-6 .
Antanasijević, Davor, Pocajt, Viktor, Povrenović, Dragan, Perić-Grujić, Aleksandra, Ristić, Mirjana, "Modelling of dissolved oxygen content using artificial neural networks: Danube River, North Serbia, case study" in Environmental Science and Pollution Research, 20, no. 12 (2013):9006-9013, https://doi.org/10.1007/s11356-013-1876-6 . .