Modeling the BOD of Danube River in Serbia using spatial, temporal, and input variables optimized artificial neural network models
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2016
Authors
Šiljić-Tomić, AleksandraAntanasijević, Davor
Ristić, Mirjana
Perić-Grujić, Aleksandra
Pocajt, Viktor
Article (Published version)
Metadata
Show full item recordAbstract
This paper describes the application of artificial neural network models for the prediction of biological oxygen demand (BOD) levels in the Danube River. Eighteen regularly monitored water quality parameters at 17 stations on the river stretch passing through Serbia were used as input variables. The optimization of the model was performed in three consecutive steps: firstly, the spatial influence of a monitoring station was examined; secondly, the monitoring period necessary to reach satisfactory performance was determined; and lastly, correlation analysis was applied to evaluate the relationship among water quality parameters. Root-mean-square error (RMSE) was used to evaluate model performance in the first two steps, whereas in the last step, multiple statistical indicators of performance were utilized. As a result, two optimized models were developed, a general regression neural network model (labeled GRNN-1) that covers the monitoring stations from the Danube inflow to the city of ...Novi Sad and a GRNN model (labeled GRNN-2) that covers the stations from the city of Novi Sad to the border with Romania. Both models demonstrated good agreement between the predicted and actually observed BOD values.
Keywords:
BOD / ANN optimization / GRNN / Danube RiverSource:
Environmental Monitoring and Assessment, 2016, 188, 5Publisher:
- Springer, Dordrecht
Funding / projects:
DOI: 10.1007/s10661-016-5308-1
ISSN: 0167-6369
WoS: 000376017400041
Scopus: 2-s2.0-84964199553
Institution/Community
Tehnološko-metalurški fakultetTY - JOUR AU - Šiljić-Tomić, Aleksandra AU - Antanasijević, Davor AU - Ristić, Mirjana AU - Perić-Grujić, Aleksandra AU - Pocajt, Viktor PY - 2016 UR - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/3254 AB - This paper describes the application of artificial neural network models for the prediction of biological oxygen demand (BOD) levels in the Danube River. Eighteen regularly monitored water quality parameters at 17 stations on the river stretch passing through Serbia were used as input variables. The optimization of the model was performed in three consecutive steps: firstly, the spatial influence of a monitoring station was examined; secondly, the monitoring period necessary to reach satisfactory performance was determined; and lastly, correlation analysis was applied to evaluate the relationship among water quality parameters. Root-mean-square error (RMSE) was used to evaluate model performance in the first two steps, whereas in the last step, multiple statistical indicators of performance were utilized. As a result, two optimized models were developed, a general regression neural network model (labeled GRNN-1) that covers the monitoring stations from the Danube inflow to the city of Novi Sad and a GRNN model (labeled GRNN-2) that covers the stations from the city of Novi Sad to the border with Romania. Both models demonstrated good agreement between the predicted and actually observed BOD values. PB - Springer, Dordrecht T2 - Environmental Monitoring and Assessment T1 - Modeling the BOD of Danube River in Serbia using spatial, temporal, and input variables optimized artificial neural network models IS - 5 VL - 188 DO - 10.1007/s10661-016-5308-1 ER -
@article{ author = "Šiljić-Tomić, Aleksandra and Antanasijević, Davor and Ristić, Mirjana and Perić-Grujić, Aleksandra and Pocajt, Viktor", year = "2016", abstract = "This paper describes the application of artificial neural network models for the prediction of biological oxygen demand (BOD) levels in the Danube River. Eighteen regularly monitored water quality parameters at 17 stations on the river stretch passing through Serbia were used as input variables. The optimization of the model was performed in three consecutive steps: firstly, the spatial influence of a monitoring station was examined; secondly, the monitoring period necessary to reach satisfactory performance was determined; and lastly, correlation analysis was applied to evaluate the relationship among water quality parameters. Root-mean-square error (RMSE) was used to evaluate model performance in the first two steps, whereas in the last step, multiple statistical indicators of performance were utilized. As a result, two optimized models were developed, a general regression neural network model (labeled GRNN-1) that covers the monitoring stations from the Danube inflow to the city of Novi Sad and a GRNN model (labeled GRNN-2) that covers the stations from the city of Novi Sad to the border with Romania. Both models demonstrated good agreement between the predicted and actually observed BOD values.", publisher = "Springer, Dordrecht", journal = "Environmental Monitoring and Assessment", title = "Modeling the BOD of Danube River in Serbia using spatial, temporal, and input variables optimized artificial neural network models", number = "5", volume = "188", doi = "10.1007/s10661-016-5308-1" }
Šiljić-Tomić, A., Antanasijević, D., Ristić, M., Perić-Grujić, A.,& Pocajt, V.. (2016). Modeling the BOD of Danube River in Serbia using spatial, temporal, and input variables optimized artificial neural network models. in Environmental Monitoring and Assessment Springer, Dordrecht., 188(5). https://doi.org/10.1007/s10661-016-5308-1
Šiljić-Tomić A, Antanasijević D, Ristić M, Perić-Grujić A, Pocajt V. Modeling the BOD of Danube River in Serbia using spatial, temporal, and input variables optimized artificial neural network models. in Environmental Monitoring and Assessment. 2016;188(5). doi:10.1007/s10661-016-5308-1 .
Šiljić-Tomić, Aleksandra, Antanasijević, Davor, Ristić, Mirjana, Perić-Grujić, Aleksandra, Pocajt, Viktor, "Modeling the BOD of Danube River in Serbia using spatial, temporal, and input variables optimized artificial neural network models" in Environmental Monitoring and Assessment, 188, no. 5 (2016), https://doi.org/10.1007/s10661-016-5308-1 . .