NDLI: Modeling the BOD of Danube River in Serbia using spatial, temporal, and input variables optimized artificial neural network models

Content Provider	Springer Nature Link
Author	Šiljić Tomić, Aleksandra N. Antanasijević, Davor Z. Ristić, Mirjana Đ. Perić Grujić, Aleksandra A. Pocajt, Viktor V.
Copyright 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.
Starting Page	1
Ending Page	12
Page Count	12
File Format	PDF
ISSN	01676369
Journal	Environmental Monitoring and Assessment
Volume Number	188
Issue Number	5
e-ISSN	15732959
Language	English
Publisher	Springer International Publishing
Publisher Date	2016-04-19
Publisher Place	Cham
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	BOD ANN optimization GRNN Danube River Environmental Monitoring/Analysis Environmental Management Ecotoxicology Atmospheric Protection/Air Quality Control/Air Pollution Ecology
Content Type	Text
Resource Type	Article
Subject	Pollution Medicine Management, Monitoring, Policy and Law

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