Šiljić Tomić Aleksandra N, Antanasijević Davor Z, Ristić Mirjana Đ, Perić-Grujić Aleksandra A, Pocajt Viktor V
Faculty of Technology and Metallurgy, University of Belgrade, Karnegijeva 4, Belgrade, 11120, Serbia.
Innovation Center of the Faculty of Technology and Metallurgy, Karnegijeva 4, Belgrade, 11120, Serbia.
Environ Monit Assess. 2016 May;188(5):300. doi: 10.1007/s10661-016-5308-1. Epub 2016 Apr 19.
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.
本文描述了人工神经网络模型在预测多瑙河生物需氧量（BOD）水平方面的应用。以流经塞尔维亚的河段上17个站点定期监测的18个水质参数作为输入变量。模型优化分三个连续步骤进行：首先，考察监测站的空间影响；其次，确定达到满意性能所需的监测期；最后，应用相关分析评估水质参数之间的关系。在前两步中使用均方根误差（RMSE）评估模型性能，而在最后一步中，使用多个性能统计指标。结果，开发了两个优化模型，一个是涵盖从多瑙河入流到诺维萨德市监测站的广义回归神经网络模型（标记为GRNN - 1），另一个是涵盖从诺维萨德市到与罗马尼亚边境监测站的GRNN模型（标记为GRNN - 2）。两个模型预测的BOD值与实际观测值之间均显示出良好的一致性。
