Forecasting of dissolved oxygen in the river danube using neural networks
Anita Csábrági, Sándor Molnár, Péter Tanos, József Kovács · Hungarian Agricultural Engineering · 2015
The Danube is the second-largest river in Europe and the conservation of its water quality is very important because it influences the lives of millions people. The aim of this research is to predict one of the most important water quality parameters, dissolved oxygen, with the help of water pH, runoff, water temperature and electrical conductivity data. Multivariate Linear Regression (MLR), Back-propagation Neural Networks (BPNN) and General Regression Neural Networks (GRNN) were applied and their performances compared in this study. The most accurate prediction proved to be GRNN. This paper describes the influence of single input parameters on the prediction.