Neural network modeling of dissolved oxygen in the Gruža reservoir, Serbia

By Vesna Ranković, J. Radulović, I. Radojević, A. Ostojić, L. Čomić. 2010

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A Review of the Artificial Neural Network Models for Water Quality Prediction (2020, methodology): “Categories Authors(Year) Locations Water Quality Variables Meteorological Factors Other Factors Output Strategy Dataset Time Step Data Dividing Methods Prediction Lengths Feedforward [133] Lakes (USA) TN, TP; pH,EC, Tur NA NA Category 2 1217 samples No details Train:55%, validate: 22%, test: 23% ANN”
Efficient Water Quality Prediction Using Supervised Machine Learning (2019, methodology): “[12] used 10 input parameters, which is unsuitable for inexpensive real time systems.”
Application of adaptive neuro-fuzzy inference system (ANFIS) to estimate the biochemical oxygen demand (BOD) of Surma River (2015, methodology): “Correlation coefficient is defined as the degree of correlation between the experimental and modeled values (Rankovic et al., 2012)”