Application of Artificial Neural Networks ANN and Adaptive Neuro Fuzzy Inference System ANFIS Models in Water Quality Simulation of Tigris River at Baghdad City


  • Waleed Mohammed Sheet Alabdraba Tikrit University - Iraq
  • Chelang A .Arslan Assist. Prof. Civil Engineering Department Kirkuk University-Iraq
  • Zainab B. Mohammed Constructions and Building Department, Technology University – Iraq



Water quality, ANN, LMNN, SCGNN, ANFIS.


In this paper two different types of artificial neural networks LMNN, SCGNN applied to simulate the total dissolved solids at of Tigris River at El-Wihda station using different water quality parameters data (pH, Temp., Hardness, Turbidity, EC, SO4, CL) at different stations upstream El-Wihda station. Different architecture and different input combinations with trying different numbers of neurons at the hidden layer. In addition, another application, which is an adaptive neuro fuzzy logic inference system ANFIS applied for the same purpose, the results shows that Even though the available data size is relatively small, reasonably a very good results found and a high performance obtained for the water quality prediction. Both ANN and ANFIS models show   a very good performance in simulation of the TDS at the required station, and for the two types of ANNs, It can see that LMNN is better than SCGNN.

Author Biography

Waleed Mohammed Sheet Alabdraba, Tikrit University - Iraq

Environmental Engineering Department

Assistant Proffesor


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How to Cite

Alabdraba, W. M. S., A .Arslan, C., & Mohammed, Z. B. (2017). Application of Artificial Neural Networks ANN and Adaptive Neuro Fuzzy Inference System ANFIS Models in Water Quality Simulation of Tigris River at Baghdad City. Transactions on Machine Learning and Artificial Intelligence, 5(5), 47.