Prediction of River Water Quality by Adaptive Neuro Fuzzy Inference System (ANFIS)



Limitations on freshwater resources have caused water resources managers to focus an increasing attention over the past few decades on water quality protection. Surface water quality management in such resources as rivers, seas, lakes, and estuaries is of a greater importance than other water resources and a greater number of studies have been conducted on them as they are more accessible and, therefore, more directly exposed to a variety of contaminants and pollutants. Application of appropriate and efficient mathematical models for river water quality simulation is essential for the formulation of comprehensive guidelines used in evaluating measures that are employed for river pollution control and management. The non-linear equations dominating pollutant transfer phenomena in rivers, the complexity of their simultaneous solution, and the multiplicity of kinetic constants and coefficients have made it difficult, or at times impossible, to use physically-based models and methods for this purpose. Therefore, most of these models can only be applied to simplified cases or to situations where the models are strictly calibrated and validated, with no adequate accuracy when applied to unrestricted conditions. The uncertainties in water quality problems have made fuzzy inference systems, especially as combined with adaptive neural networks, to be used as a novel approach. The main objective of the present study is to exploit the capabilities of the adaptive neuro-fuzzy inference system (ANFIS) for river quality predictions with emphasis on DO and BOD. In the case study carried out on the Zayandehroud River, BOD predictions were obtained by the proposed system with a correlation coefficient of 0.953 in the calibration stage and 0.931 in the validation stage and DO predictions were obtained with a correlation coefficient of 0.921 in the calibration stage and 0.904 in the validation stage. Comparison of the results reveals the high accuracy level of the proposed model.