Determination of water quality parameters and nutrient level with an Adaptive Neuro- Fuzzy Inference System

In this research, the physico-chemical water quality parameters and the effect of climate changes onwater quality is evaluated. During the observation period (5 months) physico-chemical parameterssuch as water temperature, turbidity, saturated oxygen, dissolved oxygen, pH, chlorophyll-a, salinity,conductivity, and concentration of total nitrogen (nutrient level) as main pollutant factor have beenmeasured in Iran from September to February 2013 in the Amirkabir dam area. Moreover, anadaptive neuro fuzzy inference mechanism (ANFIS) is designed for the sake of modeling andprediction. In order to learn the proposed ANFIS mechanism a Quantum behave particle swarmoptimization (QPSO) is employed. The proposed ANFIS architecture has nine-input and one outputin which the physico -chemical parameters of water and total nitrogen have been considered as inputand output of the proposed ANFIS, respectively. In this paper to reduce the noise and measurementerrors a wavelet transform strategy is utilized.