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 on
water quality is evaluated. During the observation period (5 months) physico-chemical parameters
such 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 been
measured in Iran from September to February 2013 in the Amirkabir dam area. Moreover, an
adaptive neuro fuzzy inference mechanism (ANFIS) is designed for the sake of modeling and
prediction. In order to learn the proposed ANFIS mechanism a Quantum behave particle swarm
optimization (QPSO) is employed. The proposed ANFIS architecture has nine-input and one output
in which the physico -chemical parameters of water and total nitrogen have been considered as input
and output of the proposed ANFIS, respectively. In this paper to reduce the noise and measurement
errors a wavelet transform strategy is utilized.