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.