|
Abstract
|
In the present investigation, the usefulness and capabilities of four artificial intelligence (AI) models, namely feedforward neural networks (FFNNs), gene expression programming (GEP), adaptive neuro-fuzzy inference system with grid partition (ANFIS-GP) and adaptive neuro-fuzzy inference system with subtractive clustering (ANFIS-SC), were investigated in an attempt to evaluate their predictive ability of the phycocyanin pigment concentration (PC) using data from two stations operated by the United States Geological Survey (USGS). Four water quality parameters, namely temperature, pH, specific conductance and dissolved oxygen, were utilized for PC concentration estimation. The four models were evaluated using root mean square errors (RMSEs), mean absolute errors (MAEs) and correlation coefficient (R). The results showed that the ANFIS-SC provided more accurate predictions in comparison with ANFIS-GP, GEP and FFNN for both stations. For USGS 06892350 station, the R, RMSE and MAE values in the test phase for ANFIS-SC were 0.955, 0.205 μg/L and 0.148 μg/L, respectively. Similarly, for USGS 14211720 station, the R, RMSE and MAE values in the test phase for ANFIS-SC, respectively, were 0.950, 0.050 μg/L and 0.031 μg/L. Also, using several combinations of the input variables, the results showed that the ANFIS-SC having only temperature and pH as inputs provided good accuracy, with R, RMSE and MAE values in the test phase, respectively, equal to 0.917, 0.275 μg/L and 0.200 μg/L for USGS 06892350 station. This study proved that artificial intelligence models are good and powerful tools for predicting PC concentration using only water quality variables as predictors.
|