Letter to the editor "comparing artificial intelligence techniques for chlorophyll-a prediction in US lakes".

The discussers wish to thank the authors of the original paper for investigating the comparing accuracy of artificial intelligence techniques trained to predict chlorophyll-a in US lakes. In the original paper (Luo et al., Environ Sci Pollut Res 26: 30524-30532, 2019), four data-driven models were established to estimate the chlorophyll-a (CHLA) values in natural and man-made lakes. Three of these models are adaptive neuro-fuzzy inference system (ANFIS)-based, while one is (artificial neural network) ANN-based. The authors used total phosphorus (TP), total nitrogen (TN), turbidity (TB), and the Secchi depth (SD) as independent variables in order to predict CHLA. They stated that ANFIS with subtractive clustering method (ANFIS_SC) models and multilayer perceptron neural network (MLPNN) models gives higher accuracy in the prediction of CHLA values for natural lakes and man-made lakes, respectively. In this letter, some of the missing points in the original publication, which is important for the estimation and comparison of CHLA values in two different lake sets that differ according to the type of formation, are highlighted. In addition, several points are mentioned in order to make these points more clarified for potential readers.