The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Default user image.

Babak Mohammadi

Doctoral student

Default user image.

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

Author

  • Eyyup Ensar Başakın
  • Ömer Ekmekcioğlu
  • Babak Mohammadi

Summary, in English

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.

Publishing year

2020-06

Language

English

Pages

22131-22134

Publication/Series

Environmental Science and Pollution Research

Volume

27

Issue

17

Document type

Journal article (letter)

Publisher

Springer

Keywords

  • Artificial Intelligence
  • Chlorophyll/analysis
  • Chlorophyll A
  • Environmental Monitoring
  • Lakes

Status

Published

ISBN/ISSN/Other

  • ISSN: 1614-7499