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


Başakın E. E., Ekmekcioğlu Ö., Mohammadi B.

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, cilt.27, sa.17, ss.22131-22134, 2020 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Kısa Makale
  • Cilt numarası: 27 Sayı: 17
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1007/s11356-020-08666-8
  • Dergi Adı: ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, IBZ Online, ABI/INFORM, Aerospace Database, Agricultural & Environmental Science Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, EMBASE, Environment Index, Geobase, MEDLINE, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.22131-22134
  • Anahtar Kelimeler: Artificial intelligence, Chlorophyll-a, Natural lakes, Man-made lakes, MLPNN, ANFIS, ALGORITHM
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

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.