ARTIFICIAL NEURAL NETWORKS APPROACH TO GROWTH PROPERTIES ATHERINA BOYERI RISSO, 1810 IN YAMULA DAM LAKE, TURKEY

Benzer S.

FRESENIUS ENVIRONMENTAL BULLETIN, cilt.29, sa.2, ss.1145-1152, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 29 Sayı: 2
  • Basım Tarihi: 2020
  • Dergi Adı: FRESENIUS ENVIRONMENTAL BULLETIN
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Agricultural & Environmental Science Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Chemical Abstracts Core, Communication Abstracts, Environment Index, Geobase, Greenfile, Metadex, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.1145-1152
  • Gazi Üniversitesi Adresli: Evet

Özet

It is aimed to predict of length-weight relationship (LWR) parameters by using Artificial Neural Networks (ANNs). The present study investigates the properties of the Big-scale Sand Smelt, Atherina boyeri Risso, 1810 in Yamula Dam Lake (Kayseri, Turkey). Minimum and maximum fork length size and weight were found 3.5 and 8.3 cm; 0.38 and 4.82 g for all individuals. The weight-length relationships were W = 0.01285708 L-2.8167 (R-2 = 0.934) for females, W = 0.00972323 L-2.8690 (R-2 = 0.950) for males and W=0.1014131 L-2.8476 (R-2 = 0.940) for all individuals. The condition factor was calculated as 0.812, 0.797 and 0.804 for females, males and all individuals respectively. The results obtained by ANNs and LWR equation are compared to those obtained by the growth rate. LWR and ANNs models was found for females, males and all individuals.