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


BENZER S.

FRESENIUS ENVIRONMENTAL BULLETIN, vol.29, no.2, pp.1145-1152, 2020 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 29 Issue: 2
  • Publication Date: 2020
  • Title of Journal : FRESENIUS ENVIRONMENTAL BULLETIN
  • Page Numbers: pp.1145-1152

Abstract

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.