Alternative growth models in fisheries: Artificial Neural Networks
JOURNAL OF FISHERIES, vol.7, no.3, pp.719-725, 2019 (ESCI)
- Publication Type: Article / Article
- Volume: 7 Issue: 3
- Publication Date: 2019
- Journal Name: JOURNAL OF FISHERIES
- Journal Indexes: Emerging Sources Citation Index (ESCI)
- Page Numbers: pp.719-725
- Gazi University Affiliated: Yes
Abstract
In this study growth of Atherina boyeri, collected from Sureyyabey Dam Lake, was determination by Artificial Neural Networks (ANNs) along with study of length weight relationships (LWRs). A total of 394 individuals including 32.5% female and 67.5% male specimens were studied collected during the fishing season between May 2015 and May 2016 from the local fisherman. The total length and weight of the specimens were 32-90 mm and 0.225-4.062 g respectively. The relationships were W = 0.01285708 L-2.67 (R-2 = 0.983) for females, W = 0.00678019 L-2.95 (R-2 = 0.969) for males and W = 0.00641527 L-2.87 (R-2 = 0.970) for pooled individuals. Mean Absolute Percentage Error (MAPE) of ANNs (0.182) for all specimens was lower than MAPE value of LWR (1.763). The results of study show that ANNs are superior tool to LWRs for fishes of Sureyyabey Dam Lake.