Forecasting of jack mackerel landings (Trachurus murphyi) in central‐southern Chile through neural networks. (14th May 2015)

• Record Type: Journal Article
• Authors: Naranjo, Laura; Plaza, Francisco; Yáñez, Eleuterio; Barbieri, María Ángela; Sánchez, Felipe
• Is Part Of: Fisheries oceanography. Volume 24:Number 3(2015:May)
• Page Start: 219
• Page End: 228
• Publication Date: 2015-05-14
• Subjects: environment -- fishing effort -- forecasting -- jack mackerel -- landings -- neural networks; Fishery oceanography -- Periodicals; 639.2
• DOI: 10.1111/fog.12105
• Languages: English
• ISSNs: 1054-6006

Abstract

Abstract: In the present study, the performance of neuronal networks models in monthly landing forecasting of jack mackerel ( Trachurus murphyi ) in central‐southern Chile (32°S–42°S) was assessed. Thus, monthly estimations for 10 environmental variables, fishing effort (fe) and jack mackerel landings for the period 1973–2008 were used. A preliminary analysis was done in order to remove strongly correlated variables. Sea surface temperature (SST) and fe are established as input variables, then, a non‐linear cross correlation analysis was performed to estimate the lag between the input variables and jack mackerel landings. Two models were adjusted: model one includes both training and testing cases randomly selected using all data involved in the analysed period; for model 2, the data is divided into two time series: the first from 1973 to 2002 used for training and the second between 2003 and 2008 used for validation. The external validation process for model 1 showed an explained variance of 92%, with a standard forecasting error of 30%. The explained variance for model 2 was 81%, with a standard forecasting error of 38%. Finally, the sensitivity analysis for both models showed the fe as the most influential variable to jack mackerel landings, which presents functionality depending on anthropogenic effects rather than environmental conditions.