Forecasting of jack mackerel landings (Trachurus murphyi) in central‐southern Chile through neural networks

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.