Search

Forecasting fish landings is a critical element tool for fisheries managers and policymakers to short-term quantitative recommendations for fisheries management. In this study, the forecasting of a quarterly landing of total fish catch and the catch of major pelagic fish species (Indian Mackerel and Bombay duck) was done by nonlinear autoregressive with exogenous inputs (NARX), an artificial neural network model. The quarterly landings data of total fish catch and the catch of major pelagic fish along with quarterly average data on the mean value of environmental variables were used for building the model and forecasting. The developed NARX model was validated with the actual fish catch on holdout data with prediction error 2.45–11.42%. Further, the developed NARX model was used to forecast fish catch for the next 20 quarters (5 years) and was compared and found good agreement with the actual catch reported by Central Marine Fisheries Research Institute, Kochi, annual report(Year- 2014, 2015 and 2016). The developed NARX model in the present case study is of the first time to forecast the fish catch landing using exogenous input in the Maharashtra region.