Prediction of monthly Hilsa (Tenualosa ilisha) catch in the Northern Bay of Bengal using Bayesian structural time series model

Abstract Monthly catch of Hilsa in the Northern Bay of Bengal was modeled and predicted using the Bayesian structural time series (BSTS) with chlorophyll and rainfall as explanatory covariates. Chlorophyll and rainfall were used in the model with a lag of eleven months and one month, respectively. These lag values were determined from the exploratory data analysis. In this study, four separate models were developed to predict the Hilsa catch and compare the results. In the first model, the catch was predicted based on trend and seasonality of the previous years’ catch data. The second and third models predicted monthly catch with chlorophyll (having a lag of 11 months) and rainfall (having a lag of 1 month), respectively, as the only covariate in the model. The fourth model had both chlorophyll and rainfall as covariates with a lag of 11 months and 1 month, respectively. The training mean absolute percentage error (MAPE) of the fourth model (measuring model fit) was observed as 0.2827, while the test MAPE (measuring model accuracy) was 0.2786. The coefficient values for both chlorophyll and rainfall were very close (0.999 and 1.064) which indicated the almost equal effect of both the parameters on the Hilsa catch. This study indicated that more abundance of the food material (phytoplankton) during the post-monsoon season would promote the growth and maturity of more Hilsa that would successively migrate towards river or estuary during the subsequent monsoon season and the catch would likely increase with the intensification of the southwest monsoon.