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Fishing Trip Cost Modeling Using Generalized Linear Model and Machine Learning Methods

September 07, 2021

Despite fishing trip cost being an important element in evaluating performance of fisheries, low observe coverage often results in limited trip-level data. This article introduces a model to estimate and predict the fishing trip costs of unsampled trips.

Fishing trip cost is an important element in evaluating economic performance of fisheries, assessing economic effects from fisheries management alternatives, and serving as input for ecosystem and bioeconomic modeling. However, many fisheries have limited trip-level data due to low observer coverage. This article introduces a generalized linear model (GLM) utilizing machine learning (ML) techniques to develop a modeling approach to estimate the functional forms and predict the fishing trip costs of unsampled trips. GLM with Lasso regularization and ML cross-validation of model are done simultaneously for predictor selection and evaluation of the predictive power of a model. This modeling approach is applied to estimate the trip-level fishing costs using the empirical sampled trip costs and the associated trip-level fishing operational data and vessel characteristics in the Hawaii and American Samoa longline fisheries. Using this approach to build models is particularly important when there is no strong theoretical guideline on predictor selection. Also, the modeling approach addresses the issue of skewed trip cost data and provides predictive power measurement, compared with the previous modeling efforts in trip cost estimation for the Hawaii longline fishery. As a result, fishing trip costs for all trips in the fishery can be estimated. Lastly, this study applies the estimated trip cost model to conduct an empirical analysis to evaluate the impacts on trip costs due to spatial regulations in the Hawaii longline fishery. The results show that closing the Western and Central Pacific Ocean (WCPO) could induce an average 14% increase in fishing trip costs, while the trip cost impacts of the Eastern Pacific Ocean (EPO) closures could be lower.


Chan HL, Pan M. 2021. Fishing trip cost modeling using generalized linear model and machine learning methods - A case study with longline fisheries in the Pacific and an application in Regulatory Impact Analysis. PLoS ONE. 16(9):e0257027.  https://doi.org/10.1371/journal.pone.0257027.

Last updated by Pacific Islands Fisheries Science Center on 11/23/2021

Longline