Machine learning of factors for improving oyster hatchery production
Srishti Vishwakarma, Matthew W. Gray, Greg M. Silsbe, Vyacheslav Lyubchich
TL;DR
This study uses machine learning to identify factors affecting oyster hatchery production, helping operators make better decisions to improve yields and sustainability.
Contribution
The study introduces a data-driven forecasting tool using machine learning to optimize oyster hatchery production.
Findings
Week number, salinity, and fecundity are key predictors of oyster yield variability.
Salinity-related variables are especially important in low-yield scenarios.
The model provides an early warning system for hatchery operators to adjust conditions and improve outcomes.
Abstract
Oyster aquaculture and restoration in the Chesapeake Bay are vital, yet hatcheries frequently struggle with inconsistent larval growth and sudden mass mortality events. Unpredictable disruptions in larval production cause large economic losses, represent a perceived risk to growers, and impede industry expansion. To better understand associations between production yield and its potential predictors, we applied machine learning (random forest, and neural network) and statistical (generalized additive model) models to a comprehensive dataset of environmental, water quality, and operational parameters from a Maryland oyster hatchery, aiming to identify key yield predictors and develop a robust forecasting tool. We used recursive Boruta algorithm for variable selection, pinpointing critical predictors, and employed cross-validation to fine-tune model settings. Shapley value analysis…
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Taxonomy
TopicsMarine Bivalve and Aquaculture Studies · Hydrological Forecasting Using AI · Marine and fisheries research