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CGS: Coupled growth and survival model with cohort fairness

August 27, 2023
Fish modeling in complex environments is critical for understanding drivers of population dynamics in aquatic systems. This paper proposes a Bayesian network method for modeling fish survival and growth over multiple connected rivers. Traditional fish survival models capture the effect of multiple environmental drivers (e.g., stream temperature, stream flow) by adding different variables, which increases model complexity and results in very long and impractical run times (i.e., weeks). We propose a coupled survival-growth model that leverages the observations from both sources simultaneously. It also integrates the Bayesian process into the neural network model to efficiently capture complex variable relationships in the system while also conforming to known survival processes used in existing fish models. To further reduce the performance disparity of fish body length across cohorts, we propose two approaches for enforcing fairness by the adjustment of training priorities and data augmentation. The results based on a real-world fish dataset collected in Massachusetts, US demonstrate that the proposed method can greatly improve prediction accuracy in modeling survival and body length compared to independent models on survival and growth, and effectively reduce the performance disparity across cohorts. The fish growth and movement patterns discovered by the proposed model are also consistent with prior studies in the same region, while vastly reducing run times and memory requirements.
Publication Year 2023
Title CGS: Coupled growth and survival model with cohort fairness
DOI 10.24963/ijcai.2023/664
Authors Erhu He, Yue Wan, Benjamin Letcher, Jennifer Burlingame Hoyle Fair, Yiquin Xie, Xiaowei Jia
Publication Type Conference Paper
Publication Subtype Conference Paper
Index ID 70248806
Record Source USGS Publications Warehouse
USGS Organization Leetown Science Center; New England Water Science Center; Eastern Ecological Science Center