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<p>This paper presents a modeling framework for simulating capelin spawning migration in the Barents Sea by integrating artificial neural networks (ANNs), a genetic algorithm (GA), an individual-based model (IBM), and environmental variables. ANNs determine movement direction based on inputs like temperature and ocean currents and are trained using a GA whose fitness function dynamically adapts to temperature and proximity to spawning routes. The model successfully reproduces the southeastward migration observed in 2019, aligning with historical spawning sites along northern Norway’s eastern coast. Validation against empirical data confirms its accuracy, and results highlight the importance of the adaptive fitness function in such learning-based models. The proposed model is also compared to three alternatives based on passive swimming, gradient detection, and restricted-area search. It outperforms them by accurately replicating migration patterns, with most simulated fish reaching spawning sites. In contrast, the alternative models often failed due to current-induced drift, particularly in the gradient detection and restricted-area search approaches.</p>

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A machine learning framework for modeling Barents Sea Capelin spawning migration

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月30日

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  • Salah Alrabeei,
  • Talal Rahman,
  • Sam Subbey

摘要

This paper presents a modeling framework for simulating capelin spawning migration in the Barents Sea by integrating artificial neural networks (ANNs), a genetic algorithm (GA), an individual-based model (IBM), and environmental variables. ANNs determine movement direction based on inputs like temperature and ocean currents and are trained using a GA whose fitness function dynamically adapts to temperature and proximity to spawning routes. The model successfully reproduces the southeastward migration observed in 2019, aligning with historical spawning sites along northern Norway’s eastern coast. Validation against empirical data confirms its accuracy, and results highlight the importance of the adaptive fitness function in such learning-based models. The proposed model is also compared to three alternatives based on passive swimming, gradient detection, and restricted-area search. It outperforms them by accurately replicating migration patterns, with most simulated fish reaching spawning sites. In contrast, the alternative models often failed due to current-induced drift, particularly in the gradient detection and restricted-area search approaches.