Multiphysical similarity of behavioral characteristics of hydrobiont in the World Ocean

Abstract

This article discusses a modern approach to studying aquatic organism be-havior, combining hydroacoustic methods, machine vision, and physical modeling. Currently, echo recordings are being decoded using AI to estimate biomass and allowable catch. The widespread use of hydroacoustic methods (HAM) for quantitative population census is discussed; however, these methods require high equipment costs. The authors note that direct observations are rare, requiring automation: machine vision, augmented reality, and mathematical modeling to collect statistics and understand growth dynamics. The article identifies five groups of studies: registration methods, physiology, social behavior, chemical factors, and engineering solutions. The importance of multiphysics modeling and numerical experiments is emphasized, which allow for the prediction of optimal trawl performance and resource manage-ment without expensive field observations. A review of the physical similarity criterion based on dimensional theory and the need to maintain the scale of geometry, mechanics, hydrodynamics, and light properties is provided. The authors propose a transition from integrated statistics to a full distribution analysis, which will lead to increased forecast accuracy and provide a more complete picture of population structure. Overall, the article emphasizes the integration of modern technologies and scientific approaches for sustainable fish farming management.