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Research Article

Enhancing Marine Comprehensive Carrying Capacity and Energy Assessment and Prediction Using an Improved Ant Colony Algorithm and System Dynamics Model

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  • @ARTICLE{10.4108/ew.6099,
        author={Hao Luo and Demin Zhang and Liping Jiao},
        title={Enhancing Marine Comprehensive Carrying Capacity and Energy Assessment and Prediction Using an Improved Ant Colony Algorithm and System Dynamics Model},
        journal={EAI Endorsed Transactions on Energy Web},
        volume={11},
        number={1},
        publisher={EAI},
        journal_a={EW},
        year={2024},
        month={5},
        keywords={Marine Comprehensive Carrying Capacity, Ant Colony Algorithm, System Dynamics, Environmental Management, Sustainable Marine Development},
        doi={10.4108/ew.6099}
    }
    
  • Hao Luo
    Demin Zhang
    Liping Jiao
    Year: 2024
    Enhancing Marine Comprehensive Carrying Capacity and Energy Assessment and Prediction Using an Improved Ant Colony Algorithm and System Dynamics Model
    EW
    EAI
    DOI: 10.4108/ew.6099
Hao Luo1, Demin Zhang1,*, Liping Jiao2
  • 1: Ningbo University, Ningbo, 315211, China
  • 2: Xiamen Environmental Monitoring Station, Xiamen 360200, China
*Contact email: Zzhangdm@yeah.net

Abstract

The primary aim of this paper is to introduce a novel approach to simulating and predicting Marine Comprehensive Carrying Capacity (MCCC), which seeks to enhance the efficacy and accuracy of MCCC assessment and prediction. MCCC is crucial for effective marine resource management and sustainable energy exploitation, as it determines the maximum activities that the marine environment can support without significant degradation. Given the considerable complexity associated with the marine environment and the need for more reliable predictive technologies, this paper proposes an integrated model that combines the capabilities of the proven optimization algorithm, Enhanced Ant Colony, and System Dynamics Modelling. This approach allows for detailed simulation of the variables associated with MCCC, improving prediction precision.The study details the methodology for developing an adapted Ant Colony algorithm and the foundation of a system dynamics model. These models are interconnected within a single framework, tested across multiple scenarios to validate their robustness and sustainability. The results demonstrate the superiority of the proposed approach over conventional models in terms of prediction accuracy and precision, confirmed through both in-sample and out-of-sample validation procedures.This paper is a significant contribution to the fields of sustainability and energy management within marine environments. It provides a new tool for policymakers and environmental managers to enhance their decision-making processes with a greater depth of knowledge, ensuring the sustainable utilization of marine resources and energy potential.

Keywords
Marine Comprehensive Carrying Capacity, Ant Colony Algorithm, System Dynamics, Environmental Management, Sustainable Marine Development
Received
2024-02-19
Accepted
2024-05-21
Published
2024-05-30
Publisher
EAI
http://dx.doi.org/10.4108/ew.6099

Copyright © 2024 Luo et al., licensed to EAI. This is an open access article distributed under the terms of the CC BYNC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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