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Updated: Jul 10, 2025

A Fish-feeding Laboratory Bioassay to Assess the Antipredatory Activity of Secondary Metabolites from the Tissues of Marine Organisms

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Published on: January 11, 2015

使用机器学习方法进行数据驱动的建模,以评估海洋生态系统中使用机器学习方法的食物状况.

Md Galal Uddin¹, Stephen Nash², Azizur Rahman³

¹School of Engineering, University of Galway, Ireland; Ryan Institute, University of Galway, Ireland; MaREI Research Centre, University of Galway, Ireland; Eco-HydroInformatics Research Group (EHIRG), Civil Engineering, University of Galway, Ireland.

Environmental research

|November 26, 2023

概括

一个新的评估热带状态指数 (ATSI) 模型使用机器学习来改进沿海水域的优化评估. 这种人工智能驱动的工具为海洋生态系统管理提供了更准确的热带状态评估.

关键词:

在ATSI模型中,ATSI模型是:沿海和过渡水域的沿海和过渡水科克港 (Cork Harbour) 是一个港口.ML和AI的方法是ML和AI.热带状况评估热带状况评估

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相关实验视频

Last Updated: Jul 10, 2025

A Fish-feeding Laboratory Bioassay to Assess the Antipredatory Activity of Secondary Metabolites from the Tissues of Marine Organisms

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Published on: January 11, 2015

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

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Published on: January 13, 2023

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

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Published on: November 20, 2017

科学领域:

海洋生态海洋生态学
环境科学环境科学
环境监测中的人工智能

背景情况:

现有的沿海和过渡水域的热带状态指数 (TSI) 模型存在多线性,数据冗余和复杂的分类问题.
精确评估缩对于管理沿海和过渡性水质至关重要.

研究的目的:

开发一种新的数据驱动工具,即评估热带状况指数 (ATSI) 模型,用于改进过渡和沿海 (TrC) 水域的热带状况评估.
通过整合机器学习 (ML) 和人工智能 (AI) 来解决现有 TSI 模型的局限性.

主要方法:

采用ML技术,包括深度学习,以优化输入数据并最大限度地减少ATSI模型的冗余.
使用十个算法开发了一个CHL预测模型,XGBoost显示出卓越的性能 (RMSE=0.0,MSE=0.0,MAE=0.01).
使用新型线性调整尺度的插值函数计算ATSI分数,并使用R2,NSE和MEF指标评估模型效率.

主要成果:

XGBoost模型在CHL预测方面表现出色,显示出高精度.
在不同的应用领域中,ATSI模型表现出更高的灵敏度和效率.
在评估四个爱尔兰水体时,ATSI模型和ATSEBI系统之间观察到显著的差异,突出了ATSI的独特结果.

结论:

通过利用ML/AI和新的分类方案,ATSI模型显著提高了海洋生态系统中热带状态评估的准确性.
ATSI模型为评估和监测TrC水域和其他水体的热带条件提供了一个有希望的方法.
这项研究通过改进的水质评估工具,为海洋生态系统管理和保护做出了重大贡献.