开发高度准确的机器学习模型,以优化鱼水产养殖中的水质管理决策
Ashwaq M Alnemari1, Wael M Elmessery2, Amjad S Qazaq3
1Biology Department, College of Science and Humanities, Prince Sattam bin Abdulaziz University, P.O. Box: 83, 11940, Al- Kharj, Saudi Arabia.
Scientific reports
|October 13, 2025
概括
这项研究开发了机器学习模型,用于自动化鱼水产养殖的水质管理决策. 几种模型实现了高精度,证明了可持续水产养殖实践的有希望的基础.
科学领域:
- 水产养殖是水产养殖的一种方式.
- 环境科学 环境科学
- 计算机科学 计算机科学
背景情况:
- 优化水质对于可持续的鱼水产养殖至关重要.
- 目前的管理依赖于手动监控,这可能是低效的.
- 自动化决策需要先进的预测建模.
研究的目的:
- 开发和比较机器学习模型,以预测鱼水产养殖中的最佳水质管理行动.
- 创建一个决策支持系统,自动化管理策略.
- 在模拟水产养殖环境中评估各种算法的性能.
主要方法:
- 创建了一个20个关键水质情景的合成数据集.
- 使用SMOTETomek进行类平衡和特征缩放的预处理数据.
- 训练并评估了随机森林,梯度提升,XGBoost,SVM,后勤回归,神经网络和投票分类器组合.
主要成果:
- 多种模型,包括投票分类器,随机森林,梯度提升,XGBoost和神经网络,在测试组中实现了完美的准确性.
- 交叉验证证实了高性能,神经网络显示了最高的平均精度 (98.99% ± 1.64%).
- 模型选择取决于具体的部署需求和运营优先事项.
结论:
- 机器学习为优化鱼养殖水产品水质管理提供了一个强大的工具.
- 开发的模型为数据驱动系统提供了基础,以提高效率和可持续性.
- 通过人工智能自动化管理决策可以显著改善水产养殖业务.
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