基于具有并行注意力机制的变压器和自适应光谱增强的关键水质的长期预测模型
Lu Liu1,2, Xiaodong Ji1,2, Bentao Duan3
1School of Computer Science and Technology, Shandong Technology and Business University, Yantai, 264005, Shandong, China.
Environmental geochemistry and health
|July 20, 2025
概括
一个新的基于变压器的模型准确地预测了长期的海洋水质,这对生态系统健康和水产养殖至关重要. 这种先进的方法提高了预测准确度,有助于环境管理.
科学领域:
- 海洋生物学 海洋生物学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 准确的水质预测对于海洋生态系统健康和可持续水产养殖至关重要.
- 全球气候变化和人类活动增加了数据的复杂性,挑战了长期预测.
- 现有的方法与高维,非静止的海洋数据集作斗争.
研究的目的:
- 开发一种先进的模型,用于对海水质量参数的长期多变量时间序列预测.
- 解决海洋环境数据中复杂的相互作用和时间依赖性所带来的挑战.
主要方法:
- 提出了一种基于变压器的模型,包含适应光谱增强模块 (ASEM) 来降低噪音.
- 引入了多源表示和托克内并行注意 (MSR-TPA) 来捕捉变量间的相关性.
- 实现多尺度网关和互通并行注意 (MSG-TPA) 以用于多尺度特征提取和远程依赖模型.
主要成果:
- 该模型在8个真实海洋养殖数据集上显示了显著的改进.
- 在山东柳冈岛数据集中,平均绝对误差减少了25.93%,平方根平均误差减少了40.78%.
- 展示了卓越的准确性和概括能力,优于现有方法.
结论:
- 拟议的基于变压器的模型为长期海水质量预测提供了可靠的解决方案.
- 为海洋生态保护和海洋养殖系统的水质管理提供关键的技术支持.
- 突出了先进的深度学习技术在应对复杂的环境挑战方面的潜力.
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