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基于机器学习的季节性缺氧的预测在使用电容电位测量传感器的安trophic河口
Seongsik Park1, Kyunghoi Kim1, Tadashi Hibino2
1Department of Ocean Engineering, Pukyong National University, Busan, Republic of Korea.
Marine environmental research
|March 15, 2024
概括
现在使用电容潜力 (CP) 和机器学习更准确地预测河口缺氧. 这种方法改善了环境管理和水产养殖的早期预警,减轻了缺氧灾害风险.
科学领域:
- 环境科学 环境科学
- 海洋生物学 海洋生物学
- 数据科学数据科学数据科学
背景情况:
- 欧的河口容易出现缺氧,影响水生生态系统和水产养殖.
- 对低氧的准确预测对于环境管理和灾害减缓至关重要.
研究的目的:
- 使用长期短期记忆 (LSTM) 模型,预测缩河口中缺氧的发生情况.
- 为了评估电容电位 (CP) 作为缺氧预测指标的有效性.
主要方法:
- 使用K-means集群,将年度溶氧 (DO) 趋势分为三个阶段.
- 采用LSTM模型,使用CP,降水量,潮水位,盐度和水温预测DO阶段和缺氧.
- 在各种预测时间步骤 (PTS) 中评估了模型性能.
主要成果:
- 容量潜力 (CP) 是集群DO阶段中最有影响力的变量.
- 在使用CP和其他变量时,LSTM模型在12小时PTS预测缺氧发生时达到92.1%的准确性.
- 在所有PTS中,包括CP始终提高了对所有PTS中缺氧发生和不发生的预测准确性.
结论:
- 整合CP和机器学习 (LSTM) 显著提高了缺氧预测的准确性.
- 这种预测能力允许积极应对低氧相关的环境和水产养殖灾害.
- CP为DO度提供了有价值的定量见解,改善了预测模型.
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