模拟藻类毒素动态和湖泊综合网络框架

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Özlem Baydaroğlu^1,2, Serhan Yeşilköy^3,4,5, Anchit Dave⁶

¹NOAA, Global Systems Laboratory, Boulder, CO 80305, USA.

|July 25, 2025

概括

使用SINDy技术,有害藻类繁殖 (HABs) 的建模现在更加准确. 一个新的网络工具有助于教育公众,并为HAB活动找到解决方案.

科学领域:

环境科学环境科学
数据科学数据科学数据科学
生态生态学生态生态学

背景情况:

有害藻类繁殖 (HABs) 对公共健康,生态系统和经济构成重大风险.
准确的HAB建模对于有关水质的可持续发展目标至关重要.
在HAB建模中的挑战包括数据稀缺性和藻类生长的非线性动态.

研究的目的:

使用SINDy技术,开发一种强大的微囊素模型,这是一种关键的藻类毒素.
利用溶解氧和蒸发数据来改善HAB预测.
创建一个互动工具,用于环境教育和公众对HABs的认识.

主要方法:

采用了稀疏识别非线性动力学 (SINDy) 方法,一种机器学习方法.
综合溶解氧 (水质) 和蒸发 (气象) 作为模型参数.
开发了一个基于Web的交互平台,用于场景分析和HAB跟踪.

主要成果:

在四个研究的湖泊中,三个湖泊的平均绝对百分比误差 (MAPE) 约为2%,实现了高模型准确性.
第四个湖的模型显示了11%的MAPE,表明需要进一步改进的区域.
交互式工具有效地可视化HAB动态和参数影响.

结论:

辛迪技术为准确的HAB毒素建模提供了一个有希望的方法.
开发的网络工具提高了公众的理解,并促进了主动的HAB管理策略.
这种综合方法支持知情决策,以减轻HAB的影响.

关键词:

环境健康环境健康有害的藻类藻类.微囊是一种微囊 (microcystin).公共卫生公共卫生.非线性动态的稀疏识别 (SINDy)网络框架网络框架

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