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追踪重金属对人类健康和复杂沿海水层生态环境的影响,使用改进的机器学习优化优化

Abdulhayat M Jibrin¹, Sani I Abba², Jamilu Usman³

¹Civil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia.

Environmental science and pollution research international

|August 24, 2024

概括

这项研究引入了先进的机器学习模型,用于预测大马的重金属污染.

科学领域:

环境科学环境科学
水文地质学水文地质学
数据科学数据科学数据科学

背景情况:

沿海水层中重金属 (HM) 污染的增加对公众健康和环境可持续性构成重大风险.
像达马姆这样的地区的城市化加剧了HM污染,挑战了监管合规 (EPA,WHO) 和可持续发展目标 (SDGs).
有效的监测对于管理来自工业,农业和城市活动的HM污染至关重要.

研究的目的:

开发基于预测的重金属 (,,) 度监测系统,用于达马姆沿海含水层中的重金属 (,,).
通过使用动态系统识别和机器学习 (ML) 来提高环境监测的准确性,可靠性和效率.
为了比较各种ML组合技术对HM预测的性能.

主要方法:

采用动态系统识别和ML模型,包括简单平均组合 (SAE),加权平均组合 (WAE) 和神经组合 (N-ESB).
综合汉默斯坦-维纳 (HW) 模型用于HM度预测.
利用广泛的数据收集,模型校准,验证和k-fold交叉验证来进行可靠的性能评估.

主要成果:

神经组合 (N-ESB) 模型在预测 (RMSE = 0.0010 毫克/公斤) 和 (RMSE = 0.0179 毫克/公斤) 方面表现优异.
汉默斯坦-维纳 (HW) 算法被证明是对预测最有效的 (RMSE = 0.0000 mg/kg).
在预测HM度方面,N-ESB和HW模型明显优于其他方法.

结论:

由于其高性能和低错误率,N-ESB模型是实时,精确监测和管理沿海水层中HM污染的最佳选择.
集成先进的预测建模技术对于有效的环境科学,政策制定和生态管理至关重要.
这项研究为应对脆弱的沿海环境中的重金属污染提供了强有力的框架.

关键词:

人工智能的人工智能是人工智能.沿海的水源层是沿海的.污染污染污染污染这是一种重金属,重金属.预测预测预测

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