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混合机器学习方法集成GMDH和SVR用于预测尘埃样本中的重金属度

Jamshid Piri¹, Mohammad Reza Rezaei Kahkha², Ozgur Kisi^3,4

¹Department of Water Engineering, Faculty of Soil & Water, University of Zabol, P.O. Box: 98615-538, Zabol, Iran. j.piri@uoz.ac.ir.

Environmental science and pollution research international

|September 10, 2024

概括

机器学习模型准确地预测尘暴中的有毒重金属,保护食品安全. 组合数据处理组方法 (GMDH) 和支持向量回归 (SVR) 与和搜索优化 (H) 的混合模型在评估,和污染方面表现出卓越的性能.

科学领域:

环境科学环境科学
地质化学地质化学
计算毒理学计算毒理学

背景情况:

农业地区的沙尘暴对食品安全和公共卫生构成重大风险,原因是土壤和作物受到重金属污染.
来自尘暴的空气颗粒可以运输有毒金属,如,和,影响农田和人类健康.
伊朗东南部的西斯坦地区特别脆弱,经常经历沙尘暴,从干燥的湖床上调动颗粒物.

研究的目的:

评估机器学习模型在尘埃样本中预测有毒重金属度 (,,) 的潜力.
开发和评估混合机器学习模型,将数据处理的组方法 (GMDH) 和支持向量回归 (SVR) 与和搜索优化 (H) 结合起来.
识别关键预测金属 (,铜,,,,) 用于估计有毒金属度.

主要方法:

在农业西斯坦地区的15个站点收集了2012-2018年的夏季尘埃样本.
在尘埃样本中测量了,铜,,,,,,和的度.
开发并验证了两种混合模型:GMDH+H和SVR+H,以预测使用其他测量金属作为输入的,和.

主要成果:

混合GMDH+H和SVR+H模型与单个模型相比,显著提高了预测准确性.
该GMDH+H模型在领先预测方面表现出色 (d-index=0.98,RR=0.96).
在SVR+H模型中, (d-index=0.96,RR=0.92) 和 (d-index=0.96,RR=0.93) 获得了最高的精度.
不确定性分析表明,南部和西部地区的误差更高,与尘暴影响区域相关.
泰勒图和热图分析证实了混合建模方法的优越性.

结论:

混合机器学习模型,特别是GMDH+H和SVR+H,是预测尘埃中有毒重金属度的有效工具.
这些先进的计算方法为解决沙尘暴带来的复杂环境健康挑战提供了有价值的方法.
这些发现突显了预测建模在脆弱农业地区环境监测和风险评估中的实用性.

关键词:

尘埃尘埃的尘埃,就是我们的尘埃.和的搜索优化和的搜索优化.这是一种重金属,重金属.改进了GMDH的改善改进了 SVR 的功能.预测预测预测

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