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基于优化的机器学习方法,通过土壤蒸汽提取预测有机污染物的去除效率

Shuai Zhang¹, Jiating Zhao¹, Lizhong Zhu¹

¹College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China; Zhejiang Provincial Key Laboratory of Organic Pollution Process and Control, Hangzhou, Zhejiang 310058, China.

The Science of the total environment

|April 13, 2024

概括

机器学习准确地预测了土壤蒸汽提取 (SVE) 效率,以去除有机污染物. 一个优化的XGBoost模型将时间,污染物类型和温度确定为有效的土壤修复的关键因素.

科学领域:

环境工程环境工程
地质技术工程地质技术工程
计算科学计算科学

背景情况:

土壤蒸汽提取 (SVE) 是一种具有成本效益的对挥发性和半挥发性有机污染物的修复技术.
修复效率受到SVE参数,土壤特性和污染物特性的影响,往往导致特定地点的最佳条件.
需要一个通用的预测模型来评估各种受污染地点的SVE性能.

研究的目的:

开发一种机器学习模型,用于预测有机污染土壤中SVE修复效率.
通过使用SVE来确定影响有机污染物去除效率的关键因素.
通过优化,提高预测模型的准确性和普遍性.

主要方法:

利用文献数据来训练和测试XGBoost (XGB) 机器学习模型.
在训练和测试数据集上使用R平方值评估模型性能.
实施了优化方案,以提高模型的预测准确性和适用性.

主要成果:

一个初始的XGBoost模型实现了高的确定系数 (R2 = 0.9728).
时间,污染物类型和温度被确定为影响SVE效率的最重要因素.
优化的模型显示了更高的准确性 (R2 = 0.9799) 和更广泛的适用性.

结论:

开发的XGB模型有效地预测了SVE的有机污染物去除效率,考虑了多种影响因素.
该研究阐明了影响整治的因素之间的相互作用机制.
这种预测工具有助于评估SVE潜力并优化污染土壤中最大限度去除污染物的条件.

关键词:

机器学习模型的机器学习模型有机污染物有机污染物土壤整治土壤整治土壤整治土壤蒸气提取土壤蒸气提取

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