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一个集成优化器与一个堆叠集成替代模型用于识别地下水污染源的识别

Liuzhi Zhu¹, Wenxi Lu¹, Chengming Luo¹

¹Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130021, China; Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, Changchun 130021, China; College of New Energy and Environment, Jilin University, Changchun 130021, China.

Journal of contaminant hydrology

|September 28, 2024

概括

本研究引入了用于地下水污染源识别 (GCSI) 的集体学习框架,大大减少了模拟时间并提高了识别准确性. 新方法提高了应对复杂环境挑战的效率和可靠性.

科学领域:

环境科学环境科学
水文地质学水文地质学
计算科学计算科学

背景情况:

地下水污染源识别 (GCSI) 对于环境保护至关重要.
由于问题的复杂性,传统的模拟优化方法在时间成本和准确性方面面临挑战.

研究的目的:

开发一个创新的集体学习框架,以克服传统GCSI方法的局限性.
提高地下水污染源识别的效率和准确性.

主要方法:

开发了一个堆叠组合模型 (SEM),集成极端随机树,自适应提升和双向门式循环单元作为模拟的替代品.
创建了一个整体优化器 (E-GKSEEFO),将成吉思汗鱼优化器和电动鱼食优化器结合起来,以增强搜索策略.
验证了SEM-E-GKSEEFO框架,使用来自真实煤炭的假设场景.

主要成果:

对于高维,非线性GCSI数据,SEM在单个机器学习模型中表现出优异的拟合性能.
与个人优化器相比,E-GKSEEFO显著提高了GCSI结果的准确性.
综合的SEM-E-GKSEEFO框架有效地减少了时间成本,同时保持了高的识别准确性.

结论:

拟议的SEM-E-GKSEEFO整体反转框架对于GCSI来说是有效和优越的.
这种方法为准确有效地识别地下水污染源提供了一个有希望的解决方案.

关键词:

深度学习模型深度学习模型集成优化器集成优化器地下水污染源的识别和识别堆叠组合替代品的替代品群体情报优化器群体情报优化器

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