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深度优化水质指数和积极矩阵因子化模型,用于水质评估和污染源分配,使用随机森林模型

Han Zhang¹, Xingnian Ren¹, Sikai Chen¹

¹School of Environmental Science and Engineering, Southwest Jiaotong University, Chengdu, 610031, China.

Environmental pollution (Barking, Essex : 1987)

|March 17, 2024

概括

这项研究将机器学习与水质指数和PMF模型相结合,以确定污染源. 农业活动和家用污水被确定为河流污染的主要原因.

科学领域:

环境科学环境科学
水资源管理水资源管理
数据科学数据科学数据科学

背景情况:

有效的水质评估和污染源分配对于可持续的水资源管理至关重要.
传统的水质指数 (WQI) 和正矩阵分解 (PMF) 模型提供了宝贵的见解,但可以通过先进的技术来增强.
在当前的技术环境中,集成数据驱动方法对于完善水质评估和污染源分析至关重要.

研究的目的:

通过机器学习增强WQI和PMF模型的水污染分析能力.
确定影响明江河水质的关键水质指标.
为了更准确地分配污染源,了解水质的变化.

主要方法:

一种机器学习技术,即随机森林模型,与WQI和PMF模型相结合.
利用了来自明江河沿线6个地点 (2015-2020) 的12个水质指标的监测数据.
来自随机森林模型的特征重要性被用来改进WQI计算并调整PMF模型输出.

主要成果:

随机森林模型确定了总 (TP),总 (TN),化学氧气需求 (COD_Cr),溶解氧气 (DO) 和生物化学氧气需求 (BOD₅) 作为最重要的水质指标.
一个改进的WQI模型实现了高精度的水质预测 (R² = 0.9696).
污染源的分配显示农业活动 (30.26%),家庭污水 (29.07%) 和工业废水 (26.25%) 是主要的贡献者.

结论:

随机森林与WQI和PMF模型的整合为水质评估和污染源分配提供了一个强大的方法.
主要污染物如TP,TN,COD_Cr,DO和BOD₅显著影响河流水质.
这些发现为制定有针对性的改善水质战略提供了有价值的参考资料,重点强调农业和国内水源.

关键词:

机器学习机器学习积极矩阵因数分解模型的模型.河流污染物的污染物来源分配的分配方式评价水质水质的评估.

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