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使用Apriori和LSTM模型数据驱动的污染源识别和水质预测:在汉江河流域的案例研究

Mingyang Liu¹, Jiake Li¹, Yafang Li¹

¹State Key Laboratory of Eco-hydraulics in Northwest Arid Region of China, Xi'an University of Technology, Xi'an 710048, China.

Journal of contaminant hydrology

|April 15, 2025

概括

这项研究使用阿普里奥里算法识别了主要的工业污染物,并通过长短期记忆 (LSTM) 网络预测了水质变化,帮助可持续的水资源管理.

科学领域:

环境科学环境科学
数据科学数据科学数据科学
水资源管理水资源管理

背景情况:

城市化和工业化推动了来自点源的地表水污染.
有效地识别污染源和预测水质对于环境健康和可持续发展至关重要.

研究的目的:

将Apriori算法和LSTM网络结合起来,用于识别主要污染源和预测水质动态.
为可持续的流域管理提供科学基础,并为环境政策制定提供信息.

主要方法:

阿普里奥里算法用于识别污染物协会和初级工业来源 (金,电,化学).
关联规则分析以确定COD,Cu,SS,Pb,Cd和CN等污染物的同时存在.
长短期记忆 (LSTM) 网络用于预测水质参数 (例如,COD) 在水文站.

主要成果:

阿普里奥里将化学氧气需求 (COD),铜 (Cu),悬浮固体 (SS) 和 (Zn) 确定为高度支持的污染物 (支持=0.87).
在COD与 (Cd),, (Pb),SS和 (CN) 的同时发生方面确立了高置信度 (0.9-1.0).
在四个水文站中,LSTM模型在COD预测中取得了高准确性 (RMSE 0.20760.3366,R2 > 0.9).

结论:

金属,电和化学工业被认为是造成大量水污染的主要来源.
关键污染物之间存在强烈的相关性,这有助于来源归因和风险评估.
综合的Apriori-LSTM方法为水质监测,预测和可持续管理提供了一个强大的框架.

关键词:

一个先验的算法.相关性分析是一项相关性分析.长期短期记忆神经网络的神经网络点源污染的污染是指点源污染的污染.水环境水环境水环境

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