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通过沿海城市的机器学习模型评估和预测含有关键水参数的水质指数,中国

Jing Xu¹, Yuming Mo², Senlin Zhu¹

¹College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou, China.

|July 24, 2024

概括

本研究确定了关键的水质指标和机器学习模型,以有效评估河流. 随机森林和XGBoost模型使用总,氨和溶解氧气准确预测水质指数.

科学领域:

环境科学环境科学
评估水质水质的评估.
机器学习应用机器学习应用

背景情况:

水质指数 (WQI) 对于河流健康评估至关重要,但需要广泛的数据收集和分析.
目前用于WQI计算的方法由于许多水质参数,耗时且昂贵.
需要有效的方法来利用最小的关键指标预测WQI.

研究的目的:

确定必要的水质参数,以准确预测WQI.
评估各种机器学习模型在预测WQI方面的性能.
开发一种成本效益和时间效率高的河流水质管理方法.

主要方法:

收集了2020-2023年中国延和南通17条河流的水质数据 (九个生物物理和化学指标).
采用线性回归和七个机器学习模型 (ANN,SOM,KNN,SVM,RF,XGB,SGB) 进行WQI预测.
利用相关性分析来确定模型训练的最佳输入变量组.

主要成果:

从2020年到2022年,水质总体上有所改善,在2023年有所下降;内陆站的质量比沿海站更好.
南通的水环境比延更好. 大多数记录 (80%) 被归类为"好"或"中等"WQI.
随机森林 (RF) 和极度梯度增强 (XGB) 模型使用关键参数如总 (TP),氨 (AN) 和溶解氧 (DO) 证明了高精度 (R2 > 0.99).

结论:

使用TP,AN和DO的RF和XGB模型可靠地预测WQI值.
使用TP和AN的射频模型实现了超过85%的WQI等级预测准确度.
通过确定关键参数和有效的预测模型,这些发现支持有效的水质评估和管理.

关键词:

沿海城市沿海城市关键的水参数机器学习模型的机器学习模型水的质量水的质量水质指数 (WQI) 是指一个水质指数.

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