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将水质指数模型与基于机器学习的技术相结合,用于实时评估水生生态系统

Hyung Il Kim¹, Dongkyun Kim², Mehran Mahdian³

¹DL E&C, Civil Business Division, Donuimun, D Tower, 134 Tongil-ro, Jongno-gu, Seoul, South Korea; Department of Civil and Environmental Engineering, Hongik University, Mapo-gu, Seoul, South Korea.

Environmental pollution (Barking, Essex : 1987)

|May 29, 2024

概括

机器学习模型准确地估计了芬兰Paijänne湖的不列颠哥伦比亚省水质指数 (BCWQI),即使没有化学氧气需求 (COD) 和总 (TP) 等难以测量的参数. 长期短期记忆模型在实时评估水质方面表现出卓越的性能.

科学领域:

环境科学环境科学
水资源管理水资源管理
机器学习应用机器学习应用

背景情况:

水质指数 (WQI) 对于评估内陆水质至关重要.
实时WQI评估受到化学氧气需求 (COD) 和总 (TP) 等参数的现场测量挑战的阻碍.
精确测量COD和TP需要时间,实验室设备,并承担劳动力成本,以及采样困难.

研究的目的:

使用没有COD和TP的机器学习模型估计不列颠哥伦比亚省WQI (BCWQI).
评估不同机器学习模型在用有限参数估计BCWQI时的性能.
提出一种成本效益高,节省时间的替代传统线下水质评估方法.

主要方法:

利用了芬兰帕伊扬尼湖从1980年到2023年的综合水质数据集.
采用了四种机器学习模型:长短期记忆 (LSTM),支持向量回归 (SVR),随机森林 (RF) 和神经网络 (NN).
在估计BCWQI时评估模型性能,包括COD和TP参数和没有COD和TP参数.

主要成果:

机器学习模型在排除COD和TP的情况下也成功估计了Päijänne湖的BCWQI.
长期短期记忆 (LSTM) 模型对排除COD和TP的敏感性最小.
LSTM实现了0.91的确定系数 (R2) 和0.11的根-平均平方误差 (RMSE),在实时BCWQI估计中表现优于其他模型.

结论:

机器学习,特别是LSTM,提供了一个可行的解决方案,以有限的数据实时BCWQI估计.
这种方法提高了内陆水质的评估经济和高效.
拟议的模型可以帮助决策者了解管理实践对湖水质量动态的影响.

关键词:

英国哥伦比亚水质指数 (BCWQI)帕伊扬纳湖 (Päijänne湖) 是一个湖泊.基于机器学习的模型实时评估实时评估

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