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机器学习算法的比较,以预测城市流中的溶氧

Madeleine M Bolick¹, Christopher J Post², Mohannad-Zeyad Naser³

¹Department of Forestry and Environmental Conservation, Clemson University, Clemson, SC, 29634, USA. madelei@clemson.edu.

Environmental science and pollution research international

|June 2, 2023

概括

机器学习模型有效地使用低成本传感器预测城市流中的溶氧 (DO). 随机森林模型表现出色,突出了土地覆盖面和水质之间的联系,以改善流域管理.

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:

城市化显著影响城市流域的水质.
有效的监测系统对于识别和减轻这些影响至关重要.
低成本的传感器为广泛收集水质数据提供了可行的解决方案.

研究的目的:

使用来自低成本传感器的数据识别溶解氧 (DO) 的预测机器学习模型.
建立一个监测系统的城市流网络在汉尼卡特溪,克莱姆森,SC.
用各种机器学习算法比较多重线性回归的性能.

主要方法:

评估k-最近邻居,决策树,随机森林和梯度增强算法对多重线性回归.
使用的水温,导电性,度和水位变化作为输入参数.
使用纳什-萨克利夫模型效率系数 (NSE) 评估模型性能.

主要成果:

随机森林算法在所有四个研究地点都在预测DO方面表现最高.
三个地点的NSE得分超过0.9,第四个地点的NSE得分超过0.598,这表明预测准确度很强.
可解释的AI (XAI) 揭示了温度是DO的关键预测因素,根据特定地点的土地覆盖面和水质相互作用而产生不同的影响.

结论:

机器学习,特别是随机森林模型,为预测城市流DO提供了强大的工具.
整合土地覆盖数据可以更好地理解城市化和水质之间的复杂关系.
这种方法支持有效的城市流域管理的知情决策.

关键词:

人工智能的人工智能是人工智能.机器学习是机器学习.回归是一种回归.水的质量水的质量水资源水资源是水资源.流域的流域是一个流域.

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