论文标题
使用机器学习方法从水质替代物中估算高频营养浓度
Estimation of high frequency nutrient concentrations from water quality surrogates using machine learning methods
论文作者
论文摘要
连续的高频水质监测已成为支持水管理的关键任务。尽管传感器技术取得了进步，但某些变量不能容易和/或经济地进行现场和实时监控。在这些情况下，替代措施可用于通过数据驱动的模型进行估计。在这项工作中，通常测量原位的变量被用作替代物，以估计农村流域和城市中的养分浓度，使用机器学习模型，特别是随机森林。将结果与使用相同数量的替代物的线性建模的结果进行了比较，从而使均方根误差（RMSE）的减少降低了60.1％。计算了最多包括七个代孕传感器的利润，得出的结论是，在每个集水区中分别添加4和5个传感器，在错误改善方面不值得。
Continuous high frequency water quality monitoring is becoming a critical task to support water management. Despite the advancements in sensor technologies, certain variables cannot be easily and/or economically monitored in-situ and in real time. In these cases, surrogate measures can be used to make estimations by means of data-driven models. In this work, variables that are commonly measured in-situ are used as surrogates to estimate the concentrations of nutrients in a rural catchment and in an urban one, making use of machine learning models, specifically Random Forests. The results are compared with those of linear modelling using the same number of surrogates, obtaining a reduction in the Root Mean Squared Error (RMSE) of up to 60.1%. The profit from including up to seven surrogate sensors was computed, concluding that adding more than 4 and 5 sensors in each of the catchments respectively was not worthy in terms of error improvement.
