Dissolved organic carbon (DOC) is a master variable in aquatic systems. Resolving DOC dynamics requires high‐temporal resolution data. However, DOC concentration cannot be directly measured in situ, and discrete sample collection and analysis becomes expensive as temporal resolution increases. To surmount this problem, an option is to predict site‐specific DOC concentration with linear modeling and optical data predictors collected from high‐cost, high‐maintenance in situ spectrophotometers. This study sought to improve upon the accuracy and field costs of linear predictive DOC methods by using machine learning modeling coupled to low‐to‐zero cost predictors. To do this, we collected 16 months of in situ data (e.g., spectrophotometer attenuation, salinity, temperature), assembled freely available predictors (e.g., point in year, rainfall), and collected samples for DOC analysis, all in a salt marsh creek. At seasonal timescales, machine learning (coefficient of determination [R²] = 0.90) modestly improved upon the accuracy of linear methods (R² = 0.80) but offered substantial instrumentation cost reductions (~ 90%) by requiring only cost‐free predictors (online data) or cost‐free predictors paired with low‐cost in situ predictors (temperature, salinity, depth). At intertidal timescales, linear methods proved ill‐equipped to predict DOC concentration compared to machine learning, and again, machine learning offered a substantial instrumentation cost reduction (~ 90%). Although our models were developed for and applicable to a single site, the use of machine learning with low‐to‐zero cost predictors provides a blueprint for others trying to model DOC dynamics and other analytes in any complex aquatic system.

中文翻译：

---

通过机器学习预测动态盐沼溪流中的溶解有机碳浓度

溶解有机碳（DOC）是水生系统中的主要变量。解决DOC动态需要高温分辨率数据。但是，DOC浓度无法直接就地测量，并且随着时间分辨率的提高，离散样品的收集和分析变得昂贵。为了解决这个问题，一种选择是使用线性建模和从高成本，高维护性原位分光光度计收集的光学数据预测器来预测特定地点的DOC浓度。这项研究试图通过将机器学习建模与低成本到零成本预测器结合使用，以提高线性预测DOC方法的准确性和现场成本。为此，我们收集了16个月的原位数据（例如，分光光度计的衰减，盐度，温度），可自由组合的预测变量（例如，年份，降雨量），并在盐沼小溪中收集样品进行DOC分析。在季节性时标上，机器学习（确定系数[R ² ] = 0.90）相对于线性方法的精度（R ² = 0.80）有所改善，但是通过仅要求免费的预测变量（在线数据）或低成本的预测变量与较低的变量配对，可以显着降低仪器成本（〜90％）。成本原位预测因子（温度，盐度，深度）。在潮间时间尺度上，与机器学习相比，线性方法被证明不足以预测DOC的浓度，并且机器学习同样可以显着降低仪器成本（约90％）。尽管我们的模型是为单个站点开发的，并且适用于单个站点，但是将机器学习与低成本到零成本的预测器结合使用，可以为尝试在任何复杂水生系统中模拟DOC动态和其他分析物的其他人提供蓝图。