Predicting Dissolved Organic Carbon Concentration In A Dynamic Salt Marsh Creek Via Machine Learning

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 [R-2] = 0.90) modestly improved upon the accuracy of linear methods (R-2 = 0.80) but offered substantial instrumentation cost reductions (similar to 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 (similar to 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.