Turbidity Prediction in a Large, Shallow Lake Using Machine Learning

Large, shallow lakes lacking rooted aquatic vegetation are susceptible to wind-induced wave action that results in increased shear stress on the lake bottom, sediment resuspension and poor water clarity. The relationship between meteorological, hydrographical and sediment characteristics, and sediment dynamics has implications for internal phosphorus cycling and bioavailability, the frequency and duration of harmful cyanobacterial blooms, lake level management and restoration potential. In this study, a multi-parameter water quality sonde was deployed at various sites at the bottom of Utah Lake to measure water quality variables. Sediment cores were collected at each of the deployment sites and analyzed for common physical and chemical properties. Several machine learning regression techniques, including polynomial, decision tree, artificial neural network, and support vector machine, were applied to predict turbidity, a measure of water clarity and surrogate for sediment dynamics, using the observed explanatory variables wind speed and direction, fetch, water depth, sediment properties, algae, and cyanobacteria. The decision tree estimators, random forest and histogram-based gradient boosting had the best model performance, explaining 86–89% of the variability in turbidity when including all the explanatory variables. The artificial neural network estimator multi-layer perceptron and the polynomial regression models also performed well (81%), whereas the support vector machine estimator exhibited poor performance. Chlorophyll and phycocyanin, components of turbidity, were amongst the most important variables to the decision tree and artificial neural network models. Wind speed and water depth were also of high importance, which conforms with mechanistic explanations of sediment mobility caused by wave action and shear stress. Carbonate content was consistently a good predictor due to the calcareous nature of Utah Lake, whereas the importance of the other sediment properties was dependent on the machine learning technique applied. This case study demonstrated the potential for machine learning models to predict water clarity and has promise for more general applications to other shallow lakes and serves as a useful tool for lake management and restoration.