Sediment resuspension is critical to the internal nutrient loading in aquatic systems. Turbidity is commonly used as an indicator for sediment resuspension and is proved to be highly correlated to wind speed in large shallow lakes. A field observation of wind speed and turbidity was conducted using a portable weather station and a YSI 6600V2-2, and an observation lasting for 39 days was evaluated in this study (the data points with wind speed > 4 m/s account for 75%). The daily average values (DA dataset) as well as daily maximum (MX dataset) and minimum values (MI dataset) were calculated from the instantaneous observations (IN dataset). Correlations in IN dataset were deduced based on machine learning methods and were compared to those obtained from DA, MI, and MX datasets. Furthermore, the correlation in IN dataset was analyzed by using two statistical methods, and from the view of statistical the turbidity is regarded as a variable. Results indicate that the correlations in IN datasets follow the exponential function or power function pattern with a critical wind speed of 6 m/s, Regression on IN dataset revealed that linear regression model had the best performance on predicting the turbidity in test dataset and no significant differences are observed between exponential function and power function pattern. Correlations in DA and MX datasets exhibit higher maximal information coefficient (MIC) than IN dataset and error of turbidity prediction introduced by using these correlations in IN dataset is within the tolerance level. Statistical analysis on the IN dataset shows that a strong relationship exists among the wind speed and expectation of turbidity with a MIC over 0.99, and follows the exponential function or the power function as well with a different critical wind speed of 4 m/s. Over 95% data points fall in the predicted intervals of turbidity for both methods, suggesting a high predicting accuracy.
Keywords: Lake Taihu; Machine learning; Maximal information coefficient; Turbidity; Wind disturbance.