Sentence examples for turbidity modeling from inspiring English sources

Nevertheless, few studies have explored turbidity modeling.

Using the aforementioned nonlinear system, a predictive turbidity model was built, calibrated, and verified.

The two models are coupled by the bottom boundary conditions of the turbidity current model that depends on the instantaneous velocity of the debris flow model.

The 2006 turbidity prediction model was selected as the best predictive model because of its ability to predict turbidity within the acceptable error range and required confidence interval.

Test results demonstrate that the 2006 turbidity prediction model was selected as the best predictive model (RMSE = 5.787 and CC = 0.975) because of its ability to predict turbidity within the acceptable error range and 90%% required confidence interval (50NTU).

The main processes of low-density turbulent turbidity flow are modeled: downslope sediment-laden flow, entrainment of ambient water, erosion and deposition of several distinct lithologies.

The test results indicate that this turbidity prediction model is feasible and reliable for turbidity forecasting.

The GMDH turbidity forecast model is developed according to the procedure described below.

The ever growing importance of sustainable management of reservoir sedimentation has promoted the development and applications of turbidity current models.

Combining then the 90° Mie scattering theory with the principle of SPDT, a turbidity measurement model has been proposed to explain the experimental results.

By applying the group method of data handling algorithm to self-organization networks, we design a turbidity prediction model based on simple input/output observations of daily hydrological data (rainfall, discharge, and turbidity).