WANN Model Research Articles

Modeling the daily suspended sediment concentration in a hyperconcentrated river on the Loess Plateau, China, using the Wavelet–ANN approach

The complexity of the relationship between suspended sediment concentration (SSC) and river discharge (Q) remains a challenge for SSC prediction in hyperconcentrated rivers. In this study, the wavelet-artificial neural network model (WANN) was built to predict SSC in the Kuye River, a representative hyperconcentrated river in the middle Yellow River catchments of China. In the WANN model, the observed daily time series for Q and SSC of 2193days (from 1967 to 1972) were decomposed into subseries at different scales using discrete wavelet analysis. Then, the effective subseries were selected to construct Q/SSC inputs to the feed-forward back-propagation artificial neural network (BP ANN) to predict SSC 1day in advance (the time resolution of the observed data). The coefficient of determination (R2) and root-mean square error (RMSE) were adopted to evaluate the model's performance. The WANN model showed higher prediction accuracy (R2=0.846 and RMSE=29.82) than the sediment rating curve (SRC) model (R2=0.537 and RMSE=55.40) or the ANN model (R2=0.664 and RMSE=43.13). The WANN model exhibited more robust performance than the SRC and ANN models, indicated by the appropriate values of error autocorrelation and input-error correlation. Negative values of predicted SSC occurred in ANN and in WANN models. By adjusting the negative values to zero, the WANN R2 was improved by 4.3% from 0.846 to 0.882. In general, the results illustrate that the WANN model better predicts SSC in a hyperconcentrated river setting, with highly nonlinear and nonstationary time series.

Does gaze influence steering around a bend?

M. F. Land and D. N. Lee (1994) suggested that steering around a bend is controlled through the estimation of curvature using the visual direction of a single road feature: the tangent point. The aim of this study was to evaluate, using a simulated environment, whether the high levels of tangent point fixation reported by some researchers are indeed related to steering control. In the first experiment, gaze patterns were examined when steering along roadways of varying widths and curvatures. Experiment 2 investigated the effects of enforced fixation on steering, when gaze was directed to the road ahead at a range of lateral eccentricities, including the tangent point. All participants completed both experiments. Overall, there was no evidence for extensive tangent point fixation in the free-gaze experiment and enforced tangent point fixation did not result in more accurate steering. The present results seem to suggest that participants tend to steer in the direction of their gaze; hence, looking at the tangent point causes the driver to steer toward it. These results provide some support for the R. M. Wilkie and J. P. Wann (2002) model of steering, which proposes that drivers will direct their gaze toward points they wish to pass through.