# Analysis of Variables Influencing Scour on Large Sand-Bed Rivers Conducted Using Field Data
## 5 Conclusions  
Most of the existing scour prediction equations available in the literature are derived from laboratory data since flume experiments provide controlled hydraulic conditions and straightforward data collection. On the other hand, the limited range of flow conditions in the flume limits the wider application of derived scour equations, i.e., their application to the prototype conditions. The USGS dataset PSDB-2014 provides a wide range of on-site measured flow, sediment, and geometry variables related to scour; thus, this dataset was the most representative source of scour data for large sand-bed rivers. The measured variables from the dataset were expanded to include eight additional calculated variables commonly used in scour prediction equations and consequently filtered to retain measurements taken only for large sand-bed rivers. After filtering the original dataset, 98% of the remaining measurements were within the clear-water scour conditions, even though the clear-water criterion was not applied in the filter, which validates the reliability of the applied data filter. Several influential variables were removed by using the PCA, as well as two commonly used variables, \(\theta\) and \(d_{50}\), as they showed low impact on the scour depth. Since the measurement of \(\theta\) is arbitrary and changes with flow severity, it should be used only when it can be reliably estimated, which is not often the case. Since the pier length was also eliminated by the PCA and taking into consideration the difficult approximation of complex pier geometry with a single variable, this study selected \(b_{\sigma}\) as the only pier geometry variable, combining the information of pier geometry and alignment with the flow, if available. PCA has eliminated the characteristic sediment size as well, which can be explained by focusing on sand-bed rivers where sand is uniform and therefore has a significantly smaller range than other variables. To take into account riverbed composition, compounded hydraulic-sediment variables (\(v_{c}\), \(Fr_{c}\), and \(Fr_{d}\)) were retained, and consequently, 10 influential variables were classified into four categories: pier geometry (\(b_{\sigma}\)), hydraulic (\(y\), \(v\), \(\tau\), and \(Fr\)), sediment (\(\tau_{c}\), and \(Re_{\rho}\)), hydraulic-sediment variables (\(v_{c}\), \(Fr_{c}\), and \(Fr_{d}\)), and compounded. Afterward, the variables from these categories were used to determine the scour model variables. The proposed dimensional MNLR model designed for scour estimation on the selected data subset has a firm similarity with the two other models (AL and RM) developed using the same PSDB-2014 dataset but with different filters or methods. The comparison performed in this work indicates that the filtering method has a greater influence on the model's performance than the model type (MNLR or GEP). However, the MNLR model developed in this study, Equation (17), yielded a low R\({}^{2}\) value of 0.5 due to the scatter of the dataset, which could be a consequence of excluding multiple measurements for the same bridge locations. Since, at the time of the field measurement, it is not known whether the equilibrium or maximum state has been reached, dispersion in the results is expected. Furthermore, the selection of influential variables that can be reliably measured or estimated is crucial when creating a database for scour estimation. Based on the Pearson correlation coefficient, bef proved to be the most influential variable for estimating ds in this study, and the second most influential variable is \(y\), followed closely by \(v_{c}\), \(v\), \(Fr_{d}\), and \(Fr_{c}\). The remaining four variables (\(\tau\), \(\tau_{c}\), \(Fr\), and \(Re_{\rho}\)) exhibit a very weak correlation with scour depth, probably resulting from errors in the measurement of variables used for their calculation.