# Scour Detection with Monitoring Methods and Machine Learning Algorithms--A Critical Review
## 2 Conventional Monitoring-Based and Machine Learning-Based Methods to Identify Scour
### Synthesis of the Results
#### 2.2.2 Cluster 2--Synthesis of Machine Learning-Based Studies  
Machine Learning-based studies reviewed in the scope of this study aimed to find local or total scour around bridge piers or abutments. The models of the studies were trained with laboratory or field data. The newly proposed algorithms include convolutional neural network, multiple linear regression method, K-Star model, reduced error pruning tree base classifier, extreme learning machines, gradient tree boosting, group method of data handling, ERBNN model, support vector regression-based model, non-dominated sortinggenetic algorithm, Gaussian processes regression, relevance vector machines, deep neural network, and LS-support vector machine. The studies either compared their approach with other algorithms or with existing empirical formulas. The most frequently used reference algorithms for comparison with the newly proposed algorithms were artificial neural networks, support vector machine, gradient tree boosting, group method of data handling, and extreme learning machines. Most of the time, the proposed algorithms outperformed these well-known ones, which is the main target of designing new models. The reference empirical formulas were 65-1, 65-2, Melville-Sheppard, MBW, HEC-18, Laursen & Toch [13], and Froehlich [120], Mississippi's method [PERSON] [119], and Ataie-Ashtiani [123]. All the proposed algorithms performed better in predicting scour than the traditional equations mentioned above. The proposed models either improved the empirical formulas' accuracy levels (one study) or developed a model adapting one algorithm (four studies) or hybrid models (nine studies) for a more successful prediction of scour depth around the piers.  
Some of the challenges faced were the need to decrease the duration of computation, picking the field data, and the combination of independent parameters more explicitly. It was usually stated in the discussions that more training data would improve the accuracy levels of the studies in future work. Another work to improve the accuracy level could be the contributions of different algorithms to the hybrid models. Improving the accuracy of the scour height estimations will result in more economical solutions by preventing overdelsigns.  
Conceptualization, S.T., J.T. and E.O.; methodology, S.T.; software, not applied; validation, S.T., J.T., E.O. and J.C.M.; formal analysis, S.T.; investigation, S.T.; resources, S.T.; writing--original draft preparation, S.T.; writing--review and editing, E.O. and J.T.; visualization, S.T., supervision, J.T. and E.O.; project administration, not applied; funding acquisition, J.T., J.C.M. and E.O. All authors have read and agreed to the published version of the manuscript.  
This research was funded by FCT (Portuguese national funding agency for science, research, and technology)/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB/04029/2020 and trough the doctoral Grant 2021.06162.BD. This work has also been partly financed within the European Horizon 2020 Joint Technology Initiative Shift2 Rail through contract no. 101012456 (IN2 TRACK3).  
The authors declare no conflict of interest.