# Scour Detection with Monitoring Methods and Machine Learning Algorithms--A Critical Review
## 2 Conventional Monitoring-Based and Machine Learning-Based Methods to Identify Scour
### Direct Monitoring-Based Studies
#### 2.1.2 Cluster 2--Machine Learning-Based Research
In terms of the accuracy level of predictions made, [107] and [108] stated that their predictions are in good agreement with the measurements, with only one dataset outside the 50% error line. They stated that their approach was more effective in terms of accuracy when compared to empirical formulas such as M/S, MBW, HEC-18, 65-1, and 65-2. According to their sensitivity analysis, [108] stated that the most effective parameter in predicting scour was pier width. They also suggested an increase in the quantity of training data to predict scour depths more accurately. [109] investigated scour occurrence for different shapes of the abutment. They noted that, for all shapes of the abutment, the most effective parameter was the Excess Abutment Froude number (Fe). Moreover, for vertical-wall-shaped abutments, the most effective parameter combination contained Excess Abutment Froude number, relative sediment size (d\({}_{50}\)/l, where \"d\({}_{50}\)\" is the sediment size and \"l\" is the dimension of the abutment which is perpendicular to the flow), and relative flow depth (h/l). Finally, for semicircular and 45\({}^{\circ}\) wing shape abutments, the combination of Excess Abutment Froude number and relative sediment size was the most effective parameter combination in scour prediction. The weighted instance handler wrapper-Kstar for vertical-wall abutments, random committee-Kstar for semicircular walls, and 45\({}^{\circ}\) wing wall were the best algorithms among five novel hybrid algorithms studied. Their algorithms outperformed the empirical formulas of [PERSON] and [PERSON] [6] and Muzzammil [7]. The hybrid approach of [107], based on a random subspace meta classifier, resulted in the pile cap level being the most sensitive factor in the prediction of complex piers' local scour depths. The reduced error pruning tree base classifier resulted in similar root mean square errors to artificial neural networks, support vector machines, and M5P. The predictions obtained through reduced error pruning tree and other machine learning algorithms were significantly better than the scour depths computed with the empirical models of FDOT and HEC-18. Both [109] and [110] were able to increase the prediction power of standalone algorithms with the hybrid algorithms they proposed. [111] proposed a self-adaptive evolutionary extreme learning machine to predict scour around bridge piers. They indicated that the ratio of the median diameter of particle size to flow depth, the ratio of pier length to flow depth, and the ratio of pier width to flow depth were the most effective parameters. Self-adaptive evolutionary extreme learning machines outperformed artificial neural networks and support vector machines. In 2018, [112] proposed 25 models to predict scour around coastal and hydraulic pile groups. The extreme learning machine model generated had the most optimal input parameter combination and provided better results than the artificial neural networks and support vector machines considered. They also identified that pier diameter affected the predictions the most. Later in 2019, it was shown that extreme learning machines were one of the most effective heuristic optimization algorithms for non-linear systems [113]. The sensitivity analysis included 31 models with different input combinations [113]. Their approach outperformed the empirical equations of [PERSON] and [PERSON] [114], [PERSON] [115], [PERSON] [116], and [PERSON] and [PERSON] [13]. They recommended that the proposed methodology be improved by utilizing other artificial intelligence methods such as gene expression programming, and the group method of data handling. Furthermore, [117] generated a model based on gradient tree boosting to predict scour depth around piers with different geometries: rectangular, circular, sharp-nosed, and round-nosed. In conclusion, gradient tree boosting was more accurate and effective than the group method of data handling for all shapes. It was noted that the model worked best with the rectangular form.