# 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
[118] proposed a model using an evolutionary radial basis function neural network and it outperformed not only empirical HEC-18, [PERSON]'s, [PERSON]'s (1956), and [PERSON]'s methods, but also other algorithms being compared (back-propagation neural network, genetic programming, M5 regression tree, and support vector machine). They suggested future studies consider the efficiency, duration required for computation, stability of artificial intelligence methods, and techniques in between complements. In their research based on support vector regression, [121] obtained better predictions when support vector regressions are used together with algorithms for selecting features. The variable neighborhood search algorithm had the best performance when compared with sequential forward selection and sequential backward selection for parameter selection. The proposed method also made more accurate scour depth predictions compared to HEC18, Melville and Coleman [122], and Ataie-Ashtiani [123]. They recommended a future study of the support vector regression and other Kernel Functions together. Nondominated sorting genetic algorithm-based method of [124] outperformedboth hydraulic engineering circular no. 18 and Froehlich equations and gene expression programming. The authors highlighted that their approach was as simple as empirical formulas. They noted that the failure rates should be decreased. They also suggested modifying the function and picking field data and other independent parameter combinations more explicitly. [85] found out that ignoring randomly selected neurons (dropout layers) on deep neural networks could not level up accuracy level significantly. When the prediction values were compared with the measurements, it was seen that both deep neural networks and back-propagation neural networks performed equally well in predicting the scour depth. Additionally, predictions of deep neural networks outperformed four empirical formulas listed in Table 3; root mean square errors were 2-4 times better. [125] proposed an algorithm of sequential quadratic programming optimization and managed to increase the accuracy of the existing formulas of HEC18, Melville and Coleman [122], and Ataie-Ashtiani [123] up to 2-4 times. Overall, the majority of the data used in the studies reviewed under the machine learning-based cluster were used for training (to feed machine learning algorithms) and the rest was kept for validation. Selecting the training data correctly by pre-processing before applying the algorithm-based models helped the models approach accurate predictions more quickly. The studies provided root mean squared error, mean square error, coefficient of determination (R2), or cumulative sums of orthogonal distance indexes to compare their approaches with frequently used machine learning algorithms and/or empirical scour formulas. The main objective was to generate a new model which provides more robust scour depths than empirical scour formulas and/or some frequently used machine learning algorithms. These models adopt one or more (hybrid) algorithms and heuristic parameter selection algorithms for better predictions and computation time. [74] stated that the usage, demand, and reliability of heuristic techniques have quickly boosted their use because these solve actual problems of life simply, quickly, efficiently, and in an applicable way. Of the studies summarized here in this review, it can be seen that those with sensitivity analysis, i.e., following an optimization method for hyperparameter selection, resulted in faster and more accurate predictions of scour depth. Particle swarm optimization and artificial bee colony were two of the methods used in the studies presented here. With such optimization methods, it was easier to identify the most effective parameter or combination of parameters influencing the prediction.