# 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
Back propagation neural network is defined as a method that regularly modifies the weights of the connections of the network to make the difference between the absolute output vector and requested output vector minimum [83]. It is a monitor-learning technique which uses gradient descent and multivariate linear regression for recognition of change with the weight of connection in the network [84]. In many studies that adopted back propagation neural network, the accuracy levels of empirical scour formulas were increased [85]. Multivariate linear regression seeks for the relationship between the variable to be analyzed and its independent variables, and applied for prediction purposes [86]. Trial-error method [87] and beetle antennae search [87; 88] are generally preferred when the hyperparameters of back propagation neural network models are tuned. Support Vector Machines by [PERSON] navigate input vectors from primary feature space to a feature space which is multi-dimensional via a Kernel function [89]. Support Vector Machines were derived from statistical learning theory and are considerably new and encourage learning to sort functions to recognize the pattern or to operate an evaluation for a function within a regression problem [90]. Variations of cross-validation such as Leave One Out and k-Fold, Xi-Alpha bound, generalized comparative Generalized Kullback-Liebler Distance, approximate span bound, and Radius-margin bounds are some of the successful performance measures to pick hyperparameters of a Support Vector Machine model; except for Vapnik Chervonenkis (VC) bound, which failed to provide and efficiently estimate the parameters [91]. Support Vector Machines can deal with various feature spaces and can categorize the data with no structure or semi-structure (i.e., image and texts) very well; nevertheless, they require high computation of complicated data, are bad at processing noisy input, and comprehending the resulting model, the effect, and weights of the variables is not easy [82]. Compared with the conventional algorithms based on gradient, extreme learning machines learn more quickly and eliminate some challenges of stating a criterion to stop, rate of learning, duration of learning, and local minima [88]. Their fundamental benefit is reducing the volume of calculation, which is particularly related to working with the templates within a high dimensional space [89]. Differential evolution algorithms [92], statistical learning theory [93], and particle swarm optimization [94] are proven to be effective in optimizing hyperparameters of the extreme learning machines. Gradient tree boosting aims to find a link which is capable of navigating the input to output to minimize the difference between the loss function for the pre-defined and anticipated value [95]. Although the process is time-consuming, Bayesian hyperparameter optimization is a method to set the parameters of a gradient tree boosting model and grid search; while GPU acceleration provided more speed in the selection process [96]. Another algorithm providing successful applications for scour estimation [97; 98] is group method of data handling, a procedure which organizes itself through models created step-by-step, based on their performance assessment on a group of multi-input-single-output data couples [99]. Grid search is one of the most common methods to set parameters of group method of data handling models. [100; 101; 102]. Finally, ANFIS is a composite neuro-fuzzy system [103] and is the combination of neural networks and fuzzy logic methods; internal parameters are learnt off-line, such as neural network does and acts fuzzy logic system when it is operated [104]. Grasshopper optimization algorithm [105] and cultural algorithm [106] are utilized to set parameters of ANFIS models.