## 2 AI Predictive Techniques on Scouring below Seabed Pipeline: A Brief Review
### Current-Induced-Pipeline Scour  
In the case of pipeline scour due to currents, a large number of investigations was attempted to use various AI models along with preservation of scouring conceptions. [PERSON] and [PERSON] [22] applied GP model to experimental datasets, which were extracted from two various flow and sediment conditions. Ultimately, they proposed a regression-based equation, which applies to both clear-water (CW) and live-bed (LB) conditions. However, two major drawbacks arise from their research work. In the first place, they should have separated the entire dataset into two basic sections depending on whether the scouring process was governed by clear-water or live-bed approach flow conditions. It is well known that these two regimes imply different scouring processes. Incidentally, [PERSON] and [PERSON] [22] did not consider the ratio \(e/D\) of the pipeline embedment depth \(e\) to the pipeline diameter \(D\) as a controlling variable in modeling live-bed conditions; and the reason is that \(e/D\) in the experiments made by [PERSON] and [PERSON] [28], to which [PERSON] and [PERSON] [22] relate, was kept constant for live-bed conditions. The second drawback of the study by [PERSON] and [PERSON] [22] is that they considered the Reynolds number as the controlling variable of local scour depth, though approach flows were fully turbulent. These issues cause some concerns on the use of the equation developed by [PERSON] and [PERSON] [22] in practical applications. [PERSON] et al. [29] later employed the LGP technique to provide an empirical equation by still considering the same current-induced scour datasets. However, the equation given by LGP is also undermined by the same drawbacks as for [PERSON] and [PERSON] [22]. The same comments would apply to the study by [PERSON] and [PERSON] [15], though they applied ANN models. [PERSON] et al. [17] improved the ANFIS model by using Particle Swarm Optimization (PSO) to predict the local scour depth around seabed pipelines due to currents. However, the experimental datasets used in their study were released from a few studies with limited ranges for the governing variables. Furthermore, they did not always consider turbulent flows (i.e., flows independent from Reynolds number) during the development of the ANFIS-PSO models. Similarly, [PERSON] and [PERSON] [20] proposed a regression-based formulation by means of GEP technique. It looks like also their research did not follow physical insights of scouring mechanisms in terms of sediment bed motion and turbulent flow circumstances. [PERSON] et al. [23] applied the GMDH technique to predict the local scour for various sediment motion conditions of approach flows. They not only enhanced existing inaccuracies in the literature, but also covered fundamental misconceptions related to the physical insights of scouring processes in similar previous investigations. They concluded that the performance of GMDH was relatively more accurate than SVM technique. [PERSON] and [PERSON] [19] presented the CART-model-based probabilistic framework for various levels of risk in the design of undersea pipelines. They propose a regression equation, based on experimental data, for the economic design of a pipeline due to scour depth. [PERSON] [25] used the MARS technique to provide a robust formulation for the estimation of scour depth below pipelines exposed to clear-water circumstances. He found that MARS model was more accurate than the ANN technique and empirical equations. [PERSON] and [PERSON] [21] presented some regression-based equations by three robust AI models (i.e., EPR, GEP, and MT) for both clear-water and live-bed conditions. As a merit, they found that the performance of the AI models-based regression equations they considered had highly satisfying performance in comparison to empirical equations from the literature. [PERSON] et al. [18] employed SVM as a kernel-based AI model.