# Machine Learning Techniques in Structural Wind Engineering: A State-of-the-Art Review
## 5 Discussion and Conclusions  
As in any other application, the quantity and the quality of data is the main challenge in successfully implementing ML models in the broader area of structural wind engineering. It is important to mention that the quality of the dataset used for training is as important as the quantity of data. The measurements usually may involve some anomalies such as missing data or outliers, thus removing the outliers is essential for the accuracy and robustness of the model [168; 169]. ML algorithms are data-hungry processes that require thousands if not millions of observations to reach acceptable performance levels. Bias in data collection is another major drawback that could dramatically affect the performance of ML models [170]. To this end, some literature recommends that the number of datasets shall not be taken less than 10 times the number of independent variables, according to 10 eventsper variable (EPV) [171]. Meanwhile, K-means clustering was used in many different studies due to its ability to analyze the dataset and recognize its underlying pattern. Most of the ML techniques need several trials and experiments through the validation process to develop a robust model with high accuracy prediction levels. For instance, whenever ANN is used, several trials are conducted for training purposes in terms of choosing the number of hidden layers and the number of neurons in each layer. The ANN method is not recommended for datasets with a small sample size because this would achieve double the mean absolute error (MAE) compared to other ML techniques [134]. ANN is capable of learning and generalizing nonlinear complex functional relationships via the training process, but there is currently no theoretical basis for determining the ideal neural network configuration [81]. The architecture of ANN and training parameters cannot be generalized even within data of similar nature [141]. Generally, one hidden layer is enough for most problems, but for very complex, fuzzy and highly non-linear problems, more than one hidden layer (node) might be required to capture the significant features in the data [172]. The number of hidden nodes is determined through trials and in most cases, this number is set to no more than 2n + 1, where n is the number of input variables [173]. In addition, a study by [PERSON] and [PERSON] [174] reviewed different models for calculating the number of hidden neurons and developed a proposed method that gave the least MSE compared to the other models. The proposed approach was implemented on wind speed prediction and was very effective compared to other models. Furthermore, a general principle of a ratio of 3:1 or 3:2 between the first and second hidden nodes provides a better prediction performance compared to other combinations [175]. Generally, a robust neural network model can be built of two hidden layers and ten neurons and will give a very reasonable response. ANN also appears to have a significant computational advantage over a CFD-based scheme. In ANN, the computational work is mainly focused on identifying the proper weights in the network. Once the training phase is completed, the output of the simulated system could be obtained through a simple arithmetic operation with any desired input information. On the other hand, in the case of a CFD scheme, each new input scenario requires a complete reevaluation of the fluid-structure interaction over the discretized domain. From the review of the literature, it was also apparent that ANN has weighted advantages over other ML methods. However, there are some challenges accompanying implementing ANN in certain types of wind engineering applications. ANN is problematic in predicting the pressure coefficients within the leading corner and edges due to the separation which is accompanied by high rms pressure coefficient values and corner vortices. This may be eliminated by training datasets of full- or large-scale models that contain high-resolution pressure tapped areas.