# Machine Learning Techniques in Structural Wind Engineering: A State-of-the-Art Review
## 5 Discussion and Conclusions
It is important to note that whenever the data are fed into a regression model or ANN model (training, validation or testing process), all the predictors are normalized between [-1, 1] to condition the input matrix. In the case of implementing ANN models, the Levenberg-Marquardt algorithm and tangent sigmoid or logarithmic sigmoid activation functions shall be used. On the contrary, the Adam optimization algorithm and Rectified linear unit activation function shall be used whenever a DNN model (i.e., three or more hidden layers) is used as the ML technique. The literature review revealed that there are selected ML techniques that might not be as popular as ANN yet but with potential for future wind engineering applications and specific structural wind engineering problems. Less common ML methods, such as the wavelet neural network (WNN), are gaining increasing attention due to their advantage over ANN and other models in terms of prediction accuracy and good fit [176]. In addition, wavelet analysis is becoming popular due to its capacity to reveal simultaneous spectral and temporal information within a single signal [177]. Other ML techniques such as DL can be used as a probabilistic model for predictions based on limited and noisy data [178]. GANs models can be used in structural health monitoring for damage detections in buildings using different images for damage that occurred during an extreme wind event. BPNNand GRNN were used to acquire the missing data due to the failed pressure sensors while testing [179]. The GPR has high accuracy for time history interpolation and extrapolation and in the same context, the WNN predicts the time series accurately compared to other methods. Surrogate models were proved to be a powerful tool to integrate both FEM with ML models which could solve complex problems, such as the dynamic response of roofs and bridges while using the wind loads from physical testing measurements and can replicate more complex geometrically nonlinear structure behavior. Ensemble methods have shown good results in predicting wind-induced forces and vibrations of structures. Due to the time-consuming and cost-prohibitive nature of conducting a lot of wind tunnel testing, ML models such as DT, KNN, RF and GBRT are found to be efficient [144], and in turn, recommended for accurately predicting crosswind vibrations. The GBRT specifically can accurately predict crosswind responses when it is needed to supplement wind tunnel tests and numerical simulation techniques. ANN and GBRT are found to be the ideal ML models for wind speed prediction. Moreover, RF and GBRT are found to predict wind-induced loads more accurately when compared to DT. GBDT is preferable to be used over ANN in the case of a small amount of input data, as ANN requires a large amount of input data for an accurate prediction as explained above. Predicting wind gusts, which has not been a common application in the reviewed work in this study, can be achieved accurately using ensemble methods or neural networks and logistic regression [180, 181, 182, 183, 184, 185]. If only wind tunnel testing is considered, the wind flow around buildings, which provides deep insight into the aerodynamic behavior of buildings, is usually captured using particle image velocimetry (PIV). However, measuring wind velocities at some locations is a challenge due to the laser-light shielding. In such cases, DL might be used to predict these unmeasured velocities at certain locations as proposed in previous work [186]. Tropical cyclones and typhoons' wind fields can be predicted using ML models using the storm parameters such as spatial coordinates, storm size and intensity [187, 188]. Overall, it was demonstrated through this review that ML techniques offer a powerful tool and were successfully implemented in several areas of research related to structural wind engineering.