## 5 Conclusions and Future Works  
This work has proposed a feature engineering technique to address the challenges associated with sea state estimation/prediction using ML/DL models. We extract fine spectral-temporal information using continuous wavelet transform on the short observation windows. Normalization and filtering salient features based on wavelet coefficient intensity on different projection planes help sieve features with high entropy for classification and establish a data framework capable of accommodating extensive process information. To enhance feature variance, additional endogenous attributes are incorporated. We demonstrate our technique using data from a floating simple geometrical sphere to map sphere responses to wave inputs. Evaluating the technique's computational efficiency and validation accuracy, we processed 40 min of experimental data. Our specifically tailored technique outperforms conventional ML techniques such as CNN and ANN, reducing training time from 493 min for CNN to 4 s with validation accuracy of 98.38% and 91.92%. Even using a single GPU, our technique surpasses CNN and ANN in training time. While the SVR and classification models also perform well, feature-engineered data notably reduces training time for CNN and ANN from minutes to 154 s and 50 s with validation accuracy above 90%. Testing various classifiers, we found that LDA improves validation accuracy further to 96.24% for identical feature resolution. We discuss the reasons behind the improvement and address classification issues in transition conditions by testing feature-engineered data in a synthesized transitory condition between various waves. Our technique exhibits remarkable performance and sensitivity in estimating transitions between inputs over time. While promising, further validation with higher-fidelity datasets under more complex conditions, such as varying wave angles of approach, is necessary. In addition, the use of short observation windows may limit the capture of long-term dependencies and broader trends, potentially impacting accuracy in certain applications. Overall, the proposed data analysis technique holds promise for practical implementation of ML models in real-world applications. Its potential extends beyond hydrodynamics, encompassing seakeeping, ship response prediction, sea state estimation using ship motion, and maneuvering. Moreover, it can be applied to any stochastic system requiring a data-driven model trained for numerous input/output mappings to replicate real-world scenarios. Despite the progress made, future investigations could explore additional dimensions of the research. Firstly, benchmarking the model against a wider range of process noise levels would provide insights into how validation accuracy may be affected. Furthermore,  
Figure 23: Comparison of raw estimation with 3 s lagged smoothening filter. the current setup only accounts for variations in wave frequency, so future studies could expand to examine the impact of changes in other variables, such as wave amplitude, on training time, and feature distribution. Additionally, the observation window in the current study was fixed at 40 s. The current setup might show either positive or negative sensitivity to this factor. Future work should investigate the impact of varying the observation length on feature variability and further training. Scaling from experimental data to real-world trials is a perennial challenge in hydrodynamics studies, and further research is needed to assess the impact of scaling on ML performance. Beyond seakeeping applications, the current framework could be tested on different datasets and applied to other phenomena, such as atmospheric events like wind and rain or the relationship between weather temperature and frost formation. H.M.: Conceptualization, methodology, software, validation, writing original draft; H.E. : Project administration, conceptualization, supervision, review and editing; D.H.: Conceptualization, supervision, review and editing; E.G. : Experimental test, data procuration.