## 9 Discussion  
In this section, we discuss the findings of this research study. The critical analysis and evaluation for the integration of Metocean data intelligent operations using ML models based on empirical studies have been revealed. The oil and gas industry's business objective is to digitalize its production and development, which cannot be achieved without the implementation of ML techniques and big data analytics. In addition, the transformation and integration of existing Metocean architecture for oil and gas data operation can be enhanced with the advancement of ML techniques. The Metocean data development and integration significantly improved after applying ML techniques such as better prediction, providing efficient results, saving time, reducing system complexity, and reducing operational overheads. Moreover, the ML methods vary in different categories with its subsection known as DL in Metocean data analytics and integration. As shown in Table 2, we indicated AI models that could fit in the Metocean data system. AI models have been the environment for creating, training, and implementing models based on available datasets for better decision making and predictions, for example, classification, regression, and clustering, where authors compare several ML models which include ANN, XGBoost, SVM, and statistical regression for ship power modelling with measuring Metocean condition and hull maintenance [94; 95]. Convolutional neural network (CCN) models are part of deep neural networks which play vital roles in image classification problems through their multiple convolution layers with kernels to detect complex features. Adding to this, a novel CCN model has been applied for failure identification of mooring line and turret-moored FPSO systems [96]. Random forests (RF) are ML models based on trees assembled for prediction with high effective performance to solve regression and classification problems. For example, RF models have been developed to predict spatial distribution and frequency groundings of a ship [97]. As the modern technique provides an avenue for data-driven modelling using various methods, genetic programming (GP) techniques have evolved programs that can develop and compute solutions to human problems which cannot be solved directly. For example, the GP technique has been used for partial differential equations (PDE), discovery of Metocean processing, and acoustics function discovery [98]. The Bayesian framework for ML has been applied through Gaussian processing (GP) for efficient integrity management (IM) of steel lazy wave risers (SLWRs). However, GP is genetically supervised learning for probabilistic classification problems and regression [99]. Lastly, the state-of-art ML presented is efficient and effective in performance, which includes predictions, classifications, and decisions. All models discussed can fit the Metocean data system for better forecasting, integration, and intelligent operations. As shown in Table 3, state-of-art studies have been listed with insight on Metocean data integration in the oil and gas domain using ML techniques. Notably, ANN, SVM, RF, and XGB are the most used models for Metocean data analytics and integration due to their efficient performance. However, ML performance towards the prediction of Metocean data interoperability for intelligent operations can be enhanced through hyperparameter tuning and feature selection techniques for better performance.