###### Abstract  
This study presents the development and validation of a complex-valued neural network model to predict wave conditions--such as wave direction, height, and period--surrounding floating structures. Accurate wave predictions are crucial for optimizing the design, operation, and maintenance of offshore platforms and floating offshore wind turbines, particularly in the context of digital twins. The proposed methodology leverages motion data obtained through numerical simulations of a floating structure to train the prediction model, enabling it to predict both the amplitude and phase information of the surrounding waves. The model successfully addresses the challenges of representing wave direction data in polar coordinates and capturing phase differences between motion components, which are difficult for traditional real-valued neural networks. The performance of the model was validated through various test cases, with the maximum prediction error found to be less than 10% and most predictions showing an error of less than 5%. Wave direction predictions demonstrated high accuracy, with errors consistently below 2%. While the model was trained using pseudo-measurement data, the results suggest that high-accuracy predictions can be achieved using real-world measurement data. This work contributes to enhancing wave prediction models for floating structures and is expected to improve the safety, performance, and long-term stability of such systems. c 2025 15 1603 13 January 2025 30 January 2025 4  
stability [5; 6]. Additionally, in harbors, resonance phenomena caused by specific wave conditions can interfere with normal operations [7; 8]. Therefore, accurately predicting factors such as wave period, height, and direction is essential to prevent resonance and ensure the safe operation of the structure. Traditionally, wave measurements have been conducted using wave buoys equipped with sensors to measure motion and collect wave data. However, this method is vulnerable to damage and loss, making it particularly challenging to apply in deep-water environments [9]. On-board measurement systems, such as X-band radars, scan the sea surface around floating structures to capture wave information. These systems are easy to install and maintain but are sensitive to environmental conditions, with limitations in wavelength and resolution [10]. Inertial measurement unit (IMU) sensors attached to floating structures can also be used to assess sea states. This method treats the floating structure as a buoy, deriving the relationship between the structure's motion and the waves based on data collected from the structure [11]. However, the measured data reflect the motion of the vessel, making it challenging to capture high-frequency wave data that have minimal impact on the vessel's movement. This limitation becomes more significant for larger vessels [9]. Despite these drawbacks, the simplicity of this method makes it easily applicable to existing vessels. Furthermore, motion data from these vessels can be directly utilized for both wave estimation and structural integrity assessment, making them highly valuable for digital twin applications [2]. Motion-based wave estimation can be performed by comparing motion data measured directly on the floating structure with results from pre-conducted numerical simulations [12; 13]. In this approach, specific motions measured on the structure are correlated with the sea state that caused them, based on prior simulation results [14]. Machine learning techniques, which are increasingly attracting attention across various fields, can facilitate this process [13; 15]. In this study, we focus on utilizing artificial neural networks (ANNs) for wave prediction. By generating a dataset of structural responses under various wave conditions through numerical simulations, we can train a neural network model to predict wave characteristics [13; 15].