As the global climate changes, there is an increasing focus on the oceans and their protection and exploitation. However, the exploration of the oceans necessitates the construction of marine equipment, and the siting of such equipment has become a significant challenge. With the ongoing development of computers, machine learning using remote sensing data has proven to be an effective solution to this problem. This paper reviews the history of remote sensing technology, introduces the conditions required for site selection through measurement analysis, and uses cluster analysis methods to identify areas such as machine learning as a research hotspot for ocean remote sensing. The paper aims to integrate machine learning into ocean remote sensing. Through the review and discussion of this article, limitations and shortcomings of the current stage of ocean remote sensing are identified, and relevant development proposals are put forward.

Long-term sea level variations in the northern South China Sea (SCS) are known to significantly impact coastal ecosystems and socio-economic activities. To improve sea level prediction accuracy, four models—harmonic analysis and three artificial neural networks (ANNs), namely genetic algorithm-optimized back propagation (GA-BP), radial basis function (RBF), and long short-term memory (LSTM)—are developed and compared using 52 years of observational data (1960–2004). Key evaluation metrics are presented to demonstrate the models’ effectiveness: for harmonic analysis, the root mean square error (RMSE) is reported as 14.73, the mean absolute error (MAE) is 12.61, the mean bias error (MBE) is 0.0, and the coefficient of determination (R²) is 0.84; for GA-BP, the RMSE is measured as 29.1371, the MAE is 24.9411, the MBE is 5.6809, and the R² is 0.4003; for the RBF neural network, the RMSE is calculated as 27.1433, the MAE is 22.7533, the MBE is 2.1322, and the R² is 0.4690; for LSTM, the RMSE is determined as 23.7929, the MAE is 19.7899, the MBE is 1.3700, and the R² is 0.5872. The key findings include the following: (1) A significant sea level rise trend at 1.4 mm/year is observed in the northern SCS. (2) Harmonic analysis is shown to outperform all ANN models in both accuracy and robustness, with sea level variations effectively characterized by four principal and six secondary tidal constituents. (3) Despite their complexity, ANN models (including LSTM) are found to fail in surpassing the predictive capability of the traditional harmonic method. These results highlight the continued effectiveness of harmonic analysis for long-term sea level forecasting, offering critical insights for coastal hazard mitigation and sustainable development planning.

As an important marginal sea in the western Pacific, sea-level changes in the South China Sea not only respond to global warming but are also regulated by regional ocean dynamics and climate modes, exerting profound impacts on the socioeconomic development and engineering safety of coastal regions. To address the widespread issues of low accuracy and robustness in existing sea-level prediction models when handling nonlinear, multi-scale sequences, as well as the complexity of sea-level change mechanisms in the South China Sea, this study constructs a hybrid model combining Singular Spectrum Analysis and Long Short-Term Memory neural networks (SSA-LSTM). The coral skeletal oxygen isotope ratio (δ¹⁸O) used in this study is a key indicator for characterizing the marine environment, defined as the per mille difference in the ¹⁸O/¹⁶O ratio of a sample relative to a standard. Based on coral δ¹⁸O data from the South China Sea, the sea level from 1850 to 2015 is reconstructed. SSA is then applied to decompose the sea-level data into trend and periodic components. The trend component, accounting for 37.03%, and components 2 to 11, containing major periodic information, are extracted to reconstruct the sea-level series. The reconstructed series retains 95.89% of the original information. The trend component is modeled through curve fitting, while the periodic components are modeled using an LSTM neural network. Optimal hyperparameters for the LSTM are determined through parameter sensitivity analysis. An integrated SSA-LSTM model is constructed to predict sea level in the South China Sea, and its predictions are compared with those from a Singular Spectrum Analysis-Autoregressive Integrated Moving Average (SSA-ARIMA) model. The results indicate that from 1850 to 2015, sea level in the South China Sea exhibits periodic fluctuations with a significant overall upward trend. Specifically, the growth rate from 1921 to 1940 reaches 5.49 mm/yr. Predictions from the SSA-LSTM model are significantly higher than those from the SSA-ARIMA model. The SSA-LSTM model projects that from 2016 to 2035, sea level in the South China Sea will continue to rise at a fluctuating rate of 0.75 mm/yr, with a cumulative rise of approximately 15 mm. This study provides a novel methodology for investigating the mechanisms of sea-level change in the South China Sea and offers a scientific basis for coastal risk management.

With the rapid development of offshore photovoltaic (PV) systems, PV support structures have become a critical component in offshore PV installations. The material properties of these structures significantly influence the safety and reliability of the entire system. 3D printing technology, leveraging its advantages such as rapid prototyping, complex structure manufacturing, and high material utilization, holds broad application prospects in the field of offshore PV. However, the infill pattern of 3D printing materials can significantly affect their mechanical properties. Marine PV systems require extremely high resistance to wave action, tensile strength, and torsional performance, while offshore PV support structures need sufficient compressive capacity. Therefore, this study aims to investigate how different infill patterns affect the compressive properties of 3D printed materials, thereby optimizing material selection and printing processes for offshore PV applications. Through experimental design, a variety of common infill patterns were selected. Universal testing machines and torsion testing machines were used to conduct systematic tests on compressive strength, elastic modulus, and compressive fracture strain. The results showed that different infill patterns have a significant impact on compressive properties, among which the honeycomb infill exhibited the best overall mechanical performance, effectively enhancing load-bearing capacity and stability. Based on the experimental results, appropriate infill configurations and material combinations for different components of offshore PV systems were proposed. The feasibility of optimizing 3D printing processes to improve the overall performance of offshore PV structures was further explored. The findings of this study not only provide a theoretical basis for material selection and process optimization in 3D printing for offshore PV systems but also offer important references for promoting the application of 3D printing technology in this field.

This study establishes a numerical wave simulation framework based on Simulating Waves Nearshore (SWAN) to investigate the characteristics of typhoon-generated waves in the coastal waters of the northern South China Sea. To better represent typhoon-wind features in the study area, a blended typhoon wind field was constructed by combining the ERA5 background wind field with a Holland parametric typhoon wind field. The numerical simulations were performed with a spatial resolution of 0.25° × 0.25°. Model performance was evaluated against in situ observations and Jason-3 satellite altimetry data, showing satisfactory agreement regarding significant wave height (H_s). Hindcast simulations were conducted for 50 typhoon events that affected the coastal waters of Guangdong Province, China, during 2013–2025. Eighteen representative points were selected within marine-ranching areas distributed across the western, central, and eastern coastal sectors of Guangdong. Typhoon-wave events occurred most frequently in the central coastal sector, which also exhibited the greatest significant wave heights. In this sector, the maximum significant wave height reached 12.13 m, while the mean significant wave height was 3.37 m. The peak wave energy spectral density decreased from the central sector toward both the western and eastern sectors. In the central sector, the peak wave energy spectral density exceeded 90 m²/Hz, with a domain-wide maximum value of 297.60 m²/Hz. Across all representative points, the peak frequencies ranged from 0.07 to 0.12 Hz. Analyses of typhoon event characteristics, significant wave height features, and wave spectral properties in this study offer background references for marine-ranching planning and regional hazard assessment.