This paper proposed an MSC-Transformer model based on the Transformer’s neural network, which was applied to seabed sediment classification. The data came from about 2900 km2 of seabed area on the northern slope of the South China Sea. Using the submarine backscattering intensity and depth data obtained by the sub-bottom profiler, combined with latitude and longitude information, a seabed dataset of the slope area of the South China Sea was constructed. Moreover, using the MSC-Transformer, the accurate identification and judgment of sediment types such as calcareous bio-silt, calcareous bio-clay silt, silty sand, medium sand and gravel sand were realized. Compared with the conventional deep neural network CNN, RNN, etc., the model shows advantages when applied to the sediment dataset of the shallow sea slope region of the South China Sea. This confirms the feasibility and validity of the model and provides a reliable and accurate tool for seabed sediment classification in the field of marine science. The completeness and accuracy of the dataset and the good performance of the model provide a solid foundation for the scientificalness and practicability of the study.
The transition to renewable energy sources is crucial for mitigating the impacts of climate change and achieving sustainable development goals. In China, the rapid industrialization and urbanization have led to an increasing demand for energy, highlighting the urgent need to transition to alternative energy sources. This study aims to evaluate the effectiveness of alternative energy sources in China, considering multiple criteria such as cost, environmental impact, energy output, reliability, and scalability. We employed a Multi-Criteria Decision Analysis (MCDA) approach to compare and rank different energy sources based on these criteria. Our findings indicate that wind energy is the most effective alternative energy source overall due to its relatively low cost, high efficiency, moderate environmental impact, good scalability, and high reliability. However, geothermal energy had the lowest levelized cost of electricity (LCOE), while hydro energy performed well in terms of efficiency and reliability. The environmental impact of wind energy was found to be moderate but still less severe compared to other energy sources. Our study provides important insights into the trade-offs and considerations that policymakers and industry leaders must make when selecting which energy sources to prioritize. The findings highlight the need for a comprehensive and integrated approach to energy policy that balances economic, environmental, and social considerations. In conclusion, this study contributes to the literature by emphasizing the importance of considering multiple criteria when evaluating alternative energy sources. Our findings can inform policy decisions regarding the development of a sustainable and reliable energy mix in China, and have important implications for other countries seeking to transition to renewable energy sources.
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