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Reducing the dependence on fossil fuels and utilizing the renewable energy have become essential due to the global resource exhaustion and unfriendly environmental impact from coal, petroleum and natural gas. Therefore, the rising attention has been paid to wave energy characterized by sustainability, clean, high energy density and extensive distribution. As one of the most important parameters of wave energy, significant wave height (SWH) is difficult to forecast accurately due to the complex marine condition and ubiquitous presence of chaos in nature. In this research, a novel hybrid model called STL–CNN–PE which combines seasonal-trend decomposition procedure based on loess (STL) and one-dimensional convolutional neural networks (CNN) with positional encoding (PE) was proposed to forecast SWH efficiently and accurately. To evaluate the proposed model comprehensively, the hourly standard meteorology data at station 44007, 46087 and 51000 from NOAA’s National Data Buoy Center were selected for model training and testing. The experimental results indicated that STL–CNN–PE provided more reliable forecasting values than the single model. Meanwhile, STL–CNN–PE had enormous advantage on speed and similar precision compared with EMD-LSTM. Finally, the experimental results revealed that the models provided better forecasting metrics at deeper waters.

For the survival and development of ‘One Belt, One Road’, the present work aimed to evaluate the current situation of wave energy resources around Sri Lankan (SL) waters. Thirty-year ERA-Interim wind data were used to drive the third-generation wave model WAVEWATCH-III, and the seasonal and regional distribution characteristics of wave energy resources in SL waters were analyzed. Furthermore, the optimal season and region that contribute most to wave power in the study area were determined. On the basis of 30-year hindcast wave data, the significant wave height and wave power density, the occurrence of available SWH and rich WPD, the effective storage of wave energy, and the contribution and stability of wave energy were also analyzed. Results show that extremely optimistic wave energy resources are found at the western, southern, and southeastern waters of SL; moreover, the period of June, July, August (JJA) has great advantages in terms of the overall level of WPD, wave energy effective storage, and the contribution rate of wave energy. In addition, the wave energy during JJA is more stable than that of other periods and thus is beneficial to the transformation and development of wave energy. This study also provides important guiding value for disaster prevention and reduction, coastal zone management, and coastal development in the crucial region of the 21st Century Maritime Silk Road.