Research Article
Sea Surface Temperature Prediction in China Sea Based on SAM-LSTM Approach
Issue:
Volume 8, Issue 2, June 2024
Pages:
14-22
Received:
23 March 2024
Accepted:
17 April 2024
Published:
28 April 2024
Abstract: Sea Surface Temperature (SST), a critical environmental element in the ocean, significantly impacts the global atmosphere-ocean energy balance and holds the potential to trigger severe weather like droughts, floods, and El Niño events. Therefore, the prediction of future SST dynamics is crucial to identifying these extreme events and mitigating the damage they caused. In this study, we introduce a time series prediction method based on the Self-Attention Mechanism-Long Short-Term Memory (SAM-LSTM) model. In addition, the historical time-series satellite data of SST anomaly (SSTA) is used instead of SST itself considering that the fluctuations of SST are very small compared to their absolute magnitudes. The Seasonal-Trend decomposition using Loess (STL) method is adopted to decompose the complex non-linear SSTA time series into trend components, seasonal components, and residual components. Then, the deseasonalized time series data at 6 locations in the Bohai Sea are used to train and valid the developed SAM-LSTM model. After that, the validated models are applied to the Yellow Sea, East China Sea, and South China Sea. The experimental results show that the combination of STL time series decomposition and SAM-LSTM can achieve high-precision prediction of daily SSTA than LSTM. This suggests that the methodology used in this paper has a good application for short-term daily SST prediction.
Abstract: Sea Surface Temperature (SST), a critical environmental element in the ocean, significantly impacts the global atmosphere-ocean energy balance and holds the potential to trigger severe weather like droughts, floods, and El Niño events. Therefore, the prediction of future SST dynamics is crucial to identifying these extreme events and mitigating the...
Show More
Research Article
Study on the Influence of Canopy Density on Cycling of Soil Available N in Different Landform of Loess Plateau in China
Issue:
Volume 8, Issue 2, June 2024
Pages:
23-40
Received:
7 April 2024
Accepted:
6 May 2024
Published:
10 May 2024
Abstract: In this paper, a total of 330 soil samples with 0-100cm soil depth of 66 planted square forest (10*10m) with different canopy density in the Loess Plateau were selected for the determination and analysis of soil N content in different soil layers, and the effects of different canopy density on soil N cycle under different topographic factors of planted forest were studied. The results showed as follows: (1) the migration mechanism of different N forms to the root surface was different, the migration of nitrate nitrogen to the root surface mainly depended on mass flow, there was enrichment phenomenon near the root, ammonium nitrogen mainly through diffusion, resulting in deficiency and loss in the near rhizosphere, and the leaching loss of nitrate nitrogen was affected by soil water and root growth. (2) The thickness, composition and decomposition rate of litter were different due to different canopy density, which affected the content of ammonium nitrogen and nitrate nitrogen in forest soil. (3) Although the change of different regions in this region was spatially different, keeping the stand cover in the middle and high range of 0.75-0.8 can be conducive to maintaining the balance between the consumption of soil nutrients by the stand and the supplement of nutrient consumption, which can also be conducive to the sustainable recovery and growth of the stand in this region.
Abstract: In this paper, a total of 330 soil samples with 0-100cm soil depth of 66 planted square forest (10*10m) with different canopy density in the Loess Plateau were selected for the determination and analysis of soil N content in different soil layers, and the effects of different canopy density on soil N cycle under different topographic factors of pla...
Show More
,
Xueqing Zhang*
