论文标题
使用机器学习算法建模孟加拉中央湾地区的海面$ p $ $ _2 $
Modeling the sea-surface $p$CO$_2$ of the central Bay of Bengal region using machine learning algorithms
论文作者
论文摘要
本研究探讨了先进的机器学习算法在孟加拉湾（Bob）开放海洋中预测海面$ p $ $ _2 $的能力。我们从邮轮和系泊站收集可用的观察结果（EEZ以外）。由于鲍勃（Bob）中数据的匮乏，我们试图根据海面温度（SST）和海面盐度（SSS）预测$ P $ CO $ _2 $。将MLR，ANN和XGBOOST算法与常见数据集进行比较，表明XGBoost在BOB中预测Sea-Surface $ P $ CO $ _2 $的表现最佳。使用卫星衍生的SST和SSS，我们使用XGBoost型号预测Sea-Surface $ p $ $ _2 $，并将其与Rama Buoy的原位观察结果进行比较。该型号的性能令人满意，其相关性为0.75，RMSE为$ \ pm $ 12.23 $μ$ $ atm。进一步使用该模型，我们模仿了2010 - 2019年间中央鲍勃的海面$ p $ $ _2 $中的每月变化。使用卫星数据，我们表明中央鲍勃以每年0.0175的速度变暖，而SSS则以每年-0.0088的速度降低。建模的$ p $ co $ _2 $显示出每年以-0.4852 $ $ $ $ atm的降低。我们执行灵敏度实验，以发现SST和SSS的变化$ \ $ \ $ 41 $ \％$ \％$和$ \ $ \ $ 37 $ \％$ $ \％$ $ \％$ $ \％$ $ \％$ $ \％$ $ p $ co $ _2 $的下降趋势。季节性分析表明，季风前季节的海面$ p $ co $ _2 $的降低率最高。
The present study explores the capabilities of advanced machine learning algorithms in predicting the sea-surface $p$CO$_2$ in the open oceans of the Bay of Bengal (BoB). We collect the available observations (outside EEZ) from the cruise tracks and the mooring stations. Due to the paucity of data in the BoB, we attempt to predict $p$CO$_2$ based on the Sea Surface Temperature (SST) and the Sea Surface Salinity (SSS). Comparing the MLR, the ANN, and the XGBoost algorithm against a common dataset reveals that the XGBoost performs the best for predicting the sea-surface $p$CO$_2$ in the BoB. Using the satellite-derived SST and SSS, we predict the sea-surface $p$CO$_2$ using the XGBoost model and compare the same with the in-situ observations from RAMA buoy. The model performs satisfactorily, having a correlation of 0.75 and the RMSE of $\pm$ 12.23 $μ$atm. Further using this model, we emulate the monthly variations in the sea-surface $p$CO$_2$ for the central BoB between 2010-2019. Using the satellite data, we show that the central BoB is warming at a rate of 0.0175 per year, whereas the SSS decreases with a rate of -0.0088 per year. The modeled $p$CO$_2$ shows a declination at a rate of -0.4852 $μ$atm per year. We perform sensitivity experiments to find that the variations in SST and SSS contribute $\approx$ 41$\%$ and $\approx$ 37$\%$ to the declining trends of the $p$CO$_2$ for the last decade. Seasonal analysis shows that the pre-monsoon season has the highest rate of decrease of the sea-surface $p$CO$_2$.
