Oceanic dissolved oxygen(DO)in the ocean has an indispensable role on supporting biological respiration,maintaining ecological balance and promoting nutrient cycling.According to existing research,the total DO has dec...Oceanic dissolved oxygen(DO)in the ocean has an indispensable role on supporting biological respiration,maintaining ecological balance and promoting nutrient cycling.According to existing research,the total DO has declined by 2%of the total over the past 50 a,and the tropical Pacific Ocean occupied the largest oxygen minimum zone(OMZ)areas.However,the sparse observation data is limited to understanding the dynamic variation and trend of ocean using traditional interpolation methods.In this study,we applied different machine learning algorithms to fit regression models between measured DO,ocean reanalysis physical variables,and spatiotemporal variables.We demonstrate that extreme gradient boosting(XGBoost)model has the best performance,hereby reconstructing a four-dimensional DO dataset of the tropical Pacific Ocean from 1920 to 2023.The results reveal that XGBoost significantly improves the reconstruction performance in the tropical Pacific Ocean,with a 35.3%reduction in root mean-squared error and a 39.5%decrease in mean absolute error.Additionally,we compare the results with three Coupled Model Intercomparison Project Phase 6(CMIP6)models data to confirm the high accuracy of the 4-dimensional reconstruction.Overall,the OMZ mainly dominates the eastern tropical Pacific Ocean,with a slow expansion.This study used XGBoost to efficiently reconstructing 4-dimensional DO enhancing the understanding of the hypoxic expansion in the tropical Pacific Ocean and we foresee that this approach would be extended to reconstruct more ocean elements.展开更多
PM2.5 concentration prediction is of great significance to environmental protection and human health.Achieving accurate prediction of PM2.5 concentration has become an important research task.However,PM2.5 pollutants ...PM2.5 concentration prediction is of great significance to environmental protection and human health.Achieving accurate prediction of PM2.5 concentration has become an important research task.However,PM2.5 pollutants can spread in the earth’s atmosphere,causing mutual influence between different cities.To effectively capture the air pollution relationship between cities,this paper proposes a novel spatiotemporal model combining graph attention neural network(GAT)and gated recurrent unit(GRU),named GAT-GRU for PM2.5 concentration prediction.Specifically,GAT is used to learn the spatial dependence of PM2.5 concentration data in different cities,and GRU is to extract the temporal dependence of the long-term data series.The proposed model integrates the learned spatio-temporal dependencies to capture long-term complex spatio-temporal features.Considering that air pollution is related to the meteorological conditions of the city,the knowledge acquired from meteorological data is used in the model to enhance PM2.5 prediction performance.The input of the GAT-GRU model consists of PM2.5 concentration data and meteorological data.In order to verify the effectiveness of the proposed GAT-GRU prediction model,this paper designs experiments on real-world datasets compared with other baselines.Experimental results prove that our model achieves excellent performance in PM2.5 concentration prediction.展开更多
基金The National Natural Science Foundation of China under contract Nos T2421002, 623B2071,and 42125601the National Key R&D Program of China under contract No. 2023YFF0805300
文摘Oceanic dissolved oxygen(DO)in the ocean has an indispensable role on supporting biological respiration,maintaining ecological balance and promoting nutrient cycling.According to existing research,the total DO has declined by 2%of the total over the past 50 a,and the tropical Pacific Ocean occupied the largest oxygen minimum zone(OMZ)areas.However,the sparse observation data is limited to understanding the dynamic variation and trend of ocean using traditional interpolation methods.In this study,we applied different machine learning algorithms to fit regression models between measured DO,ocean reanalysis physical variables,and spatiotemporal variables.We demonstrate that extreme gradient boosting(XGBoost)model has the best performance,hereby reconstructing a four-dimensional DO dataset of the tropical Pacific Ocean from 1920 to 2023.The results reveal that XGBoost significantly improves the reconstruction performance in the tropical Pacific Ocean,with a 35.3%reduction in root mean-squared error and a 39.5%decrease in mean absolute error.Additionally,we compare the results with three Coupled Model Intercomparison Project Phase 6(CMIP6)models data to confirm the high accuracy of the 4-dimensional reconstruction.Overall,the OMZ mainly dominates the eastern tropical Pacific Ocean,with a slow expansion.This study used XGBoost to efficiently reconstructing 4-dimensional DO enhancing the understanding of the hypoxic expansion in the tropical Pacific Ocean and we foresee that this approach would be extended to reconstruct more ocean elements.
基金Authors The research project is partially supported by National Natural ScienceFoundation of China under Grant No. 62072015, U19B2039, U1811463National Key R&D Programof China 2018YFB1600903.
文摘PM2.5 concentration prediction is of great significance to environmental protection and human health.Achieving accurate prediction of PM2.5 concentration has become an important research task.However,PM2.5 pollutants can spread in the earth’s atmosphere,causing mutual influence between different cities.To effectively capture the air pollution relationship between cities,this paper proposes a novel spatiotemporal model combining graph attention neural network(GAT)and gated recurrent unit(GRU),named GAT-GRU for PM2.5 concentration prediction.Specifically,GAT is used to learn the spatial dependence of PM2.5 concentration data in different cities,and GRU is to extract the temporal dependence of the long-term data series.The proposed model integrates the learned spatio-temporal dependencies to capture long-term complex spatio-temporal features.Considering that air pollution is related to the meteorological conditions of the city,the knowledge acquired from meteorological data is used in the model to enhance PM2.5 prediction performance.The input of the GAT-GRU model consists of PM2.5 concentration data and meteorological data.In order to verify the effectiveness of the proposed GAT-GRU prediction model,this paper designs experiments on real-world datasets compared with other baselines.Experimental results prove that our model achieves excellent performance in PM2.5 concentration prediction.
