The synchronicity effect between the financial market and online response for time-series forecasting is an important task with wide applications.This study combines data from the Baidu index(BDI),Google trends(GT),an...The synchronicity effect between the financial market and online response for time-series forecasting is an important task with wide applications.This study combines data from the Baidu index(BDI),Google trends(GT),and transfer entropy(TE)to forecast a wide range of futures prices with a focus on China.A forecasting model based on a hybrid gray wolf optimizer(GWO),convolutional neural network(CNN),and long short-term memory(LSTM)is developed.First,Baidu and Google dual-platform search data were selected and constructed as Internetbased consumer price index(ICPI)using principal component analysis.Second,TE is used to quantify the information between online behavior and futures markets.Finally,the effective Internet-based consumer price index(ICPI)and TE are introduced into the GWO-CNN-LSTM model to forecast the daily prices of corn,soybean,polyvinyl chloride(PVC),egg,and rebar futures.The results show that the GWO-CNN-LSTM model has a significant improvement in predicting future prices.Internet-based CPI built on Baidu and Google platforms has a high degree of real-time performance and reduces the platform and language bias of the search data.Our proposed framework can provide predictive decision support for government leaders,market investors,and production activities.展开更多
Methane,a potent greenhouse gas with a global warming potential significantly higher than carbon dioxide,plays a critical role in climate change.Accurate predictions of its future concentrations are vital for understa...Methane,a potent greenhouse gas with a global warming potential significantly higher than carbon dioxide,plays a critical role in climate change.Accurate predictions of its future concentrations are vital for understanding and mitigating its environmental impact.For this reason,this paper presents a comparative analysis of deep learning models—Long Short-Term Memory(LSTM),Gated Recurrent Unit(GRU),CNN(Convolutional Neural Network)-GRU,and CNN-LSTM—for forecasting atmospheric methane concentrations through 2050.Leveraging historical data,each model's performance was evaluated using key metrics,including Mean Absolute Error(MAE)and Nash-Sutcliffe Efficiency(NSE).The results reveal that the CNNLSTM model achieved the highest accuracy,with the lowest MAE of 0.6567 and the highest NSE score of 0.933,indicating its superior capability in capturing the complexities of methane concentration trends.In contrast,the GRU model exhibited the poorest performance,with an MAE of 0.9667 and an NSE score of 0.844.Projections for 2050 indicate significant increases in methane levels,with maximum yearly concentrations expected to reach up to 2199.76 ppb,particularly under the CNN-LSTM model.These findings underscore the potential risks associated with rising methane concentrations,which could exacerbate global warming and its associated impacts.The study highlights the importance of employing advanced predictive models like CNN-LSTM to inform and enhance global climate change mitigation strategies.展开更多
The current and potential impacts of global warming have generated widespread concerns about food security among all sectors of society.Central Asian countries located deep in the interior of Asia with fragile ecologi...The current and potential impacts of global warming have generated widespread concerns about food security among all sectors of society.Central Asian countries located deep in the interior of Asia with fragile ecological environments and lower agricultural technology are particularly more prone to severe threats from climate change.Based on panel data acquired in five Central Asian countries from 1990 to 2019,a C-D-C model was developed to study how climate change affects food security in the region and to predict future trends.The study found that the level of food security has generally increased for these five Central Asian countries over the past 30 years,with Kazakhstan and Tajikistan having the highest and lowest food security levels,respectively.The average annual temperature and precipitation exhibit an inverted U-shaped relationship with the region’s food security,with the most positive effect on the food security of Kazakhstan.Extremely high and low temperatures have significantly affected food security in the studied region,with Turkmenistan experiencing the most significant negative impacts.The number of frost days had no significant effect on food security.An analysis of future climate showed that the temperature and precipitation in Central Asia will continue to increase from 2030 to 2090,which will negatively impact the food security of these countries.It is recommended that the Central Asian countries enhance their understanding of climate risks,strengthen scientific climate research,and develop multiple adaptation strategies in advance.Simultaneously,they are encouraged to consolidate international cooperation,reducing greenhouse gas emissions effectively and maintaining the ability to ensure food security.展开更多
文摘The synchronicity effect between the financial market and online response for time-series forecasting is an important task with wide applications.This study combines data from the Baidu index(BDI),Google trends(GT),and transfer entropy(TE)to forecast a wide range of futures prices with a focus on China.A forecasting model based on a hybrid gray wolf optimizer(GWO),convolutional neural network(CNN),and long short-term memory(LSTM)is developed.First,Baidu and Google dual-platform search data were selected and constructed as Internetbased consumer price index(ICPI)using principal component analysis.Second,TE is used to quantify the information between online behavior and futures markets.Finally,the effective Internet-based consumer price index(ICPI)and TE are introduced into the GWO-CNN-LSTM model to forecast the daily prices of corn,soybean,polyvinyl chloride(PVC),egg,and rebar futures.The results show that the GWO-CNN-LSTM model has a significant improvement in predicting future prices.Internet-based CPI built on Baidu and Google platforms has a high degree of real-time performance and reduces the platform and language bias of the search data.Our proposed framework can provide predictive decision support for government leaders,market investors,and production activities.
文摘Methane,a potent greenhouse gas with a global warming potential significantly higher than carbon dioxide,plays a critical role in climate change.Accurate predictions of its future concentrations are vital for understanding and mitigating its environmental impact.For this reason,this paper presents a comparative analysis of deep learning models—Long Short-Term Memory(LSTM),Gated Recurrent Unit(GRU),CNN(Convolutional Neural Network)-GRU,and CNN-LSTM—for forecasting atmospheric methane concentrations through 2050.Leveraging historical data,each model's performance was evaluated using key metrics,including Mean Absolute Error(MAE)and Nash-Sutcliffe Efficiency(NSE).The results reveal that the CNNLSTM model achieved the highest accuracy,with the lowest MAE of 0.6567 and the highest NSE score of 0.933,indicating its superior capability in capturing the complexities of methane concentration trends.In contrast,the GRU model exhibited the poorest performance,with an MAE of 0.9667 and an NSE score of 0.844.Projections for 2050 indicate significant increases in methane levels,with maximum yearly concentrations expected to reach up to 2199.76 ppb,particularly under the CNN-LSTM model.These findings underscore the potential risks associated with rising methane concentrations,which could exacerbate global warming and its associated impacts.The study highlights the importance of employing advanced predictive models like CNN-LSTM to inform and enhance global climate change mitigation strategies.
基金This work was supported by the Chinese Academy of Sciences(Grant No.xbzg-zdsys-202217)the Shandong Provincial Special Fund(Grant No.LSKJ202203300).
文摘The current and potential impacts of global warming have generated widespread concerns about food security among all sectors of society.Central Asian countries located deep in the interior of Asia with fragile ecological environments and lower agricultural technology are particularly more prone to severe threats from climate change.Based on panel data acquired in five Central Asian countries from 1990 to 2019,a C-D-C model was developed to study how climate change affects food security in the region and to predict future trends.The study found that the level of food security has generally increased for these five Central Asian countries over the past 30 years,with Kazakhstan and Tajikistan having the highest and lowest food security levels,respectively.The average annual temperature and precipitation exhibit an inverted U-shaped relationship with the region’s food security,with the most positive effect on the food security of Kazakhstan.Extremely high and low temperatures have significantly affected food security in the studied region,with Turkmenistan experiencing the most significant negative impacts.The number of frost days had no significant effect on food security.An analysis of future climate showed that the temperature and precipitation in Central Asia will continue to increase from 2030 to 2090,which will negatively impact the food security of these countries.It is recommended that the Central Asian countries enhance their understanding of climate risks,strengthen scientific climate research,and develop multiple adaptation strategies in advance.Simultaneously,they are encouraged to consolidate international cooperation,reducing greenhouse gas emissions effectively and maintaining the ability to ensure food security.
