深部煤炭开采会导致微震(MS)事件发生,在威胁工作人员人身安全的同时,也会对基础设施造成破坏,因此定量预测MS事件的时间、能量和位置(Time Energy and location,TEL)对于预防微震事件至关重要。为此,阐述了应用时空图卷积网络(STGCN)...深部煤炭开采会导致微震(MS)事件发生,在威胁工作人员人身安全的同时,也会对基础设施造成破坏,因此定量预测MS事件的时间、能量和位置(Time Energy and location,TEL)对于预防微震事件至关重要。为此,阐述了应用时空图卷积网络(STGCN)预测深部煤炭能源开采诱发的MS事件TEL的方法,通过MS传感器之间的距离确定传感器网络的邻接矩阵,构建传感器网络图,使用GCN提取图中的时空细节,并基于现场MS监测数据对模型进行检验。结果表明,余弦相似度(C)在0.90以上,平均相对误差(MRE)在0.08以下,模型对MS事件的TEL具有较好的预测效果,对于保证深部煤炭开采的安全性和作业效率至关重要。展开更多
Objectives Hand,foot and mouth disease(HFMD)is a widespread infectious disease that causes a significant disease burden on society.To achieve early intervention and to prevent outbreaks of disease,we propose a novel w...Objectives Hand,foot and mouth disease(HFMD)is a widespread infectious disease that causes a significant disease burden on society.To achieve early intervention and to prevent outbreaks of disease,we propose a novel warning model that can accurately predict the incidence of HFMD.Methods We propose a spatial-temporal graph convolutional network(STGCN)that combines spatial factors for surrounding cities with historical incidence over a certain time period to predict the future occurrence of HFMD in Guangdong and Shandong between 2011 and 2019.The 2011-2018 data served as the training and verification set,while data from 2019 served as the prediction set.Six important parameters were selected and verified in this model and the deviation was displayed by the root mean square error and the mean absolute error.Results As the first application using a STGCN for disease forecasting,we succeeded in accurately predicting the incidence of HFMD over a 12-week period at the prefecture level,especially for cities of significant concern.Conclusions This model provides a novel approach for infectious disease prediction and may help health administrative departments implement effective control measures up to 3 months in advance,which may significantly reduce the morbidity associated with HFMD in the future.展开更多
文摘深部煤炭开采会导致微震(MS)事件发生,在威胁工作人员人身安全的同时,也会对基础设施造成破坏,因此定量预测MS事件的时间、能量和位置(Time Energy and location,TEL)对于预防微震事件至关重要。为此,阐述了应用时空图卷积网络(STGCN)预测深部煤炭能源开采诱发的MS事件TEL的方法,通过MS传感器之间的距离确定传感器网络的邻接矩阵,构建传感器网络图,使用GCN提取图中的时空细节,并基于现场MS监测数据对模型进行检验。结果表明,余弦相似度(C)在0.90以上,平均相对误差(MRE)在0.08以下,模型对MS事件的TEL具有较好的预测效果,对于保证深部煤炭开采的安全性和作业效率至关重要。
基金supported by grants from the Key Technologies Research and Development Program from the Ministry of Science and Technology[grant number:ZDZX-2018ZX102001002-003-003]the Beijing Natural Science Foundation[project number:L192014]
文摘Objectives Hand,foot and mouth disease(HFMD)is a widespread infectious disease that causes a significant disease burden on society.To achieve early intervention and to prevent outbreaks of disease,we propose a novel warning model that can accurately predict the incidence of HFMD.Methods We propose a spatial-temporal graph convolutional network(STGCN)that combines spatial factors for surrounding cities with historical incidence over a certain time period to predict the future occurrence of HFMD in Guangdong and Shandong between 2011 and 2019.The 2011-2018 data served as the training and verification set,while data from 2019 served as the prediction set.Six important parameters were selected and verified in this model and the deviation was displayed by the root mean square error and the mean absolute error.Results As the first application using a STGCN for disease forecasting,we succeeded in accurately predicting the incidence of HFMD over a 12-week period at the prefecture level,especially for cities of significant concern.Conclusions This model provides a novel approach for infectious disease prediction and may help health administrative departments implement effective control measures up to 3 months in advance,which may significantly reduce the morbidity associated with HFMD in the future.
