The stability of reservoir bank slopes during the impoundment period has become a critical issue in the construction and operation of large-scale hydropower projects.A predictive and early warning method for reservoir...The stability of reservoir bank slopes during the impoundment period has become a critical issue in the construction and operation of large-scale hydropower projects.A predictive and early warning method for reservoir bank slopes is proposed,based on slip resistance stability evolution analysis.Using a refined three-dimensional numerical calculation model of the bank slope,the creep damage model is employed for simulation and analysis,enabling the derivation of stress field and strain field evolution from bank slope excavation to the long-term impoundment period.Subsequently,for the stress field of the bank slope at any given moment,the safety factors of the sliding blocks are determined by using the multigrid method and vector sum method.Accordingly,the evolutionary law of the sliding safety factor for the bank slope can be derived.By integrating the long-term stability evolution trend of the slope with specific engineering practices,the safety factors for graded warning can be determined.Based on the time correspondence,the graded warning moment and the deformation warning index for slope measurement points can be determined.In this study,the proposed method is applied to the left bank slope of the Jinping I Hydropower Station.The results indicate that from excavation to June 2022,the left bank slope exhibits a strong correlation with excavation elevation and the number of reservoir water cycles.The initial,maximum,and minimum safety factors are 2.01,3.07,and 1.58,respectively.The deep fracture SL44-1 serves as the primary stress-bearing slip surface of the left bank slope,while the safety margin of the fault f42-9 and lamprophyre X is slightly insufficient.Based on the long-term stability evolution trend of the slope and in accordance with relevant standards,the safety factors for graded warning indicators—K_(w1),K_(w2),K_(w3),and K_(w4)—are determined as 1.350,1.325,1.300,and 1.275,respectively.Correspondingly,the estimated warning times are 12/30/2066,12/30/2084,and 12/30/2120.Accordingly,the deformation graded warning indexes for slope measurement points are established.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
Floods and storm surges pose significant threats to coastal regions worldwide,demanding timely and accurate early warning systems(EWS)for disaster preparedness.Traditional numerical and statistical methods often fall ...Floods and storm surges pose significant threats to coastal regions worldwide,demanding timely and accurate early warning systems(EWS)for disaster preparedness.Traditional numerical and statistical methods often fall short in capturing complex,nonlinear,and real-time environmental dynamics.In recent years,machine learning(ML)and deep learning(DL)techniques have emerged as promising alternatives for enhancing the accuracy,speed,and scalability of EWS.This review critically evaluates the evolution of ML models—such as Artificial Neural Networks(ANN),Convolutional Neural Networks(CNN),and Long Short-Term Memory(LSTM)—in coastal flood prediction,highlighting their architectures,data requirements,performance metrics,and implementation challenges.A unique contribution of this work is the synthesis of real-time deployment challenges including latency,edge-cloud tradeoffs,and policy-level integration,areas often overlooked in prior literature.Furthermore,the review presents a comparative framework of model performance across different geographic and hydrologic settings,offering actionable insights for researchers and practitioners.Limitations of current AI-driven models,such as interpretability,data scarcity,and generalization across regions,are discussed in detail.Finally,the paper outlines future research directions including hybrid modelling,transfer learning,explainable AI,and policy-aware alert systems.By bridging technical performance and operational feasibility,this review aims to guide the development of next-generation intelligent EWS for resilient and adaptive coastal management.展开更多
BACKGROUND:This study aimed to evaluate the discriminatory performance of 11 vital sign-based early warning scores(EWSs)and three shock indices in early sepsis prediction in the emergency department(ED).METHODS:We per...BACKGROUND:This study aimed to evaluate the discriminatory performance of 11 vital sign-based early warning scores(EWSs)and three shock indices in early sepsis prediction in the emergency department(ED).METHODS:We performed a retrospective study on consecutive adult patients with an infection over 3 months in a public ED in Hong Kong.The primary outcome was sepsis(Sepsis-3 definition)within 48 h of ED presentation.Using c-statistics and the DeLong test,we compared 11 EWSs,including the National Early Warning Score 2(NEWS2),Modified Early Warning Score,and Worthing Physiological Scoring System(WPS),etc.,and three shock indices(the shock index[SI],modified shock index[MSI],and diastolic shock index[DSI]),with Systemic Inflammatory Response Syndrome(SIRS)and quick Sequential Organ Failure Assessment(qSOFA)in predicting the primary outcome,intensive care unit admission,and mortality at different time points.RESULTS:We analyzed 601 patients,of whom 166(27.6%)developed sepsis.NEWS2 had the highest point estimate(area under the receiver operating characteristic curve[AUROC]0.75,95%CI 0.70-0.79)and was significantly better than SIRS,qSOFA,other EWSs and shock indices,except WPS,at predicting the primary outcome.However,the pooled sensitivity and specificity of NEWS2≥5 for the prediction of sepsis were 0.45(95%CI 0.37-0.52)and 0.88(95%CI 0.85-0.91),respectively.The discriminatory performance of all EWSs and shock indices declined when used to predict mortality at a more remote time point.CONCLUSION:NEWS2 compared favorably with other EWSs and shock indices in early sepsis prediction but its low sensitivity at the usual cut-off point requires further modification for sepsis screening.展开更多
Rangelands dominate arid and semi-arid lands of the Greater Horn of Africa(GHA)region,whereby pastoralism being the primary source of livelihood.The pastoral livelihood is affected by the seasonal variability of pastu...Rangelands dominate arid and semi-arid lands of the Greater Horn of Africa(GHA)region,whereby pastoralism being the primary source of livelihood.The pastoral livelihood is affected by the seasonal variability of pasture and water resources.This research sought to design a grid-based forage monitoring and prediction model for the cross-border areas of the GHA region.A technique known as Geographically Weighted Regression was used in developing the model with monthly rainfall,temperature,soil moisture,and the Normalized Difference Vegetation Index(NDVI).Rainfall and soil moisture had a high correlation with NDVI,and thus formed the model development parameters.The model performed well in predicting the available forage biomass at each grid-cell with March-May and October-December seasons depicting a similar pattern but with a different magnitude in ton/ha.The output is critical for actionable early warning over the GHA region’s rangeland areas.It is expected that this mode can be used operationally for forage monitoring and prediction over the eastern Africa region and further guide the regional,national,sub-national actors and policymakers on issuing advisories before the season.展开更多
Purpose–Using the strong motion data ofK-net in Japan,the continuous magnitude prediction method based on support vector machine(SVM)was studied.Design/methodology/approach–In the range of 0.5–10.0 s after the P-wa...Purpose–Using the strong motion data ofK-net in Japan,the continuous magnitude prediction method based on support vector machine(SVM)was studied.Design/methodology/approach–In the range of 0.5–10.0 s after the P-wave arrival,the prediction time window was established at an interval of 0.5 s.12 P-wave characteristic parameters were selected as the model input parameters to construct the earthquake early warning(EEW)magnitude prediction model(SVM-HRM)for high-speed railway based on SVM.Findings–The magnitude prediction results of the SVM-HRM model were compared with the traditional magnitude prediction model and the high-speed railway EEW current norm.Results show that at the 3.0 s time window,themagnitude prediction error of the SVM-HRMmodel is obviously smaller than that of the traditionalτc method and Pd method.The overestimation of small earthquakes is obviously improved,and the construction of the model is not affected by epicenter distance,so it has generalization performance.For earthquake events with themagnitude range of 3–5,the single station realization rate of the SVM-HRMmodel reaches 95%at 0.5 s after the arrival of P-wave,which is better than the first alarm realization rate norm required by“The TestMethod of EEW andMonitoring Systemfor High-Speed Railway.”For earthquake eventswithmagnitudes ranging from3 to 5,5 to 7 and 7 to 8,the single station realization rate of the SVM-HRM model is at 0.5 s,1.5 s and 0.5 s after the P-wave arrival,respectively,which is better than the realization rate norm of multiple stations.Originality/value–At the latest,1.5 s after the P-wave arrival,the SVM-HRM model can issue the first earthquake alarm that meets the norm of magnitude prediction realization rate,which meets the accuracy and continuity requirements of high-speed railway EEW magnitude prediction.展开更多
By means of analysing the historical data of flood-drought grade series in the past 2000 years(A.D.0-1900),especially in the last 5000 years (1470-1900) , this paper revealed the spatial-temporaldistribution features ...By means of analysing the historical data of flood-drought grade series in the past 2000 years(A.D.0-1900),especially in the last 5000 years (1470-1900) , this paper revealed the spatial-temporaldistribution features of severe flood and drought in Yellow River Valley. Statistical methods of varianceanalysis, probability transition and the principles of scale correspondence were employed tocomprehensively predicate 90's tendency of severe flood and drought in the Yellow River Valley. In addi-tion, this paper pointed out the possible breaching dikes, sectors and the flooding ranges by future's se-vere flood, meanwhile estimating the associated economic losses and impact to environment.展开更多
The bolt support quality of coal roadways is one of the important factors for the efficiency and security of coal production. By means of a self-developed technique and equipment of random non-destructive testing, non...The bolt support quality of coal roadways is one of the important factors for the efficiency and security of coal production. By means of a self-developed technique and equipment of random non-destructive testing, non-destructive detection and pre-warning analysis on the quality of bolt support in deep roadways of mining districts were performed in a number of mining areas. The measured data were obtained in the detection instances of abnormal in-situ stress and support invalidation etc. The corresponding relation between axial bolt load variation and roadway surrounding rock deformation and stability was summarized in different mining service stages. Pre-warning technology of roadway surrounding rock stability is proposed based on the detection of axial bolt load. Meanwhile, pre-warning indicators of axial bolt load in different mining service stages are offered and some successful pre-warning cases are also illustrated.The research results show that the change rules of axial bolt load in different mining service stages are quite similar in different mining areas. The change of axial bolt load is in accord with the adjustment of surrounding rock stress, which can consequently reflect the deformation and stability state of roadway surrounding rock. Through the detection of axial bolt load in different sections of roadways, the status of real-time bolt support quality can be reflected; meanwhile, the rationality of bolt support design can be evaluated which provides reference for bolting parameters optimization.展开更多
This paper presents a hybrid deep learning framework for earthquake early warning(EEW)that leverages front-site observations to predict target-site spectral characteristics-specifically Fourier amplitude spectra(FAS)a...This paper presents a hybrid deep learning framework for earthquake early warning(EEW)that leverages front-site observations to predict target-site spectral characteristics-specifically Fourier amplitude spectra(FAS)and 5%damped pseudo-velocity response spectra(pSᵥ)in real time.In its current form,the framework is site-specific,as the front-site/target-site pairs used for training and evaluation are fixed.By integrating a convolutional neural network(CNN)front end with a long short-term memory(LSTM)sequence model,our approach captures both spatial frequency content and temporal correlations without requiring explicit source,path,or detailed geological inputs.Trained on a diverse corpus of historic accelerograms,the CNN-LSTM network learns cross-spectral and multicomponent dependencies and region-specific site effects,yielding rapid,physically consis-tent spectral estimates.We evaluate its performance across five case studies,demonstrating that our model not only reduces prediction error relative to established GMPEs for both FAS and pSᵥ,but also preserves spectral shape and cross-period correlations essential for reliable EEW.The developed technique is capable of estimating target-sites through very low latency inference,providing real-time capabilities.Compared to traditional GMPE-based warnings,our data-driven method achieves substantially faster issuance and improved shaking intensity forecasts.We conclude by outlining avenues for embedding sites’distance and physics-informed constraints,expanding observation datasets,and enhancing model usefulness in seismic demand prediction which are key steps toward rapid EEW systems.展开更多
As one of the provinces of highest economic growth in coastal China,Zhejiang Province is experiencing serious geological disasters during the past development of economy.The main kinds of geo-hazards include landslide...As one of the provinces of highest economic growth in coastal China,Zhejiang Province is experiencing serious geological disasters during the past development of economy.The main kinds of geo-hazards include landslides,rock falls and debris-flows in Zhejiang Province,which are mainly induced by intensive rainfall during typhoon season or by long-term rainfall from May to June every year.展开更多
There has been rapid development of high-speed railway lines, especially passenger-dedicated railway lines, in China. Trains are traveling at speeds exceeding 250 km per hour and they require highly smooth tracks to e...There has been rapid development of high-speed railway lines, especially passenger-dedicated railway lines, in China. Trains are traveling at speeds exceeding 250 km per hour and they require highly smooth tracks to ensure safety. However, there have been no in-depth studies on the early warning of the settlement of high-speed railway lines in China or abroad. Most methods use a simple model based on data processing and decision rules. The core issues of early warning lie in the science and rationality of decision rules. The present paper therefore investigates novel and critical indexes for the warning of settlement under high-speed railway lines according to existing norms and field data, and several essential indexes of deformation warning are suggested through theoretical and experimental analysis.展开更多
This paper summarized theory discussion,main research methods,contents,empirical and engineering researches of farmland prewarning in China.It stated that future researches of farmland pre-warning in China will focus ...This paper summarized theory discussion,main research methods,contents,empirical and engineering researches of farmland prewarning in China.It stated that future researches of farmland pre-warning in China will focus on deepening application of farmland security prewarning models,revealing mechanism of changes in different farmland resources,establishing pre-warning models suitable for research areas,accurate evaluation and prediction of farmland security,and exploring establishing and improving farmland security monitoring system and operating mechanism of all levels.展开更多
滑坡预警预报是滑坡研究的热点和难点。速度倒数模型的简捷性和有效性使之成为广泛使用的临滑预报模型。滑坡变形加速开始点(Onset of Acceleration)直接影响到速度倒数模型的预报精度。本文基于经济学领域广泛使用的指数移动平均线,提...滑坡预警预报是滑坡研究的热点和难点。速度倒数模型的简捷性和有效性使之成为广泛使用的临滑预报模型。滑坡变形加速开始点(Onset of Acceleration)直接影响到速度倒数模型的预报精度。本文基于经济学领域广泛使用的指数移动平均线,提出了准确识别滑坡变形加速开始点的方法:(1)将滑坡速度绝对值化;(2)定义趋势变化指数ω,利用滑动时间窗口法,识别滑坡加速趋势区;(3)对加速趋势区进行速度倒数线性拟合,根据线性拟合的相关性系数,识别滑坡加速变形开始点。在此基础上,以云南省区布嘎渐变型滑坡为例,对模型识别出的OOA点准确性进行了验证,结果表明:利用本文提出的方法,可准确识别渐变型滑坡的OOA点,利用识别的OOA点对后续数据进行线性回归,其相关性系数在0.8以上,预测误差在4 d以下,显示出较好的预测结果。展开更多
基金supported by the National Natural Science Foundation of China(Grant No.41961134032).
文摘The stability of reservoir bank slopes during the impoundment period has become a critical issue in the construction and operation of large-scale hydropower projects.A predictive and early warning method for reservoir bank slopes is proposed,based on slip resistance stability evolution analysis.Using a refined three-dimensional numerical calculation model of the bank slope,the creep damage model is employed for simulation and analysis,enabling the derivation of stress field and strain field evolution from bank slope excavation to the long-term impoundment period.Subsequently,for the stress field of the bank slope at any given moment,the safety factors of the sliding blocks are determined by using the multigrid method and vector sum method.Accordingly,the evolutionary law of the sliding safety factor for the bank slope can be derived.By integrating the long-term stability evolution trend of the slope with specific engineering practices,the safety factors for graded warning can be determined.Based on the time correspondence,the graded warning moment and the deformation warning index for slope measurement points can be determined.In this study,the proposed method is applied to the left bank slope of the Jinping I Hydropower Station.The results indicate that from excavation to June 2022,the left bank slope exhibits a strong correlation with excavation elevation and the number of reservoir water cycles.The initial,maximum,and minimum safety factors are 2.01,3.07,and 1.58,respectively.The deep fracture SL44-1 serves as the primary stress-bearing slip surface of the left bank slope,while the safety margin of the fault f42-9 and lamprophyre X is slightly insufficient.Based on the long-term stability evolution trend of the slope and in accordance with relevant standards,the safety factors for graded warning indicators—K_(w1),K_(w2),K_(w3),and K_(w4)—are determined as 1.350,1.325,1.300,and 1.275,respectively.Correspondingly,the estimated warning times are 12/30/2066,12/30/2084,and 12/30/2120.Accordingly,the deformation graded warning indexes for slope measurement points are established.
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.
文摘Floods and storm surges pose significant threats to coastal regions worldwide,demanding timely and accurate early warning systems(EWS)for disaster preparedness.Traditional numerical and statistical methods often fall short in capturing complex,nonlinear,and real-time environmental dynamics.In recent years,machine learning(ML)and deep learning(DL)techniques have emerged as promising alternatives for enhancing the accuracy,speed,and scalability of EWS.This review critically evaluates the evolution of ML models—such as Artificial Neural Networks(ANN),Convolutional Neural Networks(CNN),and Long Short-Term Memory(LSTM)—in coastal flood prediction,highlighting their architectures,data requirements,performance metrics,and implementation challenges.A unique contribution of this work is the synthesis of real-time deployment challenges including latency,edge-cloud tradeoffs,and policy-level integration,areas often overlooked in prior literature.Furthermore,the review presents a comparative framework of model performance across different geographic and hydrologic settings,offering actionable insights for researchers and practitioners.Limitations of current AI-driven models,such as interpretability,data scarcity,and generalization across regions,are discussed in detail.Finally,the paper outlines future research directions including hybrid modelling,transfer learning,explainable AI,and policy-aware alert systems.By bridging technical performance and operational feasibility,this review aims to guide the development of next-generation intelligent EWS for resilient and adaptive coastal management.
基金supported by the Health and Medical Research Fund of the Food and Health Bureau of the Hong Kong Special Administrative Region(Project No.19201161)Seed Fund from the University of Hong Kong.
文摘BACKGROUND:This study aimed to evaluate the discriminatory performance of 11 vital sign-based early warning scores(EWSs)and three shock indices in early sepsis prediction in the emergency department(ED).METHODS:We performed a retrospective study on consecutive adult patients with an infection over 3 months in a public ED in Hong Kong.The primary outcome was sepsis(Sepsis-3 definition)within 48 h of ED presentation.Using c-statistics and the DeLong test,we compared 11 EWSs,including the National Early Warning Score 2(NEWS2),Modified Early Warning Score,and Worthing Physiological Scoring System(WPS),etc.,and three shock indices(the shock index[SI],modified shock index[MSI],and diastolic shock index[DSI]),with Systemic Inflammatory Response Syndrome(SIRS)and quick Sequential Organ Failure Assessment(qSOFA)in predicting the primary outcome,intensive care unit admission,and mortality at different time points.RESULTS:We analyzed 601 patients,of whom 166(27.6%)developed sepsis.NEWS2 had the highest point estimate(area under the receiver operating characteristic curve[AUROC]0.75,95%CI 0.70-0.79)and was significantly better than SIRS,qSOFA,other EWSs and shock indices,except WPS,at predicting the primary outcome.However,the pooled sensitivity and specificity of NEWS2≥5 for the prediction of sepsis were 0.45(95%CI 0.37-0.52)and 0.88(95%CI 0.85-0.91),respectively.The discriminatory performance of all EWSs and shock indices declined when used to predict mortality at a more remote time point.CONCLUSION:NEWS2 compared favorably with other EWSs and shock indices in early sepsis prediction but its low sensitivity at the usual cut-off point requires further modification for sepsis screening.
基金supported by the World Bank International Development Association(IDA)Grant No.:H9190,under the Regional Pastoral Livelihoods Resilience Project(RPLRP).
文摘Rangelands dominate arid and semi-arid lands of the Greater Horn of Africa(GHA)region,whereby pastoralism being the primary source of livelihood.The pastoral livelihood is affected by the seasonal variability of pasture and water resources.This research sought to design a grid-based forage monitoring and prediction model for the cross-border areas of the GHA region.A technique known as Geographically Weighted Regression was used in developing the model with monthly rainfall,temperature,soil moisture,and the Normalized Difference Vegetation Index(NDVI).Rainfall and soil moisture had a high correlation with NDVI,and thus formed the model development parameters.The model performed well in predicting the available forage biomass at each grid-cell with March-May and October-December seasons depicting a similar pattern but with a different magnitude in ton/ha.The output is critical for actionable early warning over the GHA region’s rangeland areas.It is expected that this mode can be used operationally for forage monitoring and prediction over the eastern Africa region and further guide the regional,national,sub-national actors and policymakers on issuing advisories before the season.
基金supported by the National Natural Science Foundation of China(U2039209,U1534202,51408564)Natural Science Foundation of Heilongjiang Province(LH2021E119)the National Key Research and Development Program of China(2018YFC1504003).
文摘Purpose–Using the strong motion data ofK-net in Japan,the continuous magnitude prediction method based on support vector machine(SVM)was studied.Design/methodology/approach–In the range of 0.5–10.0 s after the P-wave arrival,the prediction time window was established at an interval of 0.5 s.12 P-wave characteristic parameters were selected as the model input parameters to construct the earthquake early warning(EEW)magnitude prediction model(SVM-HRM)for high-speed railway based on SVM.Findings–The magnitude prediction results of the SVM-HRM model were compared with the traditional magnitude prediction model and the high-speed railway EEW current norm.Results show that at the 3.0 s time window,themagnitude prediction error of the SVM-HRMmodel is obviously smaller than that of the traditionalτc method and Pd method.The overestimation of small earthquakes is obviously improved,and the construction of the model is not affected by epicenter distance,so it has generalization performance.For earthquake events with themagnitude range of 3–5,the single station realization rate of the SVM-HRMmodel reaches 95%at 0.5 s after the arrival of P-wave,which is better than the first alarm realization rate norm required by“The TestMethod of EEW andMonitoring Systemfor High-Speed Railway.”For earthquake eventswithmagnitudes ranging from3 to 5,5 to 7 and 7 to 8,the single station realization rate of the SVM-HRM model is at 0.5 s,1.5 s and 0.5 s after the P-wave arrival,respectively,which is better than the realization rate norm of multiple stations.Originality/value–At the latest,1.5 s after the P-wave arrival,the SVM-HRM model can issue the first earthquake alarm that meets the norm of magnitude prediction realization rate,which meets the accuracy and continuity requirements of high-speed railway EEW magnitude prediction.
文摘By means of analysing the historical data of flood-drought grade series in the past 2000 years(A.D.0-1900),especially in the last 5000 years (1470-1900) , this paper revealed the spatial-temporaldistribution features of severe flood and drought in Yellow River Valley. Statistical methods of varianceanalysis, probability transition and the principles of scale correspondence were employed tocomprehensively predicate 90's tendency of severe flood and drought in the Yellow River Valley. In addi-tion, this paper pointed out the possible breaching dikes, sectors and the flooding ranges by future's se-vere flood, meanwhile estimating the associated economic losses and impact to environment.
基金the State Key Research Development Program of China(No.2016YFC0600705)the Fundamental Research Funds for the Central Universities(No.2015XKZD06)+1 种基金the National Natural Science Foundation of China(Nos.51227003,51404250,51504243,51474215,51404262 and 51323004)the Natural Science Foundation of Jiangsu Province,China(Nos.BK20150191 and BK20140213)
文摘The bolt support quality of coal roadways is one of the important factors for the efficiency and security of coal production. By means of a self-developed technique and equipment of random non-destructive testing, non-destructive detection and pre-warning analysis on the quality of bolt support in deep roadways of mining districts were performed in a number of mining areas. The measured data were obtained in the detection instances of abnormal in-situ stress and support invalidation etc. The corresponding relation between axial bolt load variation and roadway surrounding rock deformation and stability was summarized in different mining service stages. Pre-warning technology of roadway surrounding rock stability is proposed based on the detection of axial bolt load. Meanwhile, pre-warning indicators of axial bolt load in different mining service stages are offered and some successful pre-warning cases are also illustrated.The research results show that the change rules of axial bolt load in different mining service stages are quite similar in different mining areas. The change of axial bolt load is in accord with the adjustment of surrounding rock stress, which can consequently reflect the deformation and stability state of roadway surrounding rock. Through the detection of axial bolt load in different sections of roadways, the status of real-time bolt support quality can be reflected; meanwhile, the rationality of bolt support design can be evaluated which provides reference for bolting parameters optimization.
基金supported by JSPS KAKENHI,Japan,Grant number 19K22002.
文摘This paper presents a hybrid deep learning framework for earthquake early warning(EEW)that leverages front-site observations to predict target-site spectral characteristics-specifically Fourier amplitude spectra(FAS)and 5%damped pseudo-velocity response spectra(pSᵥ)in real time.In its current form,the framework is site-specific,as the front-site/target-site pairs used for training and evaluation are fixed.By integrating a convolutional neural network(CNN)front end with a long short-term memory(LSTM)sequence model,our approach captures both spatial frequency content and temporal correlations without requiring explicit source,path,or detailed geological inputs.Trained on a diverse corpus of historic accelerograms,the CNN-LSTM network learns cross-spectral and multicomponent dependencies and region-specific site effects,yielding rapid,physically consis-tent spectral estimates.We evaluate its performance across five case studies,demonstrating that our model not only reduces prediction error relative to established GMPEs for both FAS and pSᵥ,but also preserves spectral shape and cross-period correlations essential for reliable EEW.The developed technique is capable of estimating target-sites through very low latency inference,providing real-time capabilities.Compared to traditional GMPE-based warnings,our data-driven method achieves substantially faster issuance and improved shaking intensity forecasts.We conclude by outlining avenues for embedding sites’distance and physics-informed constraints,expanding observation datasets,and enhancing model usefulness in seismic demand prediction which are key steps toward rapid EEW systems.
文摘As one of the provinces of highest economic growth in coastal China,Zhejiang Province is experiencing serious geological disasters during the past development of economy.The main kinds of geo-hazards include landslides,rock falls and debris-flows in Zhejiang Province,which are mainly induced by intensive rainfall during typhoon season or by long-term rainfall from May to June every year.
文摘There has been rapid development of high-speed railway lines, especially passenger-dedicated railway lines, in China. Trains are traveling at speeds exceeding 250 km per hour and they require highly smooth tracks to ensure safety. However, there have been no in-depth studies on the early warning of the settlement of high-speed railway lines in China or abroad. Most methods use a simple model based on data processing and decision rules. The core issues of early warning lie in the science and rationality of decision rules. The present paper therefore investigates novel and critical indexes for the warning of settlement under high-speed railway lines according to existing norms and field data, and several essential indexes of deformation warning are suggested through theoretical and experimental analysis.
文摘This paper summarized theory discussion,main research methods,contents,empirical and engineering researches of farmland prewarning in China.It stated that future researches of farmland pre-warning in China will focus on deepening application of farmland security prewarning models,revealing mechanism of changes in different farmland resources,establishing pre-warning models suitable for research areas,accurate evaluation and prediction of farmland security,and exploring establishing and improving farmland security monitoring system and operating mechanism of all levels.
文摘滑坡预警预报是滑坡研究的热点和难点。速度倒数模型的简捷性和有效性使之成为广泛使用的临滑预报模型。滑坡变形加速开始点(Onset of Acceleration)直接影响到速度倒数模型的预报精度。本文基于经济学领域广泛使用的指数移动平均线,提出了准确识别滑坡变形加速开始点的方法:(1)将滑坡速度绝对值化;(2)定义趋势变化指数ω,利用滑动时间窗口法,识别滑坡加速趋势区;(3)对加速趋势区进行速度倒数线性拟合,根据线性拟合的相关性系数,识别滑坡加速变形开始点。在此基础上,以云南省区布嘎渐变型滑坡为例,对模型识别出的OOA点准确性进行了验证,结果表明:利用本文提出的方法,可准确识别渐变型滑坡的OOA点,利用识别的OOA点对后续数据进行线性回归,其相关性系数在0.8以上,预测误差在4 d以下,显示出较好的预测结果。
