Bivariate statistical analysis of data-driven approaches is widely used for landslide susceptibility assessment, and the frequency ratio(FR) method is one of the most popular. However, the results of such assessments ...Bivariate statistical analysis of data-driven approaches is widely used for landslide susceptibility assessment, and the frequency ratio(FR) method is one of the most popular. However, the results of such assessments are dominated by the number of classes and bounds of landslide-related causative factors, and the optimal assessment is unknown. This paper optimizes the frequency ratio method as an example of bivariate statistical analysis for landslide susceptibility mapping based on a case study of the Caiyuan Basin, a region with frequent landslides, which is located in the southeast coastal mountainous area of China. A landslide inventory map containing a total of 1425 landslides(polygons) was produced, in which 70% of the landslides were selected for training purposes, and the remaining were used for validationpurposes. All datasets were resampled to the same 5 m × 5 m/pixel resolution. The receiver operating characteristic(ROC) curves of the susceptibility maps were obtained based on different combinations of dominating parameters, and the maximum value of the areas under the ROC curves(AUCs) as well as the corresponding optimal parameter was identified with an automatic searching algorithm. The results showed that the landslide susceptibility maps obtained using optimal parameters displayed a significant increase in the prediction AUC compared with those values obtained using stochastic parameters. The results also showed that one parameter named bin width has a dominant influence on the optimum. In practice, this paper is expected to benefit the assessment of landslide susceptibility by providing an easy-to-use tool. The proposed automatic approach provides a way to optimize the frequency ratio method or other bivariate statistical methods, which can furtherfacilitate comparisons and choices between different methods for landslide susceptibility assessment.展开更多
The ＂4.20＂ Lushan earthquake in Sichuan province, China has induced a large amount of geological hazards and produced abundant loose materials which are prone to post-earthquake rainfall- triggered landslides. A det...The ＂4.20＂ Lushan earthquake in Sichuan province, China has induced a large amount of geological hazards and produced abundant loose materials which are prone to post-earthquake rainfall- triggered landslides. A detailed landslide inventory was acquired through post-earthquake emergent field investigation and high resolution remote sensing interpretation. The rainfall analysis was conducted using historical rainfall records during the period from 1951 to 2010. Results indicate that the average annual rainfall distribution is heterogeneous and the largest average annual rainfall occurs in Yucheng district. The Stability Index MAPping （SINMAP） model was adopted to assess and analyze the post- earthquake slope stability under different rainfall scenarios （light rainfall, moderate rainfall, heavy rainfall, and rainstorm）. The model parameters were calibrated to reflect the significant influence of strong earthquakes on geological settings. The slope stability maps triggered by different rainfall scenarios were produced at a regional scale. The effect of different rainfall conditions on the slope stability is discussed. The expanding trend of the unstable area was quantitatively assessed with the different critical rainfall intensity. They provide a new insight into the spatial distribution and characteristics of post- earthquake rainfall-triggered landslides in the Lushan seismic area. An increase of rainfall intensity results in a significant increase heterogeneous distribution strongly correlated with of unstable area. The of slope instability is the distribution of earthquake intensity in spite of different rainfall conditions. The results suggest that the both seismic intensity and rainfall are two crucial factors for post- earthquake slope stability. This study provides important references for landslide prevention and mitigation in the Lushan area after earthquake.展开更多
Landslide warning models are important for mitigating landslide risks.The rainfall threshold model is the most widely used early warning model for predicting rainfall-triggered landslides.Recently,the rainfall thresho...Landslide warning models are important for mitigating landslide risks.The rainfall threshold model is the most widely used early warning model for predicting rainfall-triggered landslides.Recently,the rainfall threshold model has been coupled with the landslide susceptibility(LS)model to improve the accuracy of early warnings in the spatial domain.Existing coupled models,designed based on a matrix including predefined rainfall thresholds and susceptibility levels,have been used to determine the warning level.These predefined classifications inevitably have subjective rainfall thresholds and susceptibility levels,thus affecting the probability distribution information and eventually influencing the reliability of the produced early warning.In this paper,we propose a novel landslide warning model in which the temporal and spatial probabilities of landslides are coupled without predefining the classified levels.The temporal probability of landslides is obtained from the probability distribution of rainfall intensities that triggered historical landslides.The spatial probability of landslides is then obtained from the susceptibility probability distribution.A case study shows that the proposed probability-coupled model can successfully provide hourly warning results before the occurrence of a landslide.Although all three models successfully predicted the landslide,the probability-coupled model produced a warning zone comprising the fewest grid cells.Quantitatively,the probabilitycoupled model produced only 39 grid cells in the warning zone,while the rainfall threshold model and the matrix-coupled model produced warning zones including 81 and 49 grid cells,respectively.The proposed model is also applicable to other regions affected by rainfall-induced landslides and is thus expected to be useful for practical landslide risk management.展开更多
An analytical solution is presented for the electromagnetic scattering from an infinite-length metallic carbon nanotube and a carbon nanotube bundle. The scattering field and scattering cross section are predicted usi...An analytical solution is presented for the electromagnetic scattering from an infinite-length metallic carbon nanotube and a carbon nanotube bundle. The scattering field and scattering cross section are predicted using a modal technique based on a Bessel and Hankel function for the electric line source and a quantum conductance function for the carbon nanotube. For the particular case of an isolated armchair （10, 10） carbon nanotube, the scattered field predicted from this technique is in excellent agreement with the measured result. Furthermore, the analysis indicates that the scattering pattern of an isolated carbon nanotube differs from that of the carbon nanotube bundle of identical index （m, n） metallic carbon nanotubes.展开更多
This paper extends the resource drag studies by empirically investigating how spatial factors affect the regional economic growth. Using spatial panel econometric models, this paper estimates the dragging effect of en...This paper extends the resource drag studies by empirically investigating how spatial factors affect the regional economic growth. Using spatial panel econometric models, this paper estimates the dragging effect of energy resources of the Yangtze River Delta metropolitan areas. We fi nd that the growth drag of energy in the Yangtze River Delta is about 6% on average, which means that energy constraints decrease the economic growth by 6% annually, higher than the national level that has been previously measured in the literature. This result has taken into account the impact of neighboring cities' economic development, so as to obtain a more accurate estimate. Based on these measurement results, we propose some policy recommendations.展开更多
Using data of prefecture-level cities in Shandong province from 2004 to 2012 and the Stochastic Impacts by Regression on Population,Affluence,and Technology framework,this paper builds the geographically weighted regr...Using data of prefecture-level cities in Shandong province from 2004 to 2012 and the Stochastic Impacts by Regression on Population,Affluence,and Technology framework,this paper builds the geographically weighted regression(GWR)model of carbon emissions and its influencing factors.Unlike traditional econometric methods,such as ordinary least squares(OLS),the spatial econometrics models of spatial lag model(SLM)and spatial error model(SEM)are often estimate parameters constantly,namely these methods just estimate parameters in "average" or "globally" and can not reflect the parameters' nonstationary in different spaces.So in this paper,the influencing factors of carbon emissions are estimated by GWR,and the influencing factors of carbon emissions are estimated to be more realistic.The results indicates that the local GWR model is better than OLS,SLM and SEM,and there is spatial heterogeneity between the factors involved in economic growth,population status,industrial structure,energy price and carbon emissions across cities in Shandong province.展开更多
基金funded by the National Natural Science Foundation of China(Grant NO.41525010,41807291,41421001,41790443 and 41701458)the Strategic Priority Research Program of Chinese Academy of Sciences(CAS)(Grant NO.XDA23090301 and XDA19040304)+1 种基金the Key Research Program of Frontier Sciences of Chinese Academy of Sciences(CAS)(Grant NO.QYZDY-SSW-DQC019)the Second Tibetan Plateau Scientific Expedition and Research(STEP)program(Grant No.2019QZKK0904)
文摘Bivariate statistical analysis of data-driven approaches is widely used for landslide susceptibility assessment, and the frequency ratio(FR) method is one of the most popular. However, the results of such assessments are dominated by the number of classes and bounds of landslide-related causative factors, and the optimal assessment is unknown. This paper optimizes the frequency ratio method as an example of bivariate statistical analysis for landslide susceptibility mapping based on a case study of the Caiyuan Basin, a region with frequent landslides, which is located in the southeast coastal mountainous area of China. A landslide inventory map containing a total of 1425 landslides(polygons) was produced, in which 70% of the landslides were selected for training purposes, and the remaining were used for validationpurposes. All datasets were resampled to the same 5 m × 5 m/pixel resolution. The receiver operating characteristic(ROC) curves of the susceptibility maps were obtained based on different combinations of dominating parameters, and the maximum value of the areas under the ROC curves(AUCs) as well as the corresponding optimal parameter was identified with an automatic searching algorithm. The results showed that the landslide susceptibility maps obtained using optimal parameters displayed a significant increase in the prediction AUC compared with those values obtained using stochastic parameters. The results also showed that one parameter named bin width has a dominant influence on the optimum. In practice, this paper is expected to benefit the assessment of landslide susceptibility by providing an easy-to-use tool. The proposed automatic approach provides a way to optimize the frequency ratio method or other bivariate statistical methods, which can furtherfacilitate comparisons and choices between different methods for landslide susceptibility assessment.
基金supported by the Project of the 12th Five-year National Sci-Tech Support Plan of China (2011BAK12B09)the National Science Foundation of China (41072241)+1 种基金the One Hundred Talents Program of Chinese Academy of Sciences (A1055)the China Geological Survey Project (12120113038000)
文摘The ＂4.20＂ Lushan earthquake in Sichuan province, China has induced a large amount of geological hazards and produced abundant loose materials which are prone to post-earthquake rainfall- triggered landslides. A detailed landslide inventory was acquired through post-earthquake emergent field investigation and high resolution remote sensing interpretation. The rainfall analysis was conducted using historical rainfall records during the period from 1951 to 2010. Results indicate that the average annual rainfall distribution is heterogeneous and the largest average annual rainfall occurs in Yucheng district. The Stability Index MAPping （SINMAP） model was adopted to assess and analyze the post- earthquake slope stability under different rainfall scenarios （light rainfall, moderate rainfall, heavy rainfall, and rainstorm）. The model parameters were calibrated to reflect the significant influence of strong earthquakes on geological settings. The slope stability maps triggered by different rainfall scenarios were produced at a regional scale. The effect of different rainfall conditions on the slope stability is discussed. The expanding trend of the unstable area was quantitatively assessed with the different critical rainfall intensity. They provide a new insight into the spatial distribution and characteristics of post- earthquake rainfall-triggered landslides in the Lushan seismic area. An increase of rainfall intensity results in a significant increase heterogeneous distribution strongly correlated with of unstable area. The of slope instability is the distribution of earthquake intensity in spite of different rainfall conditions. The results suggest that the both seismic intensity and rainfall are two crucial factors for post- earthquake slope stability. This study provides important references for landslide prevention and mitigation in the Lushan area after earthquake.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA23090301)the National Natural Science Foundation of China(Grant No.42041006 and 41927806)the Fundamental Research Funds for the Central Universities,CHD(Grant No.300102262901)。
文摘Landslide warning models are important for mitigating landslide risks.The rainfall threshold model is the most widely used early warning model for predicting rainfall-triggered landslides.Recently,the rainfall threshold model has been coupled with the landslide susceptibility(LS)model to improve the accuracy of early warnings in the spatial domain.Existing coupled models,designed based on a matrix including predefined rainfall thresholds and susceptibility levels,have been used to determine the warning level.These predefined classifications inevitably have subjective rainfall thresholds and susceptibility levels,thus affecting the probability distribution information and eventually influencing the reliability of the produced early warning.In this paper,we propose a novel landslide warning model in which the temporal and spatial probabilities of landslides are coupled without predefining the classified levels.The temporal probability of landslides is obtained from the probability distribution of rainfall intensities that triggered historical landslides.The spatial probability of landslides is then obtained from the susceptibility probability distribution.A case study shows that the proposed probability-coupled model can successfully provide hourly warning results before the occurrence of a landslide.Although all three models successfully predicted the landslide,the probability-coupled model produced a warning zone comprising the fewest grid cells.Quantitatively,the probabilitycoupled model produced only 39 grid cells in the warning zone,while the rainfall threshold model and the matrix-coupled model produced warning zones including 81 and 49 grid cells,respectively.The proposed model is also applicable to other regions affected by rainfall-induced landslides and is thus expected to be useful for practical landslide risk management.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.60871073,60971064,and 51005001)the Open Program of the State Key Laboratory of Millimeter Wave of China(Grant No.K201006)+1 种基金the Special Funds for the Technological and Innovative Talent of Harbin City,China(Grant No.2010RFXXG010)the Youth Foundation of Harbin University of Science and Technology,China(Grant Nos.2009YF025 and 2009YF024)
文摘An analytical solution is presented for the electromagnetic scattering from an infinite-length metallic carbon nanotube and a carbon nanotube bundle. The scattering field and scattering cross section are predicted using a modal technique based on a Bessel and Hankel function for the electric line source and a quantum conductance function for the carbon nanotube. For the particular case of an isolated armchair （10, 10） carbon nanotube, the scattered field predicted from this technique is in excellent agreement with the measured result. Furthermore, the analysis indicates that the scattering pattern of an isolated carbon nanotube differs from that of the carbon nanotube bundle of identical index （m, n） metallic carbon nanotubes.
基金supported by the National Natural Science Foundation of China(Grant No.71373079)
文摘This paper extends the resource drag studies by empirically investigating how spatial factors affect the regional economic growth. Using spatial panel econometric models, this paper estimates the dragging effect of energy resources of the Yangtze River Delta metropolitan areas. We fi nd that the growth drag of energy in the Yangtze River Delta is about 6% on average, which means that energy constraints decrease the economic growth by 6% annually, higher than the national level that has been previously measured in the literature. This result has taken into account the impact of neighboring cities' economic development, so as to obtain a more accurate estimate. Based on these measurement results, we propose some policy recommendations.
基金supported by the National Natural Science Foundation of China(Grant No.71373079)
文摘Using data of prefecture-level cities in Shandong province from 2004 to 2012 and the Stochastic Impacts by Regression on Population,Affluence,and Technology framework,this paper builds the geographically weighted regression(GWR)model of carbon emissions and its influencing factors.Unlike traditional econometric methods,such as ordinary least squares(OLS),the spatial econometrics models of spatial lag model(SLM)and spatial error model(SEM)are often estimate parameters constantly,namely these methods just estimate parameters in "average" or "globally" and can not reflect the parameters' nonstationary in different spaces.So in this paper,the influencing factors of carbon emissions are estimated by GWR,and the influencing factors of carbon emissions are estimated to be more realistic.The results indicates that the local GWR model is better than OLS,SLM and SEM,and there is spatial heterogeneity between the factors involved in economic growth,population status,industrial structure,energy price and carbon emissions across cities in Shandong province.
