Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity ana...Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity analysis of hydrological model is a key step in model uncertainty quantification, which can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. There are, however, some shortcomings in classical approaches, including the long duration of time and high computation cost required to quantitatively assess the sensitivity of a multiple-parameter hydrological model. For this reason, a two-step statistical evaluation framework using global techniques is presented. It is based on （1） a screening method （Morris） for qualitative ranking of parameters, and （2） a variance-based method integrated with a meta-model for quantitative sensitivity analysis, i.e., the Sobol method integrated with the response surface model （RSMSobol）. First, the Morris screening method was used to qualitatively identify the parameters＇ sensitivity, and then ten parameters were selected to quantify the sensitivity indices. Subsequently, the RSMSobol method was used to quantify the sensitivity, i.e., the first-order and total sensitivity indices based on the response surface model （RSM） were calculated. The RSMSobol method can not only quantify the sensitivity, but also reduce the computational cost, with good accuracy compared to the classical approaches. This approach will be effective and reliable in the global sensitivity analysis of a complex large-scale distributed hydrological model.展开更多
A proposed concept of outburst initiation examines the release of a large amount of gas from coal seams resulted from disintegrating thermodynamically unstable coal organic matter(COM).A coal microstructure is assumed...A proposed concept of outburst initiation examines the release of a large amount of gas from coal seams resulted from disintegrating thermodynamically unstable coal organic matter(COM).A coal microstructure is assumed to getting unstable due to shear component appearance triggered by mining operations and tectonic activities considered as the primary factor while COM disintegration under the impact of weak electric fields can be defined as a secondary one.The energy of elastic deformations stored in the coal microstructure activates chemical reactions to tilt the energy balance in a“coal–gas”system.Based on this concept a mathematical model of a gas flow in the coal where porosity and permeability are changed due to chemical reactions has been developed.Using this model we calculated gas pressure changes in the pores initiated by gas release near the working face till satisfying force and energy criteria of outburst.The simulation results demonstrated forming overpressure zone in the area of intensive gas release with enhanced porosity and permeability.The calculated outburst parameters are well combined with those evaluated by field measurements.展开更多
A logistic model was employed to correlate the outbreak of highly pathogenic avian influenza (HPAI) with related environmental factors and the migration of birds. Based on MODIS data of the normalized difference veget...A logistic model was employed to correlate the outbreak of highly pathogenic avian influenza (HPAI) with related environmental factors and the migration of birds. Based on MODIS data of the normalized difference vegetation index, environmental factors were considered in generating a probability map with the aid of logistic regression. A Bayesian maximum entropy model was employed to explore the spatial and temporal correlations of HPAI incidence. The results show that proximity to water bodies and national highways was statistically relevant to the occurrence of HPAI. Migratory birds, mainly waterfowl, were important infection sources in HPAI transmission. In addition, the HPAI outbreaks had high spatiotemporal autocorrelation. This epidemic spatial range fluctuated 45 km owing to different distribution patterns of cities and water bodies. Furthermore, two outbreaks were likely to occur with a period of 22 d. The potential risk of occurrence of HPAI in China's Mainland for the period from January 23 to February 17, 2004 was simulated based on these findings, providing a useful meta-model framework for the application of environmental factors in the prediction of HPAI risk.展开更多
基金supported by the National Natural Science Foundation of China (Grant No. 41271003)the National Basic Research Program of China (Grants No. 2010CB428403 and 2010CB951103)
文摘Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity analysis of hydrological model is a key step in model uncertainty quantification, which can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. There are, however, some shortcomings in classical approaches, including the long duration of time and high computation cost required to quantitatively assess the sensitivity of a multiple-parameter hydrological model. For this reason, a two-step statistical evaluation framework using global techniques is presented. It is based on （1） a screening method （Morris） for qualitative ranking of parameters, and （2） a variance-based method integrated with a meta-model for quantitative sensitivity analysis, i.e., the Sobol method integrated with the response surface model （RSMSobol）. First, the Morris screening method was used to qualitatively identify the parameters＇ sensitivity, and then ten parameters were selected to quantify the sensitivity indices. Subsequently, the RSMSobol method was used to quantify the sensitivity, i.e., the first-order and total sensitivity indices based on the response surface model （RSM） were calculated. The RSMSobol method can not only quantify the sensitivity, but also reduce the computational cost, with good accuracy compared to the classical approaches. This approach will be effective and reliable in the global sensitivity analysis of a complex large-scale distributed hydrological model.
基金the Ministry of Education and Science of Ukraine(No.0117U001129).
文摘A proposed concept of outburst initiation examines the release of a large amount of gas from coal seams resulted from disintegrating thermodynamically unstable coal organic matter(COM).A coal microstructure is assumed to getting unstable due to shear component appearance triggered by mining operations and tectonic activities considered as the primary factor while COM disintegration under the impact of weak electric fields can be defined as a secondary one.The energy of elastic deformations stored in the coal microstructure activates chemical reactions to tilt the energy balance in a“coal–gas”system.Based on this concept a mathematical model of a gas flow in the coal where porosity and permeability are changed due to chemical reactions has been developed.Using this model we calculated gas pressure changes in the pores initiated by gas release near the working face till satisfying force and energy criteria of outburst.The simulation results demonstrated forming overpressure zone in the area of intensive gas release with enhanced porosity and permeability.The calculated outburst parameters are well combined with those evaluated by field measurements.
基金supported by the National Basic Research Program of China (2007CB714404)the National Natural Science Foundation of China (40871173)the Spe-cial Grant for the Prevention and Treatment of Infectious Diseases (2008ZX10004-012)
文摘A logistic model was employed to correlate the outbreak of highly pathogenic avian influenza (HPAI) with related environmental factors and the migration of birds. Based on MODIS data of the normalized difference vegetation index, environmental factors were considered in generating a probability map with the aid of logistic regression. A Bayesian maximum entropy model was employed to explore the spatial and temporal correlations of HPAI incidence. The results show that proximity to water bodies and national highways was statistically relevant to the occurrence of HPAI. Migratory birds, mainly waterfowl, were important infection sources in HPAI transmission. In addition, the HPAI outbreaks had high spatiotemporal autocorrelation. This epidemic spatial range fluctuated 45 km owing to different distribution patterns of cities and water bodies. Furthermore, two outbreaks were likely to occur with a period of 22 d. The potential risk of occurrence of HPAI in China's Mainland for the period from January 23 to February 17, 2004 was simulated based on these findings, providing a useful meta-model framework for the application of environmental factors in the prediction of HPAI risk.
