The Heihe River Basin is located in the arid and semi-arid regions of Northwest China.Here,the terrestrial ecosystem is vulnerable,making it necessary to identify the factors that could affect the ecosystem.In this st...The Heihe River Basin is located in the arid and semi-arid regions of Northwest China.Here,the terrestrial ecosystem is vulnerable,making it necessary to identify the factors that could affect the ecosystem.In this study,MODIS-NDVI data with a 250-m resolution were used as a proxy for the terrestrial ecosystem.By combining these with environmental factors,we were able to explore the spatial features of NDVI and identify the factors influencing the NDVI distribution in the Heihe River Basin during the period of 2000–2016.A geographical detector(Geodetector) was employed to examine the spatial heterogeneity of the NDVI and to explore the factors that could potentially influence the NDVI distribution.The results indicate that:(1) the NDVI in the Heihe River Basin appeared high in the southeast while being low in the north,showing spatial heterogeneity with a q-statistic of 0.38.The spatial trend of the vegetation in the three sub-basins generally increased in the growing seasons from 2000 to 2016;(2) The results obtained by the Geodetector(as denoted by the q-statistic as well as the degree of spatial association between the NDVI and environmental factors) showed spatial heterogeneity in the associations between the NDVI and the environmental factors for the overall basin as well as the sub-basins.Precipitation was the dominant factor for the overall basin.In the upper basin,elevation was found to be the dominant factor.The dominant factor in the middle basin was precipitation,closely followed by the soil type.In the lower basin,the dominant factor was soil type with a lower q-statistic of 0.13,and the dominant interaction between the elevation and soil type was nonlinearly enhanced(q-statistic = 0.22).展开更多
针对借贷过程中的信息不对称问题,为更有效地整合不同的数据源和贷款违约预测模型,提出一种集成学习的训练方法,使用AUC(Area Under Curve)值和Q统计值对学习器的准确性和多样性进行度量,并实现了基于AUC和Q统计值的集成学习训练算法(TA...针对借贷过程中的信息不对称问题,为更有效地整合不同的数据源和贷款违约预测模型,提出一种集成学习的训练方法,使用AUC(Area Under Curve)值和Q统计值对学习器的准确性和多样性进行度量,并实现了基于AUC和Q统计值的集成学习训练算法(TABAQ)。基于个人对个(P2P)贷款数据进行实证分析,发现集成学习的效果与基学习器的准确性和多样性关系密切,而与所集成的基学习器数量相关性较低,并且各种集成学习方法中统计集成表现最好。实验还发现,通过融合借款人端和投资人端的信息,可以有效地降低贷款违约预测中的信息不对称性。TABAQ能有效发挥数据源融合和学习器集成两方面的优势,在保持预测准确性稳步提升的同时,预测的一类错误数量更是进一步下降了4.85%。展开更多
基金National Key R&D Program of China,No.2017YFA0604704
文摘The Heihe River Basin is located in the arid and semi-arid regions of Northwest China.Here,the terrestrial ecosystem is vulnerable,making it necessary to identify the factors that could affect the ecosystem.In this study,MODIS-NDVI data with a 250-m resolution were used as a proxy for the terrestrial ecosystem.By combining these with environmental factors,we were able to explore the spatial features of NDVI and identify the factors influencing the NDVI distribution in the Heihe River Basin during the period of 2000–2016.A geographical detector(Geodetector) was employed to examine the spatial heterogeneity of the NDVI and to explore the factors that could potentially influence the NDVI distribution.The results indicate that:(1) the NDVI in the Heihe River Basin appeared high in the southeast while being low in the north,showing spatial heterogeneity with a q-statistic of 0.38.The spatial trend of the vegetation in the three sub-basins generally increased in the growing seasons from 2000 to 2016;(2) The results obtained by the Geodetector(as denoted by the q-statistic as well as the degree of spatial association between the NDVI and environmental factors) showed spatial heterogeneity in the associations between the NDVI and the environmental factors for the overall basin as well as the sub-basins.Precipitation was the dominant factor for the overall basin.In the upper basin,elevation was found to be the dominant factor.The dominant factor in the middle basin was precipitation,closely followed by the soil type.In the lower basin,the dominant factor was soil type with a lower q-statistic of 0.13,and the dominant interaction between the elevation and soil type was nonlinearly enhanced(q-statistic = 0.22).
文摘针对借贷过程中的信息不对称问题,为更有效地整合不同的数据源和贷款违约预测模型,提出一种集成学习的训练方法,使用AUC(Area Under Curve)值和Q统计值对学习器的准确性和多样性进行度量,并实现了基于AUC和Q统计值的集成学习训练算法(TABAQ)。基于个人对个(P2P)贷款数据进行实证分析,发现集成学习的效果与基学习器的准确性和多样性关系密切,而与所集成的基学习器数量相关性较低,并且各种集成学习方法中统计集成表现最好。实验还发现,通过融合借款人端和投资人端的信息,可以有效地降低贷款违约预测中的信息不对称性。TABAQ能有效发挥数据源融合和学习器集成两方面的优势,在保持预测准确性稳步提升的同时,预测的一类错误数量更是进一步下降了4.85%。
