针对地质灾害易发性评价因子分级数不确定的问题,引入自适应膨胀因子模糊覆盖分级方法(fuzzy cover approach for clustering based on adaptive inflation factor,AIFFC)对易发性评价因子分级进行优化。以湖南省湘乡市为研究区,提取了...针对地质灾害易发性评价因子分级数不确定的问题,引入自适应膨胀因子模糊覆盖分级方法(fuzzy cover approach for clustering based on adaptive inflation factor,AIFFC)对易发性评价因子分级进行优化。以湖南省湘乡市为研究区,提取了坡度、坡向、高程、年平均降雨量、归一化植被指数、道路、断层、岩性和土地利用9类评价因子,运用AIFFC及自然断点法(natural breakpoint classification,NBC)对连续型因子进行分级,并分别代入加权信息量模型和随机森林模型,获取研究区易发性区划图。采用单因子分级结果精度、灾积比分析和易发性分区结果对AIFFC分级法的优越性进行检验,结果表明:各因子采用AIFFC算法分级的AUC值均高于自然断点法;基于AIFFC的随机森林模型及加权信息量模型的高易发区灾积比分别提升了56.3%、74.6%,低易发区灾积比分别降低了48%、58.1%,AUC值分别提升了7.6%、2.7%。采用AIFFC分级方法优化了地质灾害易发性评价因子分级,显著提高了地质灾害易发性评价的合理性。展开更多
Forest fires in Algeria are ravaging an average of more than 32,000 hectares annually despite the prevention and control plan put in place. They are the most damaging factor of degradation of the forest and weigh heav...Forest fires in Algeria are ravaging an average of more than 32,000 hectares annually despite the prevention and control plan put in place. They are the most damaging factor of degradation of the forest and weigh heavily on the environment and the local economy. Conventional methods for fire prevention and control are time consuming and are not always reliable in view of the complexity and diversity of forest ecosystems. The main idea behind this study is to use the GIS and remote sensing for the development of a fire risk map of the Khoudida State Forest (Algeria). The approach adopted involves three parameters that control the fire behavior, which are: the top-morphology of the field, the combustibility of the plant cover and hazards. For each factor its correlation with risk was evaluated;the combination of the slope, altitude and exposure parameters in the topo-morphological index and the hazard map made it possible to evaluate the average risk for an area of more than 2132 hectares, 1521 hectares high and only 493 hectares, respectively 51.4%, 36.7% and 11.9%.展开更多
目的采用随机生存森林算法分析影响肝动脉化疗栓塞(transcatheter arterial chemoembolization,TACE)治疗不可切除肝细胞癌(hepatocellular carcinoma,HCC)患者的预后因素,并构建预后模型。方法回顾性选择2014年1月至2017年12月复旦大...目的采用随机生存森林算法分析影响肝动脉化疗栓塞(transcatheter arterial chemoembolization,TACE)治疗不可切除肝细胞癌(hepatocellular carcinoma,HCC)患者的预后因素,并构建预后模型。方法回顾性选择2014年1月至2017年12月复旦大学附属中山医院肝肿瘤内科收治的一线治疗为TACE的HCC患者636例,并按照7∶3比例划分为训练集(n=445)和验证集(n=191)。根据患者的临床数据、实验室指标及随访生存数据,建立Cox比例风险模型和基于机器学习算法的随机生存森林模型,并评估2种模型的预测能力。结果肿瘤负荷、年龄、基线G-谷氨酰转肽酶水平、基线甲胎蛋白水平和白蛋白-胆红素分级是影响TACE治疗不能切除HCC患者的独立预后因素。Cox回归模型的训练集1年、3年、5年的ROC曲线下面积(area under the curve,AUC)为0.782、0.796和0.791,验证集为0.750、0.766和0.766。随机生存森林模型训练集1年、3年和5年AUC为0.896、0.894和0.875,验证集为0.743、0.763和0.770。随机生存森林模型能将患者区分为预后好组和预后差组,两组生存期差异有统计学意义(P<0.05)。决策曲线显示随机生存森林模型的净获益优于Cox比例风险模型。结论随机生存森林模型是预测TACE治疗不可切除HCC患者预后的可靠工具。展开更多
文摘针对地质灾害易发性评价因子分级数不确定的问题,引入自适应膨胀因子模糊覆盖分级方法(fuzzy cover approach for clustering based on adaptive inflation factor,AIFFC)对易发性评价因子分级进行优化。以湖南省湘乡市为研究区,提取了坡度、坡向、高程、年平均降雨量、归一化植被指数、道路、断层、岩性和土地利用9类评价因子,运用AIFFC及自然断点法(natural breakpoint classification,NBC)对连续型因子进行分级,并分别代入加权信息量模型和随机森林模型,获取研究区易发性区划图。采用单因子分级结果精度、灾积比分析和易发性分区结果对AIFFC分级法的优越性进行检验,结果表明:各因子采用AIFFC算法分级的AUC值均高于自然断点法;基于AIFFC的随机森林模型及加权信息量模型的高易发区灾积比分别提升了56.3%、74.6%,低易发区灾积比分别降低了48%、58.1%,AUC值分别提升了7.6%、2.7%。采用AIFFC分级方法优化了地质灾害易发性评价因子分级,显著提高了地质灾害易发性评价的合理性。
文摘Forest fires in Algeria are ravaging an average of more than 32,000 hectares annually despite the prevention and control plan put in place. They are the most damaging factor of degradation of the forest and weigh heavily on the environment and the local economy. Conventional methods for fire prevention and control are time consuming and are not always reliable in view of the complexity and diversity of forest ecosystems. The main idea behind this study is to use the GIS and remote sensing for the development of a fire risk map of the Khoudida State Forest (Algeria). The approach adopted involves three parameters that control the fire behavior, which are: the top-morphology of the field, the combustibility of the plant cover and hazards. For each factor its correlation with risk was evaluated;the combination of the slope, altitude and exposure parameters in the topo-morphological index and the hazard map made it possible to evaluate the average risk for an area of more than 2132 hectares, 1521 hectares high and only 493 hectares, respectively 51.4%, 36.7% and 11.9%.
文摘目的采用随机生存森林算法分析影响肝动脉化疗栓塞(transcatheter arterial chemoembolization,TACE)治疗不可切除肝细胞癌(hepatocellular carcinoma,HCC)患者的预后因素,并构建预后模型。方法回顾性选择2014年1月至2017年12月复旦大学附属中山医院肝肿瘤内科收治的一线治疗为TACE的HCC患者636例,并按照7∶3比例划分为训练集(n=445)和验证集(n=191)。根据患者的临床数据、实验室指标及随访生存数据,建立Cox比例风险模型和基于机器学习算法的随机生存森林模型,并评估2种模型的预测能力。结果肿瘤负荷、年龄、基线G-谷氨酰转肽酶水平、基线甲胎蛋白水平和白蛋白-胆红素分级是影响TACE治疗不能切除HCC患者的独立预后因素。Cox回归模型的训练集1年、3年、5年的ROC曲线下面积(area under the curve,AUC)为0.782、0.796和0.791,验证集为0.750、0.766和0.766。随机生存森林模型训练集1年、3年和5年AUC为0.896、0.894和0.875,验证集为0.743、0.763和0.770。随机生存森林模型能将患者区分为预后好组和预后差组,两组生存期差异有统计学意义(P<0.05)。决策曲线显示随机生存森林模型的净获益优于Cox比例风险模型。结论随机生存森林模型是预测TACE治疗不可切除HCC患者预后的可靠工具。
