水资源承载力关键驱动因素的识别是进行水资源承载能力评价和预警工作的基础依据,是开展定量评价水资源承载能力的重要基础.提出了水资源承载能力影响因素有效识别和关键驱动因子评估框架,从水量、水质、水生态、社会经济四个角度,将DEM...水资源承载力关键驱动因素的识别是进行水资源承载能力评价和预警工作的基础依据,是开展定量评价水资源承载能力的重要基础.提出了水资源承载能力影响因素有效识别和关键驱动因子评估框架,从水量、水质、水生态、社会经济四个角度,将DEMATEL(Decision Making Trial and Evaluation Laboratory)方法和主成分分析法相结合,从定性与定量两个方面开展水资源承载力影响因素的有效识别,并采用熵权法提取关键驱动因素.研究结果表明:全国水资源承载力关键驱动因素分别是干旱指数、万元GDP用水量、氨氮排放量、湿地占比、城市污水处理率、COD排放量、大中型水库蓄水量.研究成果以期为水资源承载能力有效评价提供科学依据,推动水资源承载能力监测预警机制建设.展开更多
In order to address the issues of complex system structure and variable selection difficulty for the current heavy haul railway line status evaluation system, a three-category and three-layer heavy-haul line status ev...In order to address the issues of complex system structure and variable selection difficulty for the current heavy haul railway line status evaluation system, a three-category and three-layer heavy-haul line status evaluation variable set construction and reduction optimization method is proposed. Firstly, the status of heavy haul railway line is analyzed, and an initial set of evaluation variables affecting the line status is constructed. Then, based on the association rule and the principal component analysis method, key variables are extracted from the initial variable set to establish the evaluation system. Finally, this method is verified with actual data of a line. The results show that the service performance of heavy haul railway line can still be evaluated accurately when the evaluation variables are reduced by 60% in the proposed method.展开更多
文摘水资源承载力关键驱动因素的识别是进行水资源承载能力评价和预警工作的基础依据,是开展定量评价水资源承载能力的重要基础.提出了水资源承载能力影响因素有效识别和关键驱动因子评估框架,从水量、水质、水生态、社会经济四个角度,将DEMATEL(Decision Making Trial and Evaluation Laboratory)方法和主成分分析法相结合,从定性与定量两个方面开展水资源承载力影响因素的有效识别,并采用熵权法提取关键驱动因素.研究结果表明:全国水资源承载力关键驱动因素分别是干旱指数、万元GDP用水量、氨氮排放量、湿地占比、城市污水处理率、COD排放量、大中型水库蓄水量.研究成果以期为水资源承载能力有效评价提供科学依据,推动水资源承载能力监测预警机制建设.
文摘In order to address the issues of complex system structure and variable selection difficulty for the current heavy haul railway line status evaluation system, a three-category and three-layer heavy-haul line status evaluation variable set construction and reduction optimization method is proposed. Firstly, the status of heavy haul railway line is analyzed, and an initial set of evaluation variables affecting the line status is constructed. Then, based on the association rule and the principal component analysis method, key variables are extracted from the initial variable set to establish the evaluation system. Finally, this method is verified with actual data of a line. The results show that the service performance of heavy haul railway line can still be evaluated accurately when the evaluation variables are reduced by 60% in the proposed method.
