An effective evaluation system can provide specific and practical suggestions to the deep groundwater management. But such kind of evaluation system has not been proposed in China. In this study, an evaluation index s...An effective evaluation system can provide specific and practical suggestions to the deep groundwater management. But such kind of evaluation system has not been proposed in China. In this study, an evaluation index system is specifically developed to evaluate deep groundwater management efficiency. It is composed of three first-level indicators(law enforcement capability, management ability, and management effectiveness) and eleven second-level indicators. The second-level indicators include seven mandatory indicators and four optional indicators. Piecewise linear function is used to normalize the quantitative indicators, and expert scoring method and questionnaire survey method are used to normalize the qualitative indicators. Then a comprehensive indicator weighting evaluation method is used to evaluate the first-level indicators and the target topic. A case study is carried out to evaluate deep groundwater management efficiency in Tianjin City. According to the evaluation score in each period, the management efficiency of every district in Tianjin City gradually improved. The overall evaluation score in the early deep groundwater extraction period is 0.12. After a series of deep groundwater protection efforts, this score reached to 0.61 in 2007, and met the regulation criteria. The evaluation results also showed that the further groundwater management efforts in Tianjin City should be focused on building a dynamic database to collect comprehensive deep well-log data; and on a reasonable design and distribution of the groundwater monitoring network. It demonstrated that the index system is suitable to locate the deficiencies of current groundwater management systems and to guide further improvements. It can then be used to protect deep groundwater.展开更多
It is generally believed that intelligent management for sewage treatment plants(STPs) is essential to the sustainable engineering of future smart cities.The core of management lies in the precise prediction of daily ...It is generally believed that intelligent management for sewage treatment plants(STPs) is essential to the sustainable engineering of future smart cities.The core of management lies in the precise prediction of daily volumes of sewage.The generation of sewage is the result of multiple factors from the whole social system.Characterized by strong process abstraction ability,data mining techniques have been viewed as promising prediction methods to realize intelligent STP management.However,existing data mining-based methods for this purpose just focus on a single factor such as an economical or meteorological factor and ignore their collaborative effects.To address this challenge,a deep learning-based intelligent management mechanism for STPs is proposed,to predict business volume.Specifically,the grey relation algorithm(GRA) and gated recursive unit network(GRU) are combined into a prediction model(GRAGRU).The GRA is utilized to select the factors that have a significant impact on the sewage business volume,and the GRU is set up to output the prediction results.We conducted a large number of experiments to verify the efficiency of the proposed GRA-GRU model.展开更多
To promote the construction of Shanghai international shipping center, the planneu new acepwatcr terminal construction in Hengsha pushes forward the innovation and breakthroughs of the existing port manage- ment syste...To promote the construction of Shanghai international shipping center, the planneu new acepwatcr terminal construction in Hengsha pushes forward the innovation and breakthroughs of the existing port manage- ment system and building mechanisms. Through reviewing, analyzing, comparing and summarizing the suc- cessful experience of the major ports at home and abroad, market-oriented recommendations will be proposed in terms of effectiveness and feasibility, as well as the idea of＂Shanghai Freeport＂.展开更多
基金Under the auspices of National Basic Research Program of China(No.2010CB428804)
文摘An effective evaluation system can provide specific and practical suggestions to the deep groundwater management. But such kind of evaluation system has not been proposed in China. In this study, an evaluation index system is specifically developed to evaluate deep groundwater management efficiency. It is composed of three first-level indicators(law enforcement capability, management ability, and management effectiveness) and eleven second-level indicators. The second-level indicators include seven mandatory indicators and four optional indicators. Piecewise linear function is used to normalize the quantitative indicators, and expert scoring method and questionnaire survey method are used to normalize the qualitative indicators. Then a comprehensive indicator weighting evaluation method is used to evaluate the first-level indicators and the target topic. A case study is carried out to evaluate deep groundwater management efficiency in Tianjin City. According to the evaluation score in each period, the management efficiency of every district in Tianjin City gradually improved. The overall evaluation score in the early deep groundwater extraction period is 0.12. After a series of deep groundwater protection efforts, this score reached to 0.61 in 2007, and met the regulation criteria. The evaluation results also showed that the further groundwater management efforts in Tianjin City should be focused on building a dynamic database to collect comprehensive deep well-log data; and on a reasonable design and distribution of the groundwater monitoring network. It demonstrated that the index system is suitable to locate the deficiencies of current groundwater management systems and to guide further improvements. It can then be used to protect deep groundwater.
基金Project(KJZD-M202000801) supported by the Major Project of Chongqing Municipal Education Commission,ChinaProject(2016YFE0205600) supported by the National Key Research&Development Program of China+1 种基金Project(CXQT19023) supported by the Chongqing University Innovation Group Project,ChinaProjects(KFJJ2018069,1853061,1856033) supported by the Key Platform Opening Project of Chongqing Technology and Business University,China。
文摘It is generally believed that intelligent management for sewage treatment plants(STPs) is essential to the sustainable engineering of future smart cities.The core of management lies in the precise prediction of daily volumes of sewage.The generation of sewage is the result of multiple factors from the whole social system.Characterized by strong process abstraction ability,data mining techniques have been viewed as promising prediction methods to realize intelligent STP management.However,existing data mining-based methods for this purpose just focus on a single factor such as an economical or meteorological factor and ignore their collaborative effects.To address this challenge,a deep learning-based intelligent management mechanism for STPs is proposed,to predict business volume.Specifically,the grey relation algorithm(GRA) and gated recursive unit network(GRU) are combined into a prediction model(GRAGRU).The GRA is utilized to select the factors that have a significant impact on the sewage business volume,and the GRU is set up to output the prediction results.We conducted a large number of experiments to verify the efficiency of the proposed GRA-GRU model.
基金Shanghai Science and Technology Research Plan(No.13dz1204900)
文摘To promote the construction of Shanghai international shipping center, the planneu new acepwatcr terminal construction in Hengsha pushes forward the innovation and breakthroughs of the existing port manage- ment system and building mechanisms. Through reviewing, analyzing, comparing and summarizing the suc- cessful experience of the major ports at home and abroad, market-oriented recommendations will be proposed in terms of effectiveness and feasibility, as well as the idea of＂Shanghai Freeport＂.
