The evaluation of urban underground space(UUS)suitability involves multiple indicators.Assigning weight to these indicators is crucial for accurate assessment.This paper presents a method for spatially variable weight...The evaluation of urban underground space(UUS)suitability involves multiple indicators.Assigning weight to these indicators is crucial for accurate assessment.This paper presents a method for spatially variable weight assignment of indicators using the order relation analysis method(G1-method),the entropy weight method,an improved grey relational analysis(GRA)and a set of spatial weight adjustment coefficients.First,the subjective and objective weights of indicators for engineering geological and hydrogeological conditions were determined by the G1-method and entropy weight method,respectively,and their combined weights were then obtained using the principle of minimum discriminatory information.This study highlighted the impact of surface restrictions,such as buildings,on UUS,and the degree of the influence of these buildings gradually decreased with the increase in depth of the rock and soil mass in UUS,which resulted in changes in weights of indicators with depth.To address this issue,a coefficient was defined as the standardized value of the ratio of additional stress applied by restrictions to the self-weight stress of soil at the same depth to modify the combined weights so that all weights of indicators could vary in space.Finally,an improved GRA was used to determine the suitability level of each evaluation cell using the maximum correlation criterion.This method was applied to the 3D suitability evaluation of UUS in Sanlong Bay,Foshan City,Guangdong Province,China,including 16 evaluation indexes.This study comprehensively considered the influence of multiple factors,thereby providing reference for evaluating the suitability of UUS in big cities.展开更多
In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system,a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using concept...In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system,a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using conceptions of the relative error,the change tendency of the forecasted object,gray basic weight and adaptive control coefficient on the basis of the method of fuzzy variable weight.Based on Visual Basic 6.0 platform,a fuzzy adaptive variable weight combined forecasting and management system was developed.The application results reveal that the forecasting precisions from the new nonlinear combined forecasting model are higher than those of other single combined forecasting models and the combined forecasting and management system is very powerful tool for the required decision in complex industry system.展开更多
基金funded by the National Key R&D Program of China(Grant No.2023YFC3007001).
文摘The evaluation of urban underground space(UUS)suitability involves multiple indicators.Assigning weight to these indicators is crucial for accurate assessment.This paper presents a method for spatially variable weight assignment of indicators using the order relation analysis method(G1-method),the entropy weight method,an improved grey relational analysis(GRA)and a set of spatial weight adjustment coefficients.First,the subjective and objective weights of indicators for engineering geological and hydrogeological conditions were determined by the G1-method and entropy weight method,respectively,and their combined weights were then obtained using the principle of minimum discriminatory information.This study highlighted the impact of surface restrictions,such as buildings,on UUS,and the degree of the influence of these buildings gradually decreased with the increase in depth of the rock and soil mass in UUS,which resulted in changes in weights of indicators with depth.To address this issue,a coefficient was defined as the standardized value of the ratio of additional stress applied by restrictions to the self-weight stress of soil at the same depth to modify the combined weights so that all weights of indicators could vary in space.Finally,an improved GRA was used to determine the suitability level of each evaluation cell using the maximum correlation criterion.This method was applied to the 3D suitability evaluation of UUS in Sanlong Bay,Foshan City,Guangdong Province,China,including 16 evaluation indexes.This study comprehensively considered the influence of multiple factors,thereby providing reference for evaluating the suitability of UUS in big cities.
基金Project(08SK1002) supported by the Major Project of Science and Technology Department of Hunan Province,China
文摘In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system,a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using conceptions of the relative error,the change tendency of the forecasted object,gray basic weight and adaptive control coefficient on the basis of the method of fuzzy variable weight.Based on Visual Basic 6.0 platform,a fuzzy adaptive variable weight combined forecasting and management system was developed.The application results reveal that the forecasting precisions from the new nonlinear combined forecasting model are higher than those of other single combined forecasting models and the combined forecasting and management system is very powerful tool for the required decision in complex industry system.
