The prediction process often runs with small samples and under-sufficient information.To target this problem,we propose a performance comparison study that combines prediction and optimization algorithms based on expe...The prediction process often runs with small samples and under-sufficient information.To target this problem,we propose a performance comparison study that combines prediction and optimization algorithms based on experimental data analysis.Through a large number of prediction and optimization experiments,the accuracy and stability of the prediction method and the correction ability of the optimization method are studied.First,five traditional single-item prediction methods are used to process small samples with under-sufficient information,and the standard deviation method is used to assign weights on the five methods for combined forecasting.The accuracy of the prediction results is ranked.The mean and variance of the rankings reflect the accuracy and stability of the prediction method.Second,the error elimination prediction optimization method is proposed.To make,the prediction results are corrected by error elimination optimization method(EEOM),Markov optimization and two-layer optimization separately to obtain more accurate prediction results.The degree improvement and decline are used to reflect the correction ability of the optimization method.The results show that the accuracy and stability of combined prediction are the best in the prediction methods,and the correction ability of error elimination optimization is the best in the optimization methods.The combination of the two methods can well solve the problem of prediction with small samples and under-sufficient information.Finally,the accuracy of the combination of the combined prediction and the error elimination optimization is verified by predicting the number of unsafe events in civil aviation in a certain year.展开更多
The present study aims to propose the method for the quantitative evaluation of safety concerning evacuation routes in case of earthquake disasters in urban areas using ACO （Ant Colony Optimization） algorithm and G...The present study aims to propose the method for the quantitative evaluation of safety concerning evacuation routes in case of earthquake disasters in urban areas using ACO （Ant Colony Optimization） algorithm and GIS （Geographic Information Systems）. Regarding the safety evaluation method, firstly, the similarity in safety was focused on while taking into consideration road blockage probability, and after classifying roads by means of the hierarchical cluster analysis, the congestion rates of evacuation routes using ACO simulations were estimated. Based on these results, the multiple evacuation routes extracted were visualized on digital maps by means of GIS, and its safety was evaluated. Furthermore, the selection of safe evacuation routes between evacuation sites, for cases when the possibility of large-scale evacuation after an earthquake disaster is high, is made possible. As the safety evaluation method is based on public information, by obtaining the same geographic information as the present study, it is effective in other areas regardless of whether the information is of the past and future. Therefore, in addition to spatial reproducibility, the safety evaluation method also has high temporal reproducibility. Because safety evaluations are conducted on evacuation routes based on quantified data, highly safe evacuation routes that are selected have been quantitatively evaluated, and thus serve as an effective indicator when selecting evacuation routes.展开更多
基金This work was supported by the Scientific Research Projects of Tianjin Educational Committee(No.2020KJ029)。
文摘The prediction process often runs with small samples and under-sufficient information.To target this problem,we propose a performance comparison study that combines prediction and optimization algorithms based on experimental data analysis.Through a large number of prediction and optimization experiments,the accuracy and stability of the prediction method and the correction ability of the optimization method are studied.First,five traditional single-item prediction methods are used to process small samples with under-sufficient information,and the standard deviation method is used to assign weights on the five methods for combined forecasting.The accuracy of the prediction results is ranked.The mean and variance of the rankings reflect the accuracy and stability of the prediction method.Second,the error elimination prediction optimization method is proposed.To make,the prediction results are corrected by error elimination optimization method(EEOM),Markov optimization and two-layer optimization separately to obtain more accurate prediction results.The degree improvement and decline are used to reflect the correction ability of the optimization method.The results show that the accuracy and stability of combined prediction are the best in the prediction methods,and the correction ability of error elimination optimization is the best in the optimization methods.The combination of the two methods can well solve the problem of prediction with small samples and under-sufficient information.Finally,the accuracy of the combination of the combined prediction and the error elimination optimization is verified by predicting the number of unsafe events in civil aviation in a certain year.
文摘The present study aims to propose the method for the quantitative evaluation of safety concerning evacuation routes in case of earthquake disasters in urban areas using ACO （Ant Colony Optimization） algorithm and GIS （Geographic Information Systems）. Regarding the safety evaluation method, firstly, the similarity in safety was focused on while taking into consideration road blockage probability, and after classifying roads by means of the hierarchical cluster analysis, the congestion rates of evacuation routes using ACO simulations were estimated. Based on these results, the multiple evacuation routes extracted were visualized on digital maps by means of GIS, and its safety was evaluated. Furthermore, the selection of safe evacuation routes between evacuation sites, for cases when the possibility of large-scale evacuation after an earthquake disaster is high, is made possible. As the safety evaluation method is based on public information, by obtaining the same geographic information as the present study, it is effective in other areas regardless of whether the information is of the past and future. Therefore, in addition to spatial reproducibility, the safety evaluation method also has high temporal reproducibility. Because safety evaluations are conducted on evacuation routes based on quantified data, highly safe evacuation routes that are selected have been quantitatively evaluated, and thus serve as an effective indicator when selecting evacuation routes.
