To address the issues of single warning indicators,fixed thresholds,and insufficient adaptability in coal and gas outburst early warning models,this study proposes a dynamic early warning model for gas outbursts based...To address the issues of single warning indicators,fixed thresholds,and insufficient adaptability in coal and gas outburst early warning models,this study proposes a dynamic early warning model for gas outbursts based on adaptive fractal dimension characterization.By analyzing the nonlinear characteristics of gas concentration data,an adaptive window fractal analysis method is introduced.Combined with boxcounting dimension and variation of box dimension metrics,a cross-scale dynamic warning model for disaster prevention is established.The implementation involves three key phases:First,wavelet denoising and interpolation methods are employed for raw data preprocessing,followed by validation of fractal characteristics.Second,an adaptive window cross-scale fractal dimension method is proposed to calculate the box-counting dimension of gas concentration,enabling effective capture of multi-scale complex features.Finally,dynamic threshold partitioning is achieved through membership functions and the 3σprinciple,establishing a graded classification standard for the mine gas disaster(MGD)index.Validated through engineering applications at Shoushan#1 Coal Mine in Henan Province,the results demonstrate that the adaptive window fractal dimension curve exhibits significantly enhanced fluctuation characteristics compared to fixed window methods,with local feature detection capability improved and warning accuracy reaching 86.9%.The research reveals that this model effectively resolves the limitations of traditional methods in capturing local features and dependency on subjective thresholds through multiindicator fusion and threshold optimization,providing both theoretical foundation and practical tool for coal mine gas outburst early warning.展开更多
Flash floods are one of the most devastating natural hazards in mountainous and hilly areas.In this study,a dynamic warning model was proposed to improve the warning accuracy by addressing the problem of ignoring the ...Flash floods are one of the most devastating natural hazards in mountainous and hilly areas.In this study,a dynamic warning model was proposed to improve the warning accuracy by addressing the problem of ignoring the randomness and uncertainty of rainfall patterns in flash flood warning.A dynamic identification method for rainfall patterns was proposed based on the similarity theory and characteristic rainfall patterns database.The characteristic rainfall patterns were constructed by k-means clustering of historical rainfall data.Subsequently,the dynamic flood early warning model was proposed based on the real-time correction of rainfall patterns and flooding simulation by the HEC-HMS(Hydrologic Engineering Center's Hydrologic Modeling System)model.To verify the proposed model,three small watersheds in China were selected as case studies.The results show that the rainfall patterns identified by the proposed approach exhibit a high correlation with the observed rainfall.With the increase of measured rainfall information,the dynamic correction of the identified rainfall patterns results in corresponding flood forecasts with Nash-Sutcliffe efficiency(NSE)exceeding 0.8 at t=4,t=5,and t=6,thereby improving the accuracy of flash flood warnings.Simultaneously,the proposed model extends the forecast lead time with high accuracy.For rainfall with a duration of six hours in the Xinxian watershed and eight hours in the Tengzhou watershed,the proposed model issues early warnings two hours and three hours before the end of the rainfall,respectively,with a warning accuracy of more than 0.90.The proposed model can provide technical support for flash flood management in mountainous and hilly watersheds.展开更多
基金funded by the National Key Research and Development ProgramFund for Young Scientists(No.2021YFC2900400)+5 种基金the National Natural Science Foundation of China(No.52304123)Fundamental Research Funds for the Central Universities(No.2024CDJXY025)Sichuan-Chongqing Science and Technology Innovation Cooperation Program Project(No.CSTB2024TIAD-CYKJCXX0016)Postdoctoral Research Foundation of China(No.2023M730412)Postdoctoral Fellowship Program of China Postdoctoral Science Foundation(No.GZB20230914)Chongqing Outstanding Youth Science Foundation Program(No.CSTB2023NSCQ-JQX0027)。
文摘To address the issues of single warning indicators,fixed thresholds,and insufficient adaptability in coal and gas outburst early warning models,this study proposes a dynamic early warning model for gas outbursts based on adaptive fractal dimension characterization.By analyzing the nonlinear characteristics of gas concentration data,an adaptive window fractal analysis method is introduced.Combined with boxcounting dimension and variation of box dimension metrics,a cross-scale dynamic warning model for disaster prevention is established.The implementation involves three key phases:First,wavelet denoising and interpolation methods are employed for raw data preprocessing,followed by validation of fractal characteristics.Second,an adaptive window cross-scale fractal dimension method is proposed to calculate the box-counting dimension of gas concentration,enabling effective capture of multi-scale complex features.Finally,dynamic threshold partitioning is achieved through membership functions and the 3σprinciple,establishing a graded classification standard for the mine gas disaster(MGD)index.Validated through engineering applications at Shoushan#1 Coal Mine in Henan Province,the results demonstrate that the adaptive window fractal dimension curve exhibits significantly enhanced fluctuation characteristics compared to fixed window methods,with local feature detection capability improved and warning accuracy reaching 86.9%.The research reveals that this model effectively resolves the limitations of traditional methods in capturing local features and dependency on subjective thresholds through multiindicator fusion and threshold optimization,providing both theoretical foundation and practical tool for coal mine gas outburst early warning.
基金supported by the National Key R&D Program of China(Grant No.2022YFC3004401)the National Natural Science Foundation of China(Grant No.52109040)。
文摘Flash floods are one of the most devastating natural hazards in mountainous and hilly areas.In this study,a dynamic warning model was proposed to improve the warning accuracy by addressing the problem of ignoring the randomness and uncertainty of rainfall patterns in flash flood warning.A dynamic identification method for rainfall patterns was proposed based on the similarity theory and characteristic rainfall patterns database.The characteristic rainfall patterns were constructed by k-means clustering of historical rainfall data.Subsequently,the dynamic flood early warning model was proposed based on the real-time correction of rainfall patterns and flooding simulation by the HEC-HMS(Hydrologic Engineering Center's Hydrologic Modeling System)model.To verify the proposed model,three small watersheds in China were selected as case studies.The results show that the rainfall patterns identified by the proposed approach exhibit a high correlation with the observed rainfall.With the increase of measured rainfall information,the dynamic correction of the identified rainfall patterns results in corresponding flood forecasts with Nash-Sutcliffe efficiency(NSE)exceeding 0.8 at t=4,t=5,and t=6,thereby improving the accuracy of flash flood warnings.Simultaneously,the proposed model extends the forecast lead time with high accuracy.For rainfall with a duration of six hours in the Xinxian watershed and eight hours in the Tengzhou watershed,the proposed model issues early warnings two hours and three hours before the end of the rainfall,respectively,with a warning accuracy of more than 0.90.The proposed model can provide technical support for flash flood management in mountainous and hilly watersheds.
