Oil and gas pipelines are of great importance in China,and pipeline security problems pose a serious threat to society and the environment.Pipeline safety has therefore become an integral part of the entire national e...Oil and gas pipelines are of great importance in China,and pipeline security problems pose a serious threat to society and the environment.Pipeline safety has therefore become an integral part of the entire national economy.Landslides are the most harmful type of pipeline accident,and have directed increasing public attention to safety issues.Although some useful results have been obtained in the investigation and prevention of pipeline-landslide hazards,there remains a need for effective monitoring and early warning methods,especially when the complexity of pipeline-landslides is considered.Because oil and gas pipeline-landslides typically occur in the superficial soil layers,monitoring instruments must be easy to install and must cause minimal disturbance to the surrounding soil and pipeline.To address the particular characteristics of pipelinelandslides,we developed a multi-parameter integrated monitoring system called disaster reduction stick equipment.In this paper,we detail this monitoring and early warning system for pipeline-landslide hazards based on an on-site monitoring network and early warning algorithms.The functionality of our system was verified by its successful application to the Chongqing Loujiazhuang pipeline-landslide in China.The results presented here provide guidelines for the monitoring,early warning,and prevention of pipeline geological hazards.展开更多
At present,most researches on the critical rainfall threshold of debris flow initiation use a linear model obtained through regression.With relatively weak fault tolerance,this method not only ignores nonlinear effect...At present,most researches on the critical rainfall threshold of debris flow initiation use a linear model obtained through regression.With relatively weak fault tolerance,this method not only ignores nonlinear effects but also is susceptible to singular noise samples,which makes it difficult to characterize the true quantization relationship of the rainfall threshold.Besides,the early warning threshold determined by statistical parameters is susceptible to negative samples(samples where no debris flow has occurred),which leads to uncertainty in the reliability of the early warning results by the regression curve.To overcome the above limitations,this study develops a data-driven multiobjective evolutionary optimization method that combines an artificial neural network(ANN)and a multiobjective evolutionary optimization implemented by particle swarm optimization(PSO).Firstly,the Pareto optimality method is used to represent the nonlinear and conflicting critical thresholds for the rainfall intensity I and the rainfall duration D.An ANN is used to construct a dual-target(dual-task)predictive surrogate model,and then a PSO-based multiobjective evolutionary optimization algorithm is applied to train the ANN and stochastically search the trained ANN for obtaining the Pareto front of the I-D surrogate prediction model,which is intended to overcome the limitations of the existing linear regression-based threshold methods.Finally,a double early warning curve model that can effectively control the false alarm rate and negative alarm rate of hazard warnings are proposed based on the decision space and target space maps.This study provides theoretical guidance for the early warning and forecasting of debris flows and has strong applicability.展开更多
基金financially supported by National Key R&D Program of China (No. 2018YFC1505201)National Natural Science Foundation of China (No. 41901008)+2 种基金Open Fund Project of Key Laboratory of Mountain Hazards and Surface Processes of the Chinese Academy of Sciencesthe Fundamental Research Funds for the Central Universities (Grant NO. 2682018CX05)financially supported by China Scholarship Council
文摘Oil and gas pipelines are of great importance in China,and pipeline security problems pose a serious threat to society and the environment.Pipeline safety has therefore become an integral part of the entire national economy.Landslides are the most harmful type of pipeline accident,and have directed increasing public attention to safety issues.Although some useful results have been obtained in the investigation and prevention of pipeline-landslide hazards,there remains a need for effective monitoring and early warning methods,especially when the complexity of pipeline-landslides is considered.Because oil and gas pipeline-landslides typically occur in the superficial soil layers,monitoring instruments must be easy to install and must cause minimal disturbance to the surrounding soil and pipeline.To address the particular characteristics of pipelinelandslides,we developed a multi-parameter integrated monitoring system called disaster reduction stick equipment.In this paper,we detail this monitoring and early warning system for pipeline-landslide hazards based on an on-site monitoring network and early warning algorithms.The functionality of our system was verified by its successful application to the Chongqing Loujiazhuang pipeline-landslide in China.The results presented here provide guidelines for the monitoring,early warning,and prevention of pipeline geological hazards.
基金financially supported by the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)(No.2019QZKK0906)National Natural Science Foundation of China(No.41901008 and No.61976046)+3 种基金National Key R&D Program of China(No.2017YFC1502504)the Fundamental Research Funds for the Central Universities(Grant No.2682018CX05)Beijing Municipal Science and Technology Project(Z191100001419015)financially supported by the China Scholarship Council。
文摘At present,most researches on the critical rainfall threshold of debris flow initiation use a linear model obtained through regression.With relatively weak fault tolerance,this method not only ignores nonlinear effects but also is susceptible to singular noise samples,which makes it difficult to characterize the true quantization relationship of the rainfall threshold.Besides,the early warning threshold determined by statistical parameters is susceptible to negative samples(samples where no debris flow has occurred),which leads to uncertainty in the reliability of the early warning results by the regression curve.To overcome the above limitations,this study develops a data-driven multiobjective evolutionary optimization method that combines an artificial neural network(ANN)and a multiobjective evolutionary optimization implemented by particle swarm optimization(PSO).Firstly,the Pareto optimality method is used to represent the nonlinear and conflicting critical thresholds for the rainfall intensity I and the rainfall duration D.An ANN is used to construct a dual-target(dual-task)predictive surrogate model,and then a PSO-based multiobjective evolutionary optimization algorithm is applied to train the ANN and stochastically search the trained ANN for obtaining the Pareto front of the I-D surrogate prediction model,which is intended to overcome the limitations of the existing linear regression-based threshold methods.Finally,a double early warning curve model that can effectively control the false alarm rate and negative alarm rate of hazard warnings are proposed based on the decision space and target space maps.This study provides theoretical guidance for the early warning and forecasting of debris flows and has strong applicability.
