顾及动力耦合关系的地质灾害链模型参数遗传进化率定方法

李倩, 丁雨淋, 刘伟, 刘飞, 向波, 何云勇, 朱庆

李倩, 丁雨淋, 刘伟, 刘飞, 向波, 何云勇, 朱庆. 顾及动力耦合关系的地质灾害链模型参数遗传进化率定方法[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20230082
引用本文: 李倩, 丁雨淋, 刘伟, 刘飞, 向波, 何云勇, 朱庆. 顾及动力耦合关系的地质灾害链模型参数遗传进化率定方法[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20230082
LI Qian, DING Yulin, LIU Wei, LIU Fei, XIANG Bo, HE Yunyong, ZHU Qing. A Parameter Genetic Algorithm for Evolutionary Calibration in Coupled Dynamics Models of Geological Hazard Chain[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20230082
Citation: LI Qian, DING Yulin, LIU Wei, LIU Fei, XIANG Bo, HE Yunyong, ZHU Qing. A Parameter Genetic Algorithm for Evolutionary Calibration in Coupled Dynamics Models of Geological Hazard Chain[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20230082

顾及动力耦合关系的地质灾害链模型参数遗传进化率定方法

基金项目: 

四川省交通运输科技项目 2021-A-04

详细信息
    作者简介:

    李倩,硕士,主要从事虚拟地理环境、灾害模拟研究。Email:lq1007@my.swjtu.edu.cn

    通讯作者:

    丁雨淋,博士,副教授,博士生导师。E-mail:rainforests@126.com

  • 中图分类号: P694

A Parameter Genetic Algorithm for Evolutionary Calibration in Coupled Dynamics Models of Geological Hazard Chain

  • 摘要: 滑坡-堰塞湖-溃决洪水地质灾害链精准模拟是山区地质灾害综合防灾减灾的关键手段,可靠的模型参数是保证模拟结果准确性的重要前提。然而,该地质灾害链多阶段机理模型间的耦合关系复杂、参数众多,传统面向单灾种模型的参数率定方法较少考虑不同灾害之间的链式关系,灾害链模型群协同交互运算过程中多参数率定计算复杂、误差累积问题突出。针对上述问题,提出了一种顾及动力耦合关系的地质灾害链模型参数遗传进化率定方法,首先建立了由颗粒流模型、漂移通量模型、泥沙冲刷模型耦合的滑坡灾害链动力耦合模型,其次提出了模型参数遗传进化率定方法,通过选择-交叉-变异等迭代遗传进化运算,得到最优种群的参数集。选用2018年白格滑坡灾害链进行试验验证,结果表明,最优参数组的模拟结果与实测数据吻合度较好,在400次迭代后种群最大适应度为0.932,偏差值在±5%以内,证明了此参数率定方法的可行性和准确性。
    Abstract: Objectives: Complex terrain and geological conditions in the western mountainous areas of China have resulted in frequent hazard chains. The simulation assessments of the landslide-barrier lake-outburst flood hazard chains are an effective tool for integrated hazard management, and reasonable model parameters are the primary prerequisite to ensure the reliability of simulation assessment results. However, the coupling relationships among the multi-stage mechanism models of hazard chain are complex. The traditional parameter calibration methods for single-hazard models seldom consider the chain relationships among different hazards, and the multi-parameter calibration calculations are complicated and the error accumulation problem is prominent during the cooperative interaction operation of the hazard chain model groups. Methods: In response to the demand for tightly coupled modeling and global calibration of nonlinear cascade relationships, we proposes a dynamic data-driven multi-parameter genetic calibration method for landslide-weir- flood hazard chain coupled dynamical model. Firstly, a landslide hazard chain coupled dynamical model coupled by granular flow model, drift flux model, and sediment scour model is established. Secondly, a multi-parameter genetic evolutionary under dynamic observation data constraint is studied The calibration method is used to obtain the parameter set of the optimal population by iterative genetic evolution operations such as selection-crossover-variation. Results: The proposed method was validated using the 2018 Baige landslide hazard chain experiment, and the simulation results of the optimal parameter set were found to be in good agreement with the measured data after 400 iterations. The maximum fitness of the population was 0.932, with a deviation of less than ±5%, demonstrating the feasibility and accuracy of this parameter calibration method. The advantages of this method over traditional calibration methods in terms of effectiveness, stability, time consumption, and efficiency were discussed from multiple perspectives. Conclusions: This method effectively utilizes temporal observation data to dynamically calibrate model parameters and accurately describes the spatiotemporal variation process of the hazard chain, providing support for the comprehensive management of geological hazard chains. Further research will focus on knowledge-guided reliable simulation and evaluation methods for hazard chains, enabling reliable analysis of hazard risks under incomplete data conditions.
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出版历程
  • 收稿日期:  2023-06-03
  • 网络出版日期:  2023-07-19

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