摘要
机器学习已经在滑坡易发性评价中大量应用且取得了较好的表现,但在进行大区域评价时,仍存在数据库样本需求量大,算力要求高;影响因素分级机械化,未考虑其与滑坡机理的相关性等情况。为减少数据库样本需求,本文提出了构建包含3种坡体状态的滑坡的数据库:已经发生过失稳的坡体、正处于失稳状态的坡体、失稳概率小的坡体,该数据库可以在数值上划分出临界值,便于模型更准确地识别滑坡,较大幅度地减小了数据量。针对影响因素分级机械化的问题,提出了基于频数分布图、累计曲线及其导数图的数理统计方式,更精细地描述因数与滑坡易发性的关系。以新疆滑坡灾害为例,验证了“包含3种坡体状态的数据库”与“基于数理统计的描述方法”的适用性,获得了新疆滑坡灾害易发性分区图。结果表明与传统数据库对比,在不明显改变精度的前提下,减少了90%以上的样本量;基于数理统计的描述方法可以绘制出更加细致的滑坡危险性分区图;活动性断裂和地形起伏度对新疆滑坡易发性起到重要的控制作用。
Machine learning has been widely used in the evaluation of landslide susceptibility,and has achieved good performance.However,there are still many problems in the evaluation of large areas.The problems include a large number of database samples and high computing power.When the impact factors are classified,their correlation with the landslide mechanism is not considered.In order to reduce the demand for database samples,this paper proposes to construct a slope state that includes three states:a slope that has already experienced instability,a slope that is in an unstable state,and a slope with a low probability of instability,and a database of landslides.The critical value is divided numerically to highlight the landslide,which is convenient for the model to identify the landslide more accurately and greatly reduces the amount of data.Aiming at the problem of grading mechanization of influencing factors,a mathematical statistical method based on frequency distribution diagram,cumulative curve and its derivative diagram is proposed to describe the relationship between factors and landslide susceptibility in a more precise manner.Taking the landslide disaster in Xinjiang as an example,the applicability of the“database containing three slope states”and the“description method based on mathematical statistics”is verified.A susceptibility zoning map of Xinjiang is obtained.Under the premise of remaining the accuracy,the sample size is reduced by 90%.The description method based on mathematical statistics can draw a more detailed landslide risk zoning map.Landslide susceptibility in Xinjiang is mainly controlled by active faults and land surface fluctuations.
作者
梁龙飞
LIANG Longfei(China University of Mining and Technology,Xuzhou 221000,China)
出处
《工程地质学报》
CSCD
北大核心
2023年第4期1394-1406,共13页
Journal of Engineering Geology
基金
新疆维吾尔自治区重点研发计划项目(资助号:2021B03004-3).
关键词
机器学习
数据库
数理统计
滑坡
Machine Learning
Database
Mathematical statistics
Landslide