基于机器学习的地质灾害风险评估研究

Geologic hazard risk assessment based on machine learning

针对地质灾害风险评估因子系数不准确和风险区划不合理的问题,本文从地质灾害多要素多特征角度出发,提出了一种基于格网的机器学习模型地质灾害风险评估方法。首先,收集和整理温州市地质灾害资料,利用ArcGIS空间分析和属性计算功能,再引入神经网络模型进行权重因子优化和筛选,将筛选的地貌类型、地形坡度、道路建设、地质构造、土壤类型、河流分布、植被覆盖度等7个因子作为地质灾害评价因子,并得到优选后的各因子权重系数;最后,利用加权分析得出地质灾害风险因子评价图。研究结果表明,通过细分格网单元的机器学习模型自动计算评价因子和权重系数,能够提高地质灾害风险评价的合理性,可以为区域性地质灾害的防灾减灾管理提供科学依据和参考。

For the problems of inaccurate factor coefficients and unreasonable risk zoning in geologic hazard risk assessment,this paper proposes a machine learning model geologic hazard risk assessment method based on grid from the perspectives of multi-factor and multi-feature of geologic hazard.Firstly,it collects and organizes the geohazard data in Wenzhou City,uses the spatial analysis and attribute calculation function of ArcGIS,introduces the neural network model to optimize and screen the weighting factors,and takes the selected geomorphic type,topographic slope,road construction,geological structure,soil type,river distribution,vegetation coverage as the geologic hazard evaluation factors,and gets the weighting coefficients of each preferred factor.Finally,it uses the weighted analysis results in the evaluation map of geologic hazard risk factors.The results show that the automatic calculation of evaluation factors and weight coefficients by the machine learning model of subdivided grid cells can improve the rationality of the evaluation of geohazard risk,which can provide a scientific basis and reference for the management of regional geohazard prevention and mitigation.