Comparison of the advantages and disadvantages of different machine learning methods in geohazard risk assessment: Taking Da'an Town, Ningqiang County as an example
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摘要:
研究目的 地质灾害的孕育和发生受多种因素的影响，具有不确定性和复杂性，给地质灾害的危险性评价带来一定困难。随着AI技术的发展，智能算法能更准确地计算地质灾害孕育与诱发因素之间的多元复杂非线性关系，大大提高了地质灾害危险性模型的准确性，在区域地质灾害危险性评价中逐步得到应用。
研究方法 本文结合宁强县大安镇野外地质调查数据，挑选与地质灾害发生密切相关的12种致灾因子，即高程、坡度、坡高、坡向、坡型、工程地质岩组、断裂距离、水系距离、道路距离、植被覆盖、降雨及地震动峰值等作为危险性分区评价因子。通过构建样本集，运用贝叶斯、随机森林、策略梯度神经网络、KNN和神经网络算法这5种模型进行宁强县大安镇地质灾害危险性建模并进行比较。
研究结果 贝叶斯模型（AUC 0.894）表现最好，绝大多数已发生的地质灾害点位于评价的极高和高危险区，且贝叶斯模型计算结果达到预测精度评价要求。
结论 在宁强县大安镇地质灾害样本数目很少的情况下选择贝叶斯算法模型进行地质灾害危险性评价，是具有可行性的。
Abstract:This paper is the result of geohazard survey engineering.
Objective The occurrence of geohazards are influenced by various factors, which have uncertainty and complexity, making it difficult to assess the risk of geohazards. With the development of AI technology, intelligent algorithms can more accurately calculate the complex and nonlinear relationships between geohazard triggering indexes, greatly improving the accuracy of geological hazard risk assessment models.
Methods Based on the field geological survey data of Da'an Town, Ningqiang County, 12 indexes closely related to the occurrence of geohazards were selected, namely elevation, slope, slope height, slope direction, slope type, engineering geological rock formations, fault distance, water system distance, road distance, vegetation coverage, rainfall, and seismic ground motion, as risk zoning evaluation factors. By constructing a sample set, Bayesian, strategy gradient neural network, random forest, KNN and neural network algorithm are used to model and compare the geohazard risk assessment result in Da'an Town, Ningqiang County.
Results The experimental results show that the Bayesian model (AUC 0.894) performs the best, with the vast majority of geohazards located in the extremely high and high−risk evaluated areas, and meets the requirements for prediction accuracy evaluation.
Conclusions It is feasible to choose Bayesian algorithm models for geological hazard risk assessment when the number of geohazard samples is small.
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Key words:
- geohazards /
- risk assessment /
- machine learning /
- geohazard survey engineering /
- Da'an Town /
- Ningqiang County /
- Shaanxi Province
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表 1 地质灾害影响因子多重共线性分析结果
Table 1. Multicollinearity analysis results of geohazards influencing factors
序列 地质灾害影响因子 统计值 TOL VIF 1 高程 0.823 1.256 2 坡型 0.674 1.765 3 坡度 0.786 1.453 4 坡高 0.887 1.125 5 坡向 0.763 1.543 6 地质岩组 0.345 2.765 7 断裂距离 0.278 3.121 8 水系距离 0.767 1.324 9 公路距离 0.243 3.988 10 植被 0.182 4.167 11 雨量 0.468 1.892 12 地震动峰值加速度 0.113 4.984 
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