基于CF与优化RF模型耦合的泰山地区地质灾害易发性评价

咸利民; 季民; 刘法军; 李强

doi:10.13961/j.cnki.stbctb.2024.05.015

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基于CF与优化RF模型耦合的泰山地区地质灾害易发性评价

试验研究

- 基于CF与优化RF模型耦合的泰山地区地质灾害易发性评价
- Assessing Geological Disaster Susceptibility in Taishan Area by Coupling Certainty Factor Model with Optimized Random Forest Model
- 水土保持通报 2024年43卷第5期页码：134-143
- 作者机构：
  
  1. 山东科技大学测绘与空间信息学院,山东,青岛,266590
  2. 山东省地质矿产勘查开发局第五地质大队,山东,泰安,271000
  3. 聊城市地质矿产调查监测中心,山东,聊城,252000
- 作者简介：
- 基金信息：
  
  2023年山东省本科教学改革研究重大项目“面向智慧城市的测绘类专业人才培养探索与实践”(Z2023208)
- DOI：10.13961/j.cnki.stbctb.2024.05.015
  中图分类号： X43;P642.2
- 纸质出版：2024
- 稿件说明：
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咸利民, 季民, 刘法军, 等. 基于CF与优化RF模型耦合的泰山地区地质灾害易发性评价[J]. 水土保持通报, 2024,43(5):134-143.

Xian Limin, Ji Min, Liu Fajun, et al. Assessing Geological Disaster Susceptibility in Taishan Area by Coupling Certainty Factor Model with Optimized Random Forest Model[J]. Bulletin of Soiland Water Conservation, 2024, 43(5): 134-143.
咸利民, 季民, 刘法军, 等. 基于CF与优化RF模型耦合的泰山地区地质灾害易发性评价[J]. 水土保持通报, 2024,43(5):134-143. DOI： 10.13961/j.cnki.stbctb.2024.05.015.

Xian Limin, Ji Min, Liu Fajun, et al. Assessing Geological Disaster Susceptibility in Taishan Area by Coupling Certainty Factor Model with Optimized Random Forest Model[J]. Bulletin of Soiland Water Conservation, 2024, 43(5): 134-143. DOI： 10.13961/j.cnki.stbctb.2024.05.015.

摘要

[目的] 针对泰山地区地质灾害频发这一现状，研究并构建地质灾害易发性评价模型，为该地区的地质灾害预防与治理工作提供参考。[方法] 以泰山地区为研究区，采用确定性系数模型与粒子群算法优化RF模型耦合的方法，完成对研究区的地质灾害易发性评价。该方法是利用确定性系数(CF)模型计算影响因子对地质灾害的敏感值，作为模型训练的属性值，引入粒子群算法对随机森林(RF)模型进行参数寻优，提高模型对地质灾害的预测精度和准确度。选取坡度、距道路距离、土地利用类型、植被指数等11个影响因子，采用皮尔逊相关系数法和多重共线性检查进行影响因子筛选择优，绘制ROC和PR曲线对训练模型进行精度评价。[结果] CF-PSO-RF耦合模型相比单一SVR、单一RF和CF-PSO-SVR模型的极高易发区面积比例分别提高10.55%，10.04%和5.08%，AUC值分别提高14%，5.1%和1.7%，AP精度分别提高了11.7%，4.4%，1.2%。预测结果显示，泰山地区的极高、高易发区主要位于泰山景区、岱岳区北部等地形起伏和坡度较大的区域，面积所占比例为28.05%，涵盖了60.1%的地质灾害点；相反，低、极低易发区主要分布在建设用地、农田等地势平坦区域，面积比例为59.26%。[结论] 将确定性系数模型与优化后RF模型耦合，相比单一模型精度有进一步的提升，又优于CF-PSO-SVR模型精度，评价结果符合实际情况。

Abstract

[Objective] The present study was performed to develop a geological disaster susceptibility evaluation model for predicting frequent geological disasters in the Taishan area. The aim was to use the results of this susceptibility evaluation as reference for preventing and managing geological disasters in this area. [Methods] Our analyses focused on the Taishan area

employing a method in which the certainty factor (CF) model was coupled with a random forest (RF) model optimized using the particle swarm optimization (PSO) algorithm to evaluate the geological disaster susceptibility in the research area. This method uses the CF model to calculate the sensitivity values of the factors influencing geological disasters

which are then used as attribute values for model training. The PSO algorithm was introduced to optimize the parameters of the RF model

thereby improving the accuracy and precision of the model in predicting geological disasters. Eleven influencing factors

including slope

distance to roads

land-use type

and vegetation index

were selected. The Pearson correlation coefficient method and a multicollinearity check were used to screen and optimize these influencing factors. The precision of the trained model was evaluated using ROC and PR curves. [Results] Compared with those of the single SVR

single RF

and CF-PSO-SVR models

the CF-PSO-RF coupled model significantly improved the proportion of extremely-high-susceptibility areas by 10.55%

10.04%

and 5.08%

respectively

increased the AUC values by 14%

5.1%

and 1.7%

respectively

and enhanced the average precision (AP)accuracy by 11.7%

4.4%

and 1.2%

respectively. The prediction results revealed that the regions with high and extremely high susceptibility to geological disasters were mainly located in the Taishan scenic area

Northern Daiyue District

and other regions with significant topographic relief and steep slopes

covering 28.05% of the area and encompassing 60.1% of the geological disaster points. In contrast

regions with low and very low susceptibility were primarily distributed in flat areas

such as construction and farm lands

accounting for 59.26% of the total area. [Conclusion] The accuracy of the CF-PSO-RF coupled model for disaster susceptibility evaluation was notably higher than that of the single models; further

its precision was superior to that of the CF-PSO-SVR model. These evaluation results are consistent with actual conditions.

关键词

Keywords

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