| 环境因子组合和负样本选取策略对花岗岩区崩岗易发性的影响 |
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| 引用本文： | 郭飞,蒋广辉,黄晓虎,王秀娟,夏栋,陈洋,李小伟.环境因子组合和负样本选取策略对花岗岩区崩岗易发性的影响[J].农业工程学报,2024,40(1):199-208. |
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| 作者姓名： | 郭飞 蒋广辉 黄晓虎 王秀娟 夏栋 陈洋 李小伟 |
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| 作者单位： | 三峡库区地质灾害教育部重点实验室，宜昌 443002;三峡大学土木与建筑学院，宜昌 443002;三峡大学水利与环境学院，宜昌 443002;广东海洋大学电子与信息工程学院，湛江 524000;中南冶金地质研究所 宜昌 443003 |
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| 基金项目： | 国家自然科学基金项目（42107489）；湖北省自然科学基金项目（2022CFB557）；湖北巴东地质灾害国家野外科学观测研究站开放基金项目（BNORSG202304）；三峡库区地质灾害教育部重点实验室开放基金项目（2022KDZ14）；土木工程防灾减灾湖北省引智创新示范基地项目（2021EJD026） |
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| 摘 要： | 不同环境因子组合和负样本选取策略对崩岗易发性评价结果存在较多不确定性。为探究其对评价结果的影响，该研究以江西省兴国县花岗岩区为例，利用地理探测器探测17个环境因子的统计量q值，根据累计q值百分比大小依次选择4、7、10和17个环境因子进行组合；利用单随机欠采样、频率比法及改进频率比法等负样本选取策略构建与正样本等量的负样本数据集；采用随机森林模型进行易发性评价，并对评价结果进行对比分析。结果表明：1）3种负样本选取策略下的模型精度随着因子数量的增加先下降再上升，考虑4个环境因子的模型AUC（area under curve）值分别为0.729、0.909和0.909，较最优环境因子组合仅相差0.020～0.038，说明考虑主控环境因子，即可得到较为理想的精度；2）通过频率比法选取的负样本数据集更具合理性；3）研究区内高和极高易发区主要分布在兴国县西南部，而极低易发区主要分布在兴国县北部及东部，这与实际情况较吻合。该研究通过探究不同环境因子组合和负样本选取策略对崩岗易发性评价的影响，可为花岗岩区崩岗的防灾减灾提供科学依据。
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| 关 键 词： | 易发性 随机森林 崩岗 地理探测器 环境因子组合 负样本选取策略 |
| 收稿时间： | 2023/7/28 0:00:00 |
| 修稿时间： | 2024/1/8 0:00:00 |
Impact of environmental factor combinations and negative sample selection on Benggang susceptibility in granite areas |
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| GUO Fei,JIANG Guanghui,HUANG Xiaohu,WANG Xiujuan,XIA Dong,CHEN Yang,LI Xiaowei.Impact of environmental factor combinations and negative sample selection on Benggang susceptibility in granite areas[J].Transactions of the Chinese Society of Agricultural Engineering,2024,40(1):199-208. |
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| Authors: | GUO Fei JIANG Guanghui HUANG Xiaohu WANG Xiujuan XIA Dong CHEN Yang LI Xiaowei |
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| Institution: | Key Laboratory of Geological Hazards on Three Gorges Reservoir Area, Ministry of Education, Yichang 443002, China;College of Civil Engineering & Architecture, China Three Gorges University, Yichang 443002, China;College of Hydraulic & Environmental Engineering, China Three Gorges University, Yichang 443002, China;School of Electronic and Information Engineering, Guangdong Ocean University, Zhanjiang 524000, China; Central-South Institute of Metallurgical Geology, Yichang 443003, China |
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| Abstract: | Benggang is one of the most severe types of soil erosion in the granite areas of southern China, due to the large erosion, strong explosiveness, and fast development speed. Accurate assessment of susceptibility is of great significance for the prevention and control of Benggang damages. In this study, different combinations of environmental factors and negative sample selection strategies were explored the impact on the assessment of Benggang susceptibility. A case study was taken from the granite area of Xingguo County, Ganzhou City, Jiangxi Province, China. A systematic detection was implemented to determine the explanatory power of 17 environmental factors on the development of Benggang using a GeoDetector (GD). According to the cumulative explanatory power percentage, 56.89%, 78.55%, 92.88%, and 100.00% were selected as the environmental factor combinations, corresponding to 4, 7, 10, and 17 environmental factors, respectively. Single random undersampling (SRU) was used to construct a negative sample dataset equal to positive samples using frequency ratio (FR). The susceptibility was calculated in the study area using automatic landslide susceptibility analysis (ALSA). Negative sample data was selected equal to positive samples in the low and extremely low susceptibility areas. The sample dataset was divided into the training and testing datasets in a 7:3 ratio. The training dataset was used to train the random forest (RF) model, and then the trained RF model was to calculate the testing dataset. The prediction accuracy of the model was evaluated to calculate the Benggang susceptibility using the receiver operating characteristic (ROC). The results show that: 1) The model accuracy under the three negative sample selection strategies decreased first and then increased with the increase of the number of factors. The area under curve (AUC) values of the model considering four environmental factors were 0.729, 0.909, and 0.909, respectively. The model accuracy was the lowest at 7 environmental factors, with the AUC values of 0.711, 0.869, and 0.893, respectively. The AUC values of the 10 environmental factors were 0.745, 0.942, and 0.919, respectively. The model accuracy was highest at 17 environmental factors, while the AUC values were 0.755, 0.947, and 0.929, respectively. There was the non-linear correlation between model accuracy and cumulative explanatory power percentage. The difference was only 0.020-0.038, although the accuracy of the model for 4 environmental factors was lower than that of 17 environmental factors. Therefore, the relatively ideal accuracy was achieved when considering the main controlling environmental factors; 2) The improved frequency ratio method was significantly improved the accuracy of the model. When the number of environmental factors was 4, the AUC values of FR and ALSA were both 0.909, and the negative samples selected by FR and ALSA were the most reasonable; When the number of environmental factors was 7, the AUC value of ALSA was 0.893, and the negative sample selected by ALSA was the most reasonable; When the environmental factors were 10 and 17, the AUC values of the FR were 0.942 and 0.947, respectively. In summary, the FR can be expected to select the most reasonable negative samples; 3) The acerage rainfall erosivity was closely related to the development of Benggang. Particularly, the acerage rainfall erosivity was ranged from 9 133.24 to 9 981.49 MJ·mm/(hm2·h·a) within the scope of the study area. The majority of high and extremely high susceptibility areas were distributed in the southwest of the study area, whereas, a small number of extremely high susceptibility areas were distributed in the central and eastern parts of Xingguo County, and the majority of extremely low susceptibility areas were distributed in the northern and eastern. This study has explored the impact of different combinations of environmental factors and negative sample selection strategies on the susceptibility assessment of Benggang. The finding can provide the scientific basis for the disaster prevention and reduction in granite areas. |
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| Keywords: | susceptibility random forest Benggang GeoDetector environmental factor combination negative sample selection strategy |
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