| 滑坡易发性预测不确定性:环境因子不同属性区间划分和不同数据驱动模型的影响 |
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| 引用本文： | 黄发明,叶舟,姚池,李远耀,殷坤龙,黄劲松,姜清辉.滑坡易发性预测不确定性:环境因子不同属性区间划分和不同数据驱动模型的影响[J].地球科学,2020,45(12):4535-4549. |
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| 作者姓名： | 黄发明 叶舟 姚池 李远耀 殷坤龙 黄劲松 姜清辉 |
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| 作者单位： | 1.南昌大学建筑工程学院, 江西南昌 330031 |
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| 基金项目： | 国家自然科学基金项目41807285国家自然科学基金项目41762020国家自然科学基金项目51879127国家自然科学基金项目51769014江西省自然科学基金20192BAB216034江西省自然科学基金20192ACB2102江西省自然科学基金20192ACB20020教育部博士后面上基金2019M652287教育部博士后面上基金2020T130274江西省博士后面上基金2019KY08 |
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| 摘 要： | 对于滑坡易发性预测建模，连续型环境因子在频率比分析时的属性区间划分数量（attribute interval numbers，AIN）和不同易发性预测模型是两个重要不确定性因素.为研究这两个因素对建模的影响规律，以江西省上犹县为例，考虑5种连续型环境因子AIN划分（4、8、12、16及20）和5种数据驱动模型（层次分析法（analytic hierarchy process，AHP）、逻辑回归（logistic regression，LR）、BP神经网络（back-propagation neural network，BPNN）、支持向量机（support vector machine，SVM）和随机森林（random forest，RF）），总计25种不同工况下的滑坡易发性预测研究.再开展滑坡易发性指数的不确定性（包括精度评价和统计规律等）分析.结果表明:（1）对于同一模型，随着AIN值从4增加至8再到20时，易发性预测精度先逐渐提升，然后缓慢提升直至稳定；（2）对于同一AIN值，RF模型预测精度最高，其后依次为SVM、BPNN、LR和AHP模型；（3）在25种组合工况下，AIN=20和RF模型的预测精度最高，AIN=4和AHP模型精度最低，但在AIN=8和RF模型组合下的易发性建模效率较高且精度也较高；（4）更大的AIN值和更先进的机器学习模型预测出的滑坡易发性指数的不确定性相对较低，更符合实际的滑坡概率分布特征.在环境因子属性区间划分为8和RF模型工况下高效准确地构建滑坡易发性预测模型.
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| 关 键 词： | 滑坡易发性预测 不确定性分析 频率比 属性区间划分 数据驱动模型 工程地质 |
| 收稿时间： | 2020-05-28 |
Uncertainties of Landslide Susceptibility Prediction: Different Attribute Interval Divisions of Environmental Factors and Different Data-Based Models |
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| Abstract: | The attribute interval numbers (AIN) in the frequency ratio analysis of continuous environmental factors and the landslide susceptibility model are two important uncertainties affecting the results of landslide susceptibility prediction (LSP). To study the effects of the two uncertain factors on the change rules of LSP, taking Shangyou County of Jiangxi Province, China, as study area, the AIN values of the continuous environmental factors are respectively set to be 4, 8, 12, 16 and 20. Meanwhile, five different data-based models (analytic hierarchy process (AHP), logistic regression (LR), BP neural network (BPNN), support vector machines (SVM) and random forests (RF)) are selected as LSP models. Hence, there are a total of 25 types of different calculation conditions for LSP. Finally, the accuracy and uncertainties of LSP are analyzed. The results show that: (1) For a certain model, the LSP accuracy gradually increases with the AIN value increasing from 4 to 8, then slowly increases to a stable level with AIN increasing from 8 to 20; (2) For a certain AIN, the LSP accuracy of the RF model is higher than SVM, followed by the BPNN, LR and AHP models; (3) Under all the 25 calculation conditions, the prediction accuracy of AIN=20 and RF model is the highest while that of AIN=4 and AHP model is the lowest, and the modeling efficiency and accuracy of AIN=8 and RF model are very high; (4) The landslide susceptibility indexes calculated by the higher AIN and more advanced machine learning models are more consistent with the actual distribution features of landslide probability and have relatively lower uncertainties. It can be concluded that an efficient and relatively accurate LSP model can be built under the condition of AIN value of 8 and RF model. |
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