| 基于贝叶斯优化机器学习超参数的滑坡易发性评价 |
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| 引用本文： | 杨灿,刘磊磊,张遗立,朱文卿,张绍和.基于贝叶斯优化机器学习超参数的滑坡易发性评价[J].地质科技通报,2022,41(2):228-238. |
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| 作者姓名： | 杨灿 刘磊磊 张遗立 朱文卿 张绍和 |
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| 基金项目： | 国家自然科学基金青年基金项目41902291湖南省自然科学基金项目2020JJ5704湖南省研究生创新基金项目CX20200236中南大学中央高校基本科研业务费专项资金项目2020zzts651 |
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| 摘 要： | 利用机器学习模型进行滑坡易发性评价时, 不同的超参数设置往往会导致评价结果的不同。采用贝叶斯算法对4种常见机器学习模型(逻辑回归LR、支持向量机SVM、人工神经网络ANN和随机森林RF)的超参数进行了优化, 探索了该算法对滑坡易发性机器学习模型的优化效果。以湘中地区4县(安化县、新华县、桃江县和桃源县)滑坡易发性评价为例说明该算法的可行性与适用性。基于滑坡历史编录, 确定研究区内1 017个滑坡点, 并选定15个滑坡影响因子, 以此构建滑坡易发性模型的训练集和测试集。利用贝叶斯优化算法对4种机器学习模型的主要超参数进行了优化, 依据优化后的超参数建立了4种优化模型, 并使用AUC值等指标来比较其预测能力。结果表明: 经超参数优化后的4种机器学习模型预测性能均有所提高, 且基于贝叶斯优化的随机森林模型表现最好。
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| 关 键 词： | 滑坡 易发性评价 湘中地区 机器学习 超参数优化 贝叶斯 |
| 收稿时间： | 2021-06-23 |
Machine learning based on landslide susceptibility assessment with Bayesian optimized the hyperparameters |
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| Abstract: | In machine learning-based landslide susceptibility assessment, there are some differences in the evaluation results obtained by using different hyperparameters. This paper aims to use the Bayesian algorithm to optimize the hyperparameters of four common machine learning models (logistic regression, support vector machine, artificial neural network and random forest) and to explore the optimization effect of this algorithm. Taking the landslide susceptibility assessment of four counties (Anhua, Xinhua, Taojiang, and Taoyuan Counties) in central Hunan as an example, the feasibility and applicability of the algorithm are illustrated. Based on the landslide inventory, 1 017 landslide points in the study area were determined, and 15 landslide influencing factors were selected to construct the training set and test set. The Bayesian optimization algorithm is used to optimize the main hyperparameters of the four machine learning models, and four optimal models are established according to the optimized hyperparameters. The AUC value and other indicators are used to compare the predictive ability of different models. The results show that ① the prediction performance of the hyperparameters optimized models is better than that of the unoptimized models. ② Among the four optimization models, the coupling model of the random forest and Bayesian optimization algorithm has the best prediction performance. |
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