| 基于特征筛选与差分进化算法优化的滑坡危险性评估方法 |
| |
| 引用本文： | 周侯伯,肖桂荣,林炫歆,尹玉环.基于特征筛选与差分进化算法优化的滑坡危险性评估方法[J].地球信息科学,2022,24(12):2373-2388. |
| |
| 作者姓名： | 周侯伯 肖桂荣 林炫歆 尹玉环 |
| |
| 作者单位： | 1.福州大学 数字中国研究院（福建），福州 3501082.福州大学 空间数据挖掘与信息共享教育部重点实验室，福州 350108 |
| |
| 基金项目： | 中央引导地方科技发展专项(2020L3005);中国科学院A类战略性先导科技专项(XDA23100504) |
| |
| 摘 要： | 突发性地质灾害危险性评估对灾害防治与风险管理具有重要意义。由于不同地区影响灾害发生的因子各不相同，实际评估过程中难以全面客观地选取适宜的评估因子。机器学习对处理灾害系统的高维非线性问题独具优势，但因模型难以调优而评估效果有限。本文尝试提出一种双向优化的滑坡危险性评估方法：在构建因子敏感性指数开展定量敏感性分析的基础上，结合重要性分析、相关性分析、共线性分析构建四维(Four-Dimensional, 4D)特征筛选法用于评估因子综合优选；为克服模型难以调优的问题，引入差分进化(Differential Evolution, DE)算法优化支持向量机(Support Vector Machine, SVM)与多层感知机(Multi-Layer Perceptron, MLP) 2种推广能力较强的机器学习模型。最后，以福建省滑坡为例，开展评估方法研究。研究表明：4D特征筛选法能更加客观全面地选取适宜性更高的危险性评估因子，从而降低数据维度、减少信息冗余以提升评估模型性能；DE算法对SVM与MLP具有显著的优化效果，有益于增强模型滑坡危险性的评估准确度，DE-SVM、DE-MLP相较于未优化前模型的AUC值分别提升了4.43%与4.37%；基于双向优化的滑坡危险性评估结果表明，降雨与土地利用类型对福建省滑坡发生具有重要影响作用，福建省滑坡极高危险区普遍年均降雨较高、地形复杂多变，极低危险区主要位于东南沿海一带及闽江流域两侧。本研究为滑坡危险性评估中的影响因子客观选取与机器学习模型调优提供了一定思路。
|
| 关 键 词： | 滑坡 危险性 因子敏感性指数 四维特征筛选法 差分进化算法 支持向量机 多层感知机 福建省 |
| 收稿时间： | 2022-04-04 |
Landslide Hazard Assessment Method based on Feature Screening and Differential Evolution Algorithm Optimization |
| |
| ZHOU Houbo,XIAO Guirong,LIN Xuanxin,YIN Yuhuan.Landslide Hazard Assessment Method based on Feature Screening and Differential Evolution Algorithm Optimization[J].Geo-information Science,2022,24(12):2373-2388. |
| |
| Authors: | ZHOU Houbo XIAO Guirong LIN Xuanxin YIN Yuhuan |
| |
| Institution: | 1. The Academy of Digital China (Fujian), Fuzhou University, Fuzhou 350108, China2. Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350108, China |
| |
| Abstract: | Hazard assessment of sudden geological disasters is of great significance for disaster prevention and risk management. Due to different factors affecting the occurrence of disasters in different regions, it is difficult to select appropriate factors comprehensively and objectively in an actual evaluation process. Machine learning has unique advantages in dealing with high-dimensional nonlinear problems of disaster systems, but its evaluation performance is limited because the model is difficult to tune. This paper attempted to propose a two-way optimization method for landslide hazard assessment. Based on a factor sensitivity index built for quantitative sensitivity analysis, combining importance analysis, correlation analysis, and collinearity analysis, and following the principle of “guarantee sensitivity, retain importance, eliminate correlation, and avoid collinearity", a four-dimensional (4D) feature screening method was constructed to evaluate the comprehensive optimization of factors. In order to overcome the problem that the model is difficult to tune, the Differential Evolution (DE) algorithm was further introduced. Two machine learning models with strong generalization ability, i.e., Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP), were optimized. Finally, we took the landslide in Fujian Province as an example to verify the proposed evaluation method. We found that the 4D feature screening method can more objectively and comprehensively select suitable hazard assessment factors, thereby reducing the data dimension and reducing information redundancy to improve the performance of the assessment model. Ten suitability assessment factors were finally used for landslide hazard assessment in Fujian Province including aspect, variance coefficient in elevation, land use type, average annual rainfall, surface cutting depth, distance to river, distance to road, engineering geological rock group, topographic wetness index, and stream power index. The DE algorithm can obtain better hyperparameters from global search and has a significant optimization effect on SVM and MLP, which is beneficial to improve the evaluation accuracy of the landslide hazard of the model. Compared with the unoptimized models, the AUC values of DE-SVM and DE-MLP increased by 4.43% and 4.37%, respectively. The results of landslide hazard assessment based on two-way optimization show that rainfall and land use types have an important impact on the occurrence of landslides in Fujian Province. Terrain curvature elements, terrain variability elements, and fault structures have little impact on landslide occurrence. The extremely high-hazard areas generally have high annual rainfall and complex and changeful terrain. The extremely low-hazard areas are mainly located along the southeast coast and on both sides of the Minjiang River Basin. This research provides some ideas for objective selection of influencing factors in landslide hazard assessment and machine learning model tuning. |
| |
| Keywords: | landslide hazard four-dimensional feature screening method factor sensitivity index differential evolution algorithm Support Vector Machine Multi-Layer Perceptron Fujian |
|
| 点击此处可从《地球信息科学》浏览原始摘要信息 |
| 点击此处可从《地球信息科学》下载免费的PDF全文 |
|
