Application and Development Trend of Machine Learning in Landslide Intelligent Disaster Prevention and Mitigation
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摘要:
滑坡灾害易发频发、点多面广、隐蔽性强、危害严重.开展“天‒空‒地‒深”观测一体化的滑坡早期识别、易发性评价及预测预报，对于保障人民生命和财产安全，推进滑坡灾害防治能力现代化具有重要意义.目前，依靠人工解译的滑坡识别耗时耗力，采用启发式模型的滑坡易发性评价不能较好地探明环境因子之间的非线性关系，基于传统监测数据的滑坡预测预报精度较低.机器学习算法凭借其强大的非线性处理能力及鲁棒性等优势，逐渐广泛应用于滑坡智能防灾减灾中.基于此，本研究系统阐述了机器学习在滑坡灾害早期识别、易发性评价及预测预报等方面的具体应用，综述了多种机器学习算法在上述3个领域中运用的优劣，最终对机器学习在滑坡灾害中未来的发展趋势进行了展望.
Abstract:Landslide is prone to occur frequently with many aspects, wide coverage, and serious threats. Therefore, based on "satellite-aerial-surface-deep" collaborative integrated big data observations, it is of great significance to carry out early landslide identification, susceptibility assessment, and prediction to ensure the safety of people's lives and property, and to promote the modernization of landslide disaster prevention capability. At present, the landslide early identification relying on manual interpretation is time-consuming and labor-intensive, the landslide susceptibility assessment using a heuristic model cannot better prove the nonlinear relationship between environmental factors, and the landslide prediction accuracy based on single monitoring data is low. The machine learning algorithm is gradually widely used in landslide disaster prevention and mitigation because of its strong nonlinear processing ability and robustness. Based on this, the study systematically expounds on the specific application of machine learning in early identification, susceptibility assessment, and prediction of landslide disasters, summarizes the advantages and disadvantages of various machine learning algorithms in the above three fields, and finally prospects the future development trend of machine learning in landslide disasters.
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图 2 全国不同地质灾害类型占比及近11年地质灾害统计
数据由中国地质调查局提供
Fig. 2. The proportion of different types of geological disasters in China and statistics of geological disasters in recent 11 years
图 3 机器学习在滑坡灾害中应用的流程
Fig. 3. Application process of machine learning in landslide hazard
图 4 Web of Science数据库统计机器学习算法在滑坡灾害总的应用频率
Fig. 4. Application frequency of statistical machine learning algorithm of Web of Science Database in landslide disaster
图 5 2010年之后机器学习在滑坡灾害应用的中英文文章发表数量统计
Fig. 5. Statistics of Chinese and English articles on the application of machine learning in landslide hazard after 2010
图 6 机器学习在滑坡早期识别中的应用
Fig. 6. Application of machine learning in early landslide identification
图 9 基于机器学习滑坡易发性影响因素：滑坡编录库表达与地形高程不同空间分辨率(据Chang et al., 2019；Dou et al., 2020b修改)
Fig. 9. Influencing factors of landslide susceptibility based on machine learning: landslide inventory database representation and terrain DEM with different spatial resolutions (modified by Chang et al., 2019; Dou et al., 2020b)
图 10 可解释性机器学习在滑坡地质灾害中的研究框架
Fig. 10. The framework of interpretable machine learning in the landslide geohazards
图 11 “天-空-地-深”多场一体化滑坡灾害观测技术架构
Fig. 11. The technical framework of " satellite-aerial-surface-deep " multi-fields for landslide observation
图 12 滑坡地质灾害智能防灾减灾目标
Fig. 12. The aim of landslide geohazards in intelligent disaster prevention and mitigation
表 1 常用机器学习算法的对比
Table 1. Comparison of common machine learning algorithms
常用机器
学习算法主要原理 优点 缺点 逻辑回归 基于现有数据对分类边界线建立回归公式，以此进行分类 ①计算代价不高
②易于理解和实现①容易欠拟合
②分类精度可能不高决策树 基于数据属性采用树状结构建立决策模型 ①复杂度不高
②对中间值的缺失不敏感
③可以处理不相关特征数据可能会产生过度匹配问题 人工神经网络 模拟生物神经网络，是一类模式匹配算法，是机器学习的一个庞大分支，有几百种不同的算法 ①类准确度高
②学习能力强①需要大量的参数
②不能观察学习过程，结果难以解释
③学习时间长支持
向量机寻找最佳超平面，可以将数据分成两部分，每部分数据都属于同一个类别 ①泛化错误率低
②计算开销不大
③结果易解释①对参数调节和核函数的选择敏感
②原始分类器不加修改适用于处理二类问题随机森林 用随机的方式建立一个森林，森林由很多独立决策树组成，最后综合得到最优分类结果 ①可充分应用有限样本
②具备多样性和准确性的优势在某些噪音较大的问题上会过拟合 深度学习 通过组合低层特征形成更加抽象的高层表示属性类别或特征，以发现数据的分布式特征表示 ①适应性强
②学习能力强、覆盖范围广
③可移植性好①训练耗时，模型验证复杂且麻烦
②便携性差，硬件成本较高
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