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| 1 | Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management显示文摘Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments, but most studies use GIS-based classification methods to conduct susceptibility zonation.This study presents a machine learning approach based on the C5.0 decision tree(DT) model and the K-means cluster algorithm to produce a regional landslide susceptibility map. Yanchang County, a typical landslide-prone area located in northwestern China, was taken as the area of interest to introduce the proposed application procedure. A landslide inventory containing 82 landslides was prepared and subsequently randomly partitioned into two subsets: training data(70% landslide pixels) and validation data(30% landslide pixels). Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means cluster algorithm. The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC) curve) of the proposed model was the highest, reaching 0.88,compared with traditional models(support vector machine(SVM) = 0.85, Bayesian network(BN) = 0.81,frequency ratio(FR) = 0.75, weight of evidence(WOE) = 0.76). The landslide frequency ratio and frequency density of the high susceptibility zones were 6.76/km^(2) and 0.88/km^(2), respectively, which were much higher than those of the low susceptibility zones. The top 20% interval of landslide occurrence probability contained 89% of the historical landslides but only accounted for 10.3% of the total area.Our results indicate that the distribution of high susceptibility zones was more focused without containing more ' stable' pixels. Therefore, the obtained susceptibility map is suitable for application to landslide risk management practices. | Zizheng Guo Yu Shi Faming Huang Xuanmei Fan Jinsong Huang | 2021 | Geoscience Frontiers2021，12，6： | 12 |
| 2 | 考虑线状环境因子适宜性和不同机器学习模型的滑坡易发性预测建模规律显示文摘对于滑坡易发性预测中的水系、公路和断层等线状环境因子,现有研究大多采用缓冲分析提取距离线状因子的距离。但缓冲分析得到的线距离属于离散型变量,带有大小不等的随机波动性且对点或线要素的误差较为敏感,导致滑坡易发性建模精度下降。提出了使用水系和公路的空间密度等连续型变量改进线状环境因子的适宜性。以江西省安远县为例,选取高程、地形起伏度、距水系和公路距离等14个环境因子(原始因子),再将距水系和公路距离2个线状因子改进为水系密度和公路密度(改进因子);之后采用逻辑回归、多层感知器、支持向量机和C5.0决策树等机器学习模型,分别构建了基于原始因子和改进因子的机器学习模型以预测滑坡易发性;最后利用ROC曲线和易发性指数分布特征等来研究建模规律。结果表明:①改进因子机器学习预测精度均高于原始因子机器学习模型,表明空间密度对于易发性预测的适宜性更好;②在4类机器学习模型中C5.0模型对于滑坡易发性预测性能最好,其次是SVM、MLP和LR;③水系和公路两类环境因子的重要性较高且使用改进因子机器学习后这两类环境因子重要性排名依然非常靠前。 | 黄发明 李金凤 王俊宇 毛达雄 盛明强 | 2022 | 地质科技通报2022，41，2： | 6 |
| 3 | 机器学习方法在滑坡易发性评价中的应用显示文摘中国山区多、地形复杂,构造发育、地质灾害隐患分布广泛。滑坡作为山区最具灾难性的地质灾害之一,严重威胁着人民群众的生命及财产安全。构建滑坡易发性模型能够量化滑坡发生的可能性,对制定防灾措施、减少潜在风险具有重要作用。由于经验驱动模型难以量化,且往往依赖主观判断,近年来,滑坡易发性模型的精度与准确度在从经验驱动和统计理论模型向新兴机器学习方向发展的过程中得到提升。对目前滑坡易发性评价常用的机器学习模型进行综合评述,并针对三峡库区的案例研究,对不同的机器学习技术进行广泛分析和比较。机器学习模型通过结合实地调查资料和历史数据,可绘制滑坡易发性地图,辅助制定滑坡减缓策略。根据滑坡易发性预测模型的准确性和效率,评价几种常用算法的优势和局限性。结果表明,与一些常用的滑坡易发性制图方法相比,基于树结构的集成算法模型性能更好。此外,高质量的数据库十分重要,深度学习算法的更多应用还有待进一步研究探索。 | 马彦彬 李红蕊 王林 仉文岗 朱正伟 杨海清 王鲁琦 袁兴中 | 2022 | 土木与环境工程学报(中英文)2022，44，1： | 5 |
| 4 | 基于GEE的徐州市土地利用分类研究显示文摘及时、准确地获取土地利用信息,可为城市发展和生态环境保护提供参考依据.基于谷歌地球引擎(Google earth engine)云平台,联合Sentinel-1 SAR数据、Sentinel-2 MSI高分辨率光学影像数据、SRTM高程数据等多源数据构建分类特征集,利用随机森林算法对徐州市2021年的土地利用类型进行分类,并对分类结果进行精度评价.研究结果表明:1)地物的光谱特征,尤其是归一化水体指数(NDWI)对分类结果贡献较大,多特征集的分类精度明显高于单一光谱特征;2)综合利用地物光谱特征、纹理特征、地形特征和雷达后向散射特征进行随机森林分类,分类精度最高,达93.55%,水体和耕地的分类效果明显优于裸地和建设用地;3)徐州市的主要土地利用类型为耕地和建设用地,两者面积占比达92.44%,水体主要分布于铜山区和新沂市,林草地和裸地分布较少. | 郭羽羽 胡召玲 | 2023 | 江苏师范大学学报(自然科学版)2023，41，1： | 1 |
| 5 | 联合光学和SAR遥感影像的山区公路滑坡易发性评价方法显示文摘艰险山区公路的滑坡易发性评价能够为公路地质选线提供关键支撑信息。传统滑坡易发性评价方法存在忽略地表形变等动态数据的使用而导致评价结果精度不高的问题。针对此问题,该文提出一种联合光学和SAR遥感影像的山区公路滑坡易发性评价方法。以青海省沿黄公路隆务峡至公伯峡段为研究区,先利用高分辨率QuickBird卫星影像提取多种滑坡灾害静态因子,并采用随机森林模型计算路线区域内的滑坡易发性风险初始等级;然后基于长时间序列的Sentinel-1A影像,获取直接反映滑坡动态变化的地表形变因子;最后,利用地表形变因子对滑坡易发性风险初始等级进行修正,得到最终的滑坡易发性评价分区图。工程实践表明,该方法综合利用滑坡灾害静态与动态因子数据,所获取的山区公路滑坡易发性评价分区图更具准确性,可为后续的公路地质选线提供准确信息。 | 余绍淮 徐乔 余飞 | 2023 | 自然资源遥感2023，35，4： | 0 |
| 6 | 川西高山峡谷地区震后滑坡演化趋势研究显示文摘汶川地震引发的同震滑坡给川西高山峡谷地区人民带来了巨大威胁。震后在强降雨的影响下,新生滑坡及古滑坡活动加剧,分析震后滑坡的演化趋势,对重点地区及时开展监测预警显得尤为重要。以汶川县东北部为研究区,基于GIS平台选取岩性、距断层距离、PGA、高程、坡度、坡向、剖面曲率、地形起伏度、年最大24 h降雨等9个影响因子,利用滑坡频率密度、面积密度和数量密度,统计分析2009年、2011年、2015年、2021年4期滑坡时空演化特征;同时结合证据权重(WOE)、随机森林(RF)、证据权重-随机森林(WOE-RF)模型,开展研究区滑坡易发性演化趋势分析。结果表明:随时间推移,滑坡数量、面积和规模均大幅减小,已处于较低水平;滑坡时空演化以2015年为转折由南向北发展;经ROC验证,WOE-RF得到的滑坡易发性精度最高,且三种模型均显示滑坡极高易发区演化也呈远离震中的由南向北之势。研究结果为川西高山峡谷地区滑坡灾害早期识别与监测预警提供理论依据。 | 倪章 常鸣 唐亮亮 向兰兰 徐恒志 | 2023 | 灾害学2023，38，2： | 0 |
| 7 | Modeling landslide susceptibility based on convolutional neural network coupling with metaheuristic optimization algorithms显示文摘Landslides are one of the most common geological hazards worldwide,especially in Sichuan Province(Southwest China).The current study's main,purposes are to explore the potential applications of convolutional neural networks(CNN)hybrid ensemble metaheuristic optimization algorithms,namely beluga whale optimization(BWO)and coati optimization algorithm(COA),for landslide susceptibility mapping in Sichuan Province(China).For this aim,fourteen landslide conditioning factors were compiled in a spatial database.The effectiveness of the conditioning factors in the development of the landslide predictive model was quantified using the linear support vector machine model.The receiver operating characteristic(ROC)curve(AUC),the root mean square error,and six statistical indices were used to test and compare the three resultant models.For the training dataset,the AUC values of the CNN-COA,CNN-BWO and CNN models were 0.946,0.937 and 0.855,respectively.In terms of the validation dataset,the CNN-COA model exhibited a higher AUC value of 0.919,while the AUC values of the CNN-BWO and CNN models were 0.906 and 0.805,respectively.The results indicate that the CNN-COA model,followed by the CNN-BWO model,and the CNN model,offers the best overall performance for landslide susceptibility analysis. | Zhuo Chen Danqing Song | 2023 | International Journal of Digital Earth2023，16，1： | 0 |
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