基于Fisher判别法的台风暴雨泥石流预测模型

对台风暴雨泥石流发生的可能性进行定量预测,有助于减少危险区内的人员伤亡、降低经济损失。以台湾地区南投县陈有兰溪流域的47条泥石流沟为研究对象,从泥石流形成所需的地形地貌、物源和降雨条件中,初步选取台风暴雨泥石流发生的影响因子,包括沟床平均坡度、有效流域面积、形状系数、主沟长度、岩性、崩滑比和平均雨强。根据因子重要性排序结果,选择崩滑比和平均雨强作为模型的预测因子,基于Fisher判别法建立了台风暴雨泥石流预测模型。采用随机取样技术,选取70%的数据用于构建模型,剩余30%的数据用于验证模型。以精确度、准确率、漏报率和误报率指标,定量评价模型的预测效果,并确定最优的预测模型。结果表明:基于Fisher判别法构建的台风暴雨泥石流预测模型,综合考虑了泥石流形成所需的物源条件和降雨条件,弥补了降雨阈值模型仅依靠降雨资料分析的不足,预测效果更好。     

Susceptibility mapping of gully erosion using GIS-based statistical bivariate models: a case study from Ali Al-Gharbi District,Maysan Governorate,southern Iraq

This work aims to evaluate the predictive capability of three bivariate statistical models, namely information value, frequency ratio, and evidential belief functions, in gully erosion susceptibility mapping in northeastern Maysan Governorate (Ali Al-Gharbi District) in southern Iraq. The gully inventory map, consisting of 21 gullies of different sizes, was prepared based on the interpretation of remotely sensed data supported by field survey. The gully inventory data (polygon format) were randomly partitioned into two sets: 14 gullies for build and training the bivariate model, and the remaining 7 gullies for validating purposes. Twelve gully influential factors were selected based on data availability and the literature review. The selected factors were related to lithology, geomorphology, soil, land cover, and topography (primary and secondary) settings. Analysis of factor importance using information gain ratio proved that out of 12 gully influential factors, eight were of more importance in developing gullies (the average merit was greater than zero). The most important factors and the training gully inventory map were used to generate three gully erosion susceptibility maps based on the three bivariate models used. For validation, the area under the operating characteristics curves for both success and prediction rates was used. The results indicated that the highest prediction rate of 82.9% was achieved using the information value technique. All the bivariate models had prediction rates greater than 80%, and thus they were regarded as very good estimators. The final conclusion was that the bivariate models offer advanced techniques for mapping gully erosion susceptibility.     

泥石流平均流速预测模型及敏感因子研究

为了探求泥石流平均流速敏感因子及影响因素耦合关系,本文采用BP神经网络和支持向量机模型对蒋家沟泥石流数据进行预测,对两种泥石流平均流速预测模型的学习与泛化能力进行比较,并对平均流速各影响因素的敏感程度进行分析,建立了泥石流平均流速敏感因子预测模型。结果表明:支持向量机的泛化能力优于BP网络,更适合样本数量较少的泥石流动态预测。沟道比降和不稳定层厚度是泥石流平均流速的主要影响因子,各因子之间存在复杂的耦合关系。基于不稳定层厚度和泥面比降的泥石流平均流速预测模型精度较高,能够定量描述泥石流动态与影响因子间的响应关系。研究成果可为泥石流防治提供依据。     

A test of transferability for landslides susceptibility models under extreme climatic events: application to the Messina 2009 disaster

A model building strategy is tested to assess the susceptibility for extreme climatic events driven shallow landslides. In fact, extreme climatic inputs such as storms typically are very local phenomena in the Mediterranean areas, so that with the exception of recently stricken areas, the landslide inventories which are required to train any stochastic model are actually unavailable. A solution is here proposed, consisting in training a susceptibility model in a source catchment, which was implemented by applying the binary logistic regression technique, and exporting its predicting function (selected predictors regressed coefficients) in a target catchment to predict its landslide distribution. To test the method, we exploit the disaster that occurred in the Messina area (southern Italy) on 1 October 2009 where, following a 250-mm/8-h storm, approximately two thousand debris flow/debris avalanches landslides in an area of 21 km² triggered, killing 37 people and injuring more than 100, and causing 0.5 M € worth of structural damage. The debris flows and debris avalanches phenomena involved the thin weathered mantle of the Varisican low to high-grade metamorphic rocks that outcrop in the eastern slopes of the Peloritani Mounts. Two 10-km²-wide stream catchments, which are located inside the storm core area, were exploited: susceptibility models trained in the Briga catchment were tested when exported to predict the landslides distribution in the Giampilieri catchment. The prediction performance (based on goodness of fit, prediction skill, accuracy and precision assessment) of the exported model was then compared with that of a model prepared in the Giampilieri catchment exploiting its landslide inventory. The results demonstrate that the landslide scenario observed in the Giampilieri catchment can be predicted with the same high performance without knowing its landslide distribution: we obtained, in fact, a very poor decrease in predictive performance when comparing the exported model to the native random partition-based model.     

Improved flood susceptibility mapping using a best first decision tree integrated with ensemble learning techniques

Improving the accuracy of flood prediction and mapping is crucial for reducing damage resulting from flood events. In this study, we proposed and validated three ensemble models based on the Best First Decision Tree (BFT) and the Bagging (Bagging-BFT), Decorate (Bagging-BFT), and Random Subspace (RSS-BFT) ensemble learning techniques for an improved prediction of flood susceptibility in a spatially-explicit manner. A total number of 126 historical flood events from the Nghe An Province (Vietnam) were connected to a set of 10 flood influencing factors (slope, elevation, aspect, curvature, river density, distance from rivers, flow direction, geology, soil, and land use) for generating the training and validation datasets. The models were validated via several performance metrics that demonstrated the capability of all three ensemble models in elucidating the underlying pattern of flood occurrences within the research area and predicting the probability of future flood events. Based on the Area Under the receiver operating characteristic Curve (AUC), the ensemble Decorate-BFT model that achieved an AUC value of 0.989 was identified as the superior model over the RSS-BFT (AUC = 0.982) and Bagging-BFT (AUC = 0.967) models. A comparison between the performance of the models and the models previously reported in the literature confirmed that our ensemble models provided a reliable estimate of flood susceptibilities and their resulting susceptibility maps are trustful for flood early warning systems as well as development of mitigation plans.     

甘肃省白龙江流域降雨型潜在泥石流危险性预报模型

【研究目的】泥石流灾害是白龙江流域分布广泛并常引起群死群伤的重大地质灾害,准确评价泥石流活动规模及其危险度,是泥石流危险性预警预报的前提,合理构建危险性预报模型是泥石流防灾减灾的关键。【研究方法】本文以研究区历史泥石流案例和对应降雨资料为基础数据,采用统计分析方法,通过分析形成泥石流关键地质环境条件及其相互关系,构建了白龙江流域潜在泥石流危险度定量评价模型,提出了两类泥石流危险级别临界判别模式。【研究结果】结果表明：（1）以泥石流活动规模、沟床平均比降、流域切割密度、不稳定沟床比例为判断因子的泥石流危险度动态定量计算模型,能快速准确预测未来不同工程情景和降雨频率工况下泥石流危险度;（2）影响降雨型泥石流发生的地形条件由流域面积、10°~40°斜坡坡度面积比、沟床平均纵比降等组成,降雨条件主要由泥石流爆发前的24 h累积降雨量、触发泥石流1 h降雨量或10 min降雨量等组成;（3）依据30条典型泥石流沟危险度计算结果,获得泥石流危险性临界判别值,提出了降雨型潜在泥石流危险性1 h预报模型（Ⅰ类）和10 min预报模型（Ⅱ类）,其中Ⅰ类模型高危险度以上泥石流预测精度大于87.5%,Ⅱ类模型中等危险度以上泥石流预测精度大于80%,而两类预报模型验证准确率为83.3%。【结论】研究成果为泥石流精准预警预报提供了技术支撑,对建立中小尺度泥石流实时化预警系统具有一定参考意义。创新点：通过确定与泥石流相对应关键地质环境因子,构建了泥石流危险度动态定量评价模型,依据泥石流危险性1 h和10 min临界判别模式可准确实现潜在泥石流预警预报。     

汶川震区暴雨泥石流危险范围预测研究

汶川地震发生后,灾区暴雨泥石流活动进入一个新的活跃期。根据对北川震区2008年9月24日暴雨泥石流调查,泥石流流域中地震诱发大量滑坡导致松散物源巨大,泥石流过程的洪峰流量比通常的要大数倍,应用以往泥石流危险范围预测模型进行计算的结果与实际的误差较大。因此,需要建立适用于强震区的泥石流危险范围预测方法。本文以9.24北川暴雨泥石流为典型实例,结合野外调查,利用震后高分辨航空图像和9.24暴雨后SPOT5图像分别提取泥石流发生前流域中滑坡物源储量及发生后形成的堆积扇特征数据,应用多元回归方法建立了汶川震区泥石流危险范围预测模型,该方法可用于估算泥石流最大堆积距离和堆积宽度。验证和应用结果表明:该模型适用于强震区泥石流危险范围的预测,模型方法可为震区重建中安全地段选择和未来地震区风险管理提供重要依据。     

基于沟床宽度与颗粒粒径的泥石流精细化预报模型

泥石流形成区沟床宽度和颗粒粒径对沟床起动型泥石流的发生影响很大,在强烈地震影响区内显得尤为突出,但目前的泥石流预报中还没考虑到这两个因素,无法准确预测强震区泥石流的发生.在泥石流10 min和1 h精细化预报模型基础上,通过现场调查群发泥石流事件,结合汶川地震强烈影响区泥石流的演化特点,引入了泥石流形成区沟道宽度和颗粒粒径的影响,建立了改进的精细化泥石流10 min和1 h预报模型,并在贵州望谟打易和四川德昌群发泥石流、汶川地震强烈影响区的文家沟多次泥石流事件中获得了很好的验证结果,得出泥石流形成区的颗粒粒径代表泥石流的地质因子,泥石流形成区沟床宽度代表泥石流的地形因子之一,这2个因子在泥石流发生中的作用都非常重要;改进的精细化10 min和1 h预报模型以及临界值,可以用于强烈地震区和一般的泥石流预报.      

Modeling debris flow initiation and run-out in recently burned areas using data-driven methods

In the framework of the landslide susceptibility assessment, the maps produced should include not only the landslide initiation areas, but also those areas potentially affected by the traveling mobilized material. To achieve this purpose, the susceptibility analysis must be separated in two distinct components: (1) The first one, which is also the most discussed in the literature, deals with the susceptibility to failure, and (2) the second component refers to the run-out modeling using the initiation areas as an input. Therefore, in this research we present a debris flow susceptibility assessment in a recently burned area in a mountain zone in central Portugal. The modeling of debris flow initiation areas is performed using two statistical methods: a bivariate (information value) and a multivariate (logistic regression). The independent validation of the results generated areas under the receiver operating characteristic curves between 0.91 and 0.98. The slope angle, plan curvature, soil thickness and lithology proved to be the most relevant predisposing factors for the debris flow initiation in recently burned areas. The run-out is simulated by applying two different methods: the empirical model Flow Path Assessment of Gravitational Hazards at a Regional Scale (Flow-R) and the hydrological algorithm D-infinity downslope influence (DI). The run-out modeling of the 36 initiation areas included in the debris flow inventory delivered a true positive rate of 83.5% for Flow-R and 80.5% for DI, reflecting a good performance of both models. Finally, the susceptibility map for the entire basin including both the initiation and the run-out areas in a scenario of a recent wildfire was produced by combining the four models mentioned above.     

A comparison of logistic regression-based models of susceptibility to landslides in western Colorado,USA

The Paonia-McClure Pass area of Colorado has been recognized as a region highly susceptible to mass movement. Because of the dynamic nature of this landscape, accurate methods are needed to predict susceptibility to movement of these slopes. The area was evaluated by coupling a geographic information system (GIS) with logistic regression methods to assess susceptibility to landslides. We mapped 735 shallow landslides in the area. Seventeen factors, as predictor variables of landslides, were mapped from aerial photographs, available public data archives, ETM + satellite data, published literature, and frequent field surveys. A logistic regression model was run using landslides as the dependent factor and landslide-causing factors as independent factors (covariates). Landslide data were sampled from the landslide masses, landslide scarps, center of mass of the landslides, and center of scarp of the landslides, and an equal amount of data were collected from areas void of discernible mass movement. Models of susceptibility to landslides for each sampling technique were developed first. Second, landslides were classified as debris flows, debris slides, rock slides, and soil slides and then models of susceptibility to landslides were created for each type of landslide. The prediction accuracies of each model were compared using the Receiver Operating Characteristic (ROC) curve technique. The model, using samples from landslide scarps, has the highest prediction accuracy (85 %), and the model, using samples from landslide mass centers, has the lowest prediction accuracy (83 %) among the models developed from the four techniques of data sampling. Likewise, the model developed for debris slides has the highest prediction accuracy (92 %), and the model developed for soil slides has the lowest prediction accuracy (83 %) among the four types of landslides. Furthermore, prediction from a model developed by combining the four models of the four types of landslides (86 %) is better than the prediction from a model developed by using all landslides together (85 %).     

基于高分遥感影像的泥石流潜在形成区识别方法探讨

提前对泥石流可能发生和造成影响的区域进行预测和防范,一直是地质灾害预测中的重要课题。为充分发挥国产高分影像的空间分辨率优势,利用NNDiffuse和Gram-Schmidt两种融合方法实现多光谱和全色波段的融合并作为研究数据,结合常用的支持向量机（SVM）和基于土壤亮度指数特征的动态聚类（ISODATA）两种分类方法对泥石流潜在形成区的自然地表覆盖和人类影响区域进行识别和提取,再利用泥石流隐患沟和集水区的空间和属性关系预测泥石流形成区。研究表明,不同融合方法会对泥石流形成区的预测产生影响,本文基于NNDiffuse融合方法进行预测的总体效果最好;SVM方法有最好的效果,表明先验知识对预测形成区的重要意义,但无先验知识的ISODATA方法结合有效的指数特征在泥石流形成区识别和预测中有较好的表现,预期未来能在测绘部门有很大的应用潜力。     

基于逻辑回归模型的泥石流易发性评价与检验：以金沙江上游奔子栏—昌波河段为例

基于地理信息系统(ArcGIS100)平台和小流域单元,采用逻辑回归(LR)模型对金沙江上游(奔子栏—昌波河段)干热河谷区进行泥石流易发性评价,并对预测结果进行总体检验与随机个案检验。评价与检验结果表明,得到的最优指标组合下LR评价模型的AUC值为827%;预测的极高易发区、高易发区面积合占全区面积的3598%,实发泥石流面积占泥石流总面积的6503%;在个案检验中,位于各等级分区的检验组样本实发泥石流比例随着分区易发性等级降低,依次为917%(极高)、750%(高)、364%(中等)、167%(低)、0(极低),表明评价效果良好。研究区泥石流集中发育于金沙江沿岸的东北部、中部和西南部,主导性的评价指标依次为距主干道路距离、岩性、距断裂带距离、雨季月平均降雨量。人类活动与季节性降雨为研究区干热河谷泥石流的主要诱发条件。基于逻辑回归模型的泥石流易发性评价方法提高了泥石流发生可能性的预测精度,可为干热河谷区泥石流预测预警和防治提供参考依据。     

基于CART算法的三维成矿预测研究——以安徽白象山矿区为例

把地质大数据和人工智能技术引入矿产资源定量评价及成矿预测体系中,提高了海量地质数据的有效信息挖掘,弥补了传统方法的不足。本文基于白象山矿区基础地质资料和物化探成果资料,利用三维地质体建模技术和三维空间分析技术,量化三维控矿因素,建立了一种基于CART 算法的三维成矿预测模型。通过在白象山矿区的实验表明：该模型能较好的定位已知矿体,并且预测出在已知矿体北部、东部、东北部、西部、南部和东南部具有较高的成矿概率,可圈定找矿靶区。该模型将地质大数据应用于找矿勘探工作,具有纯数据驱动、预测精度高、预测结果可靠等优点。研究发现,该模型的预测效果与训练数据集的数量、矿控因素提取、决策树深度等有关。     

乌东德地区泥石流危险范围预测模型

为了更加客观、准确地预测泥石流的危险范围,在前人研究经验和大量野外考察的基础上,选取多项泥石流重要影响因素,对乌东德地区上百条泥石流按照其堆积区规模分组进行多元回归分析,得到不同类型泥石流中各影响因素对堆积区形态参数的预测模型。通过不断改变步长的方式搜索适用于一个地区泥石流危险范围预测的数学模型,运用计算机搜索大量可能的数学模型,通过平均误差计算比较得到最优预测模型。将该方法和文献[3]的逐步回归分析方法一起应用到乌东德地区的实例上进行验证,预测结果显示,后者得到的误差要比本文方法计算的误差大很多,本文方法的预测误差为6.7%～9.2%,文献[3]方法的预测误差为10.5%～29.6%。     

Sedimentary Microfacies and Porosity Modeling of Deep-Water Sandy Debris Flows by Combining Sedimentary Patterns with Seismic Data: An Example from Unit I of Gas Field A, South China Sea

Sandy debris flow deposits are present in Unit I during Miocene of Gas Field A in the Baiyun Depression of the South China Sea. The paucity of well data and the great variability of the sedimentary microfacies make it difficult to identify and predict the distribution patterns of the main gas reservoir, and have seriously hindered further exploration and development of the gas field. Therefore, making full use of the available seismic data is extremely important for predicting the spatial distribution of sedimentary microfacies when constructing three-dimensional reservoir models. A suitable reservoir modeling strategy or workflow controlled by sedimentary microfacies and seismic data has been developed. Five types of seismic attributes were selected to correlate with the sand percentage, and the root mean square (RMS) amplitude performed the best. The relation between the RMS amplitude and the sand percentage was used to construct a reservoir sand distribution map. Three types of main sedimentary microfacies were identified: debris channels, fan lobes, and natural levees. Using constraints from the sedimentary microfacies boundaries, a sedimentary microfacies model was constructed using the sequential indicator and assigned value simulation methods. Finally, reservoir models of physical properties for sandy debris flow deposits controlled by sedimentary microfacies and seismic inversion data were established. Property cutoff values were adopted because the sedimentary microfacies and the reservoir properties from well-logging interpretation are intrinsically different. Selection of appropriate reservoir property cutoffs is a key step in reservoir modeling when using simulation methods based on sedimentary microfacies control. When the abnormal data are truncated and the reservoir properties probability distribution fits a normal distribution, microfacies-controlled reservoir property models are more reliable than those obtained from the sequence Gauss simulation method. The cutoffs for effective porosity of the debris channel, fan lobe, and natural levee facies were 0.2, 0.09, and 0.12, respectively; the corresponding average effective porosities were 0.24, 0.13, and 0.15. The proposed modeling method makes full use of seismic attributes and seismic inversion data, and also makes the property data of single-well depositional microfacies more conformable to a normal distribution with geological significance. Thus, the method allows use of more reliable input data when we construct a model of a sandy debris flow.     

Comparison of numerical models of two debris flows in the Cortina d’ Ampezzo area,Dolomites, Italy

The accurate prediction of runout distances, velocities and the knowledge of flow rheology can reduce the casualties and property damage produced by debris flows, providing a means to delineate hazard areas, to estimate hazard intensities for input into risk studies and to provide parameters for the design of protective measures. The application of most of models that describe the propagation and deposition of debris flow requires detailed topography, rheological and hydrological data that are not always available for the debris-flow hazard delineation and estimation. In the Cortina d’Ampezzo area, Eastern Dolomites, Italy, most of the slope instabilities are represented by debris flows; 325 debris-flow prone watersheds have been mapped in the geomorphological hazard map of this area. We compared the results of simulations of two well-documented debris flows in the Cortina d’Ampezzo area, carried on with two different single-phase, non-Newtonian models, the one-dimensional DAN-W and the two-dimensional FLO-2D, to test the possibility to simulate the dynamic behaviour of a debris flow with a model using a limited range of input parameters. FLO-2D model creates a more accurate representation of the hazard area in terms of flooded area, but the results in terms of runout distances and deposits thickness are similar to DAN-W results. Using DAN-W, the most appropriate rheology to describe the debris-flow behaviour is the Voellmy model. When detailed topographical, rheological and hydrological data are not available, DAN-W, which requires less detailed data, is a valuable tool to predict debris-flow hazard. Parameters obtained through back-analysis with both models can be applied to predict hazard in other areas characterized by similar geology, morphology and climate.     

Reconstructing historical seismicity from lake sediments (Lake Laffrey, Western Alps, France)

Sediment archives from a mountain lake are used as indicators of seismotectonic activity in the Grenoble area (French western Alps, 45°N). Sedimentological analysis (texture and grain-size characteristics) exhibits several layers resulting from instantaneous deposits in Lake Laffrey: six debris flow events up to 8 cm thick can be attributed to slope failure along the western flank of the basin. Dating with ²¹⁰Pb and ¹³⁷Cs gamma counting techniques and the reconnaissance of historical events, provide a constrained age-depth model. Over the last 250 years, five of such debris flow deposits could be related to historical earthquakes of MSK intensities greater than VI over an area of <60 km. One debris flow deposit triggered at the beginning of the last century can be related to an historical landslide possibly triggered by the artificial regulation of the lake level.     

Debris-flow event in the Frangerello Stream-Susa Valley (Italy)—calibration of numerical models for the back analysis of the 16 October, 2000 rainstorm

Three debris-flow simulation model software have been applied to the back analysis of a typical alpine debris flow that caused significant deposition on an urbanized alluvial fan. Parameters used in the models were at first retrieved from the literature and then adjusted to fit field evidence. In the case where different codes adopted the same parameters, the same input values were used, and comparable outputs were obtained. Results of the constitutive laws used (Bingham rheology, Voellmy fluid rheology and a quadratic rheology formulation which adds collisional and turbulent stresses to the Bingham law) indicate that no single rheological model appears to be valid for all debris flows. The three applied models appear to be capable of reasonable reproduction of debris-flow events, although with different levels of detail. The study shows how different software can be used to predict the debris-flow motion for various purposes from a first screening, to predict the runout distance and deposition of the solid material and to the different behaviour of the mixtures of flows with variation of maximum solid concentration.     

Landslide susceptibility assessment by Dempster–Shafer and Index of Entropy models,Sarkhoun basin,Southwestern Iran

Landslides are natural disasters often activated by interaction of different controlling environmental factors, especially in mountainous terrains. In this research, the landslide susceptibility map was developed for the Sarkhoun catchment using Index of Entropy (IoE) and Dempster–Shafer (DS) models. For this purpose, 344 landslides were mapped in GIS environment. 241 (70%) out of the landslides were selected for the modeling and the remaining (30%) were employed for validation of the models. Afterward, 10 landslide conditioning factor layers were prepared including land use, distance to drainage, slope gradient, altitude, lithology, distance to roads, distance to faults, slope aspect, Topography Wetness Index, and Stream Power Index. The relationship between the landslide conditioning factors and landslide inventory maps was determined using the IoE and DS models. In order to verify the models, the results were compared with validation landslide data not employed in training process of the models. Accordingly, Receiver Operating Characteristic (ROC) curves were applied, and Area Under the Curve (AUC) was calculated for the obtained susceptibility maps using the success (training data) and prediction (validation data) rate curves. The land use was found to be the most important factor in the study area. The AUC are 0.82, and 0.81 for success rates of the IoE, and DS models, respectively, while the prediction rates are 0.76 and 0.75. Therefore, the results of the IoE model are more accurate than the DS model. Furthermore, a satisfactory agreement is observed between the generated susceptibility maps by the models and true location of the landslides.     

坡面泥石流降雨预报模型

中国东部地区的地质灾害多以坡面泥石流的形式发生,预测预报坡面泥石流的发生对于开展防灾减灾具有重要意义。地形、地质和降雨三大条件是影响坡面泥石流发生的主要条件。通过选择同样地质条件和基本相同降雨条件的区域,研究影响坡面泥石流发生的地形条件,并得出可以用于坡面泥石流预报的坡面泥石流地形条件。结果表明:地形条件由坡面坡度因子、泥石流上部因子、泥石流侧面因子和临空面因子组成;较大的地形条件T对应较大的泥石流发生可能性;降雨条件由泥石流发生前的降雨量与1 h降雨量组成;得出了由地形条件T和降雨条件R组成的坡面泥石流预报条件P,P值越大,坡面泥石流发生的可能性越大。预报条件P可以预报坡面泥石流的发生。