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1.
Tung-Chiung Chang 《Natural Hazards》2007,42(1):209-224
A number of methods for prediction of debris flows have been studied. However, the successful prediction ratios of debris
flows cannot always maintain a stable and reliable level. The objective of this study is to present a stable and reliable
analytical model for risk degree predictions of debris flows. This study proposes an Artificial Neural Networks (ANN) model
that was constructed by seven significant factors using back-propagation (BP) algorithm. These seven factors include (1) length
of creek, (2) average slope, (3) effective watershed area, (4) shape coefficient, (5) median size of soil grain, (6) effective
cumulative rainfall, and (7) effective rainfall intensity. A total of 171 potential cases of debris flows collected in eastern
Taiwan were fed into the ANN model for training and testing. The average ratio of successful prediction reaching 99.12% demonstrates
that the presented ANN model with seven significant factors can provide a highly stable and reliable result for the prediction
of debris flows in hazard mitigation and guarding systems. 相似文献
2.
The application of genetic algorithm in debris flows prediction 总被引：4，自引：0，他引：4
Debris flows caused serious loss of human lives and damages to properties in Taiwan for the past decades. A number of methods
for prediction of debris flows have been studied including numerical method, statistic method, experiment method and neural
network method in recent years. This study proposed a genetic algorithm (GA) model for occurrence prediction of debris flows.
A total of 154 potential cases of debris flows collected in eastern Taiwan were fed into the GA for training and testing.
The average ratio of successful prediction reaching 90.4% demonstrates that the presented GA model can provide a stable and
reliable result for prediction of debris flows in the hazard mitigation and guarding system. 相似文献
3.
Hazard assessment model for debris flow prediction 总被引：4，自引：3，他引：1
Debris flow disasters have plagued Taiwan in recent decades, and caused casualties and destruction of property. Several methods,
including the numerical method, statistical method, and experimental method, have been adopted in recent years to predict
debris flow, and more recently, the neural network (NN) and the genetic algorithm (GA) methods have been introduced to simulate
the occurrence of debris flows. This study proposes using the GA to weigh seven important variables according to principles
similar to natural selection. The study then simultaneously inputs these variables into a NN model to predict debris flow
occurrences based on relevant factors. There were 154 potential cases of debris flow collected from eastern Taiwan and fed
into the model for testing. The average ratio of successful prediction reached 94.94%, which demonstrates that the proposed
model can provide stable and reliable results for predicting debris flow in hazard mitigation and guard systems. 相似文献
4.
Taiwan is a mountainous country, so there is an ever present danger of landslide disasters during the rainy seasons or typhoons. This study aims to develop a fuzzy-rule-based risk assessment model for debris flows and to verify the accuracy of risk assessment so as to help related organizations reduce losses caused by debris flows. The database is comprised of information from actual cases of debris flows that occurred in the Hualien area of Taiwan from 2007 to 2008. The established models can assess the likelihood of the occurrence of debris flows using computed indicators, verify modeling errors, and make comparisons between the existing models for practical applications. In the establishment of a fuzzy-based debris flow risk assessment model, possible for accounting it on the basis of far less information regarding a real system and the information can be of an uncertain, fuzzy or inexact character, the influential factors affecting debris flows include the average terrain slope, catchment area, effective catchment area, accumulated rainfall, rainfall intensity, and geological conditions. The results prove that the risk assessment model systems are quite suitable for debris flow risk assessment, with a resultant ratio of success 96?% and a normalized relative error 4.63?%. 相似文献
5.
Many debris flows were triggered in the Chenyulan River Watershed in Taiwan in a rainstorm caused by the Typhoon Toraji. There are 117 gullies with a significant steep topography in the catchment. During this Typhoon, debris flows were initiated in 43 of these gullies, while in 34 gullies, it was not certain whether they have occurred. High-intensity short-duration rainfall was the main triggering factor for these gully type debris flows which are probably entrained by a “fire hose” mechanism. Previous research identified 47 factors related to topography, geology, and hydrology, which may play a role in the formation of gully type debris flows. For a better understanding of the probability of the formation of debris flows, it is proposed to represent the factors related to topography, geology, and hydrology by one single factor. In addition to the existing topographic and geological factor, a normalized critical rainfall factor is suggested with an effective cumulative precipitation and a maximum hourly rainfall intensity. In this paper, a formation model for debris flows is proposed, which combines these topographic, geological, and hydraulic factors. A relationship of these factors with a triggering threshold is proposed. The model produces a good assessment of the probability of occurrence of debris flows in the study area. The model may be used for the prediction of debris flows in other areas because it is mostly based on the initiation mechanisms and not only on the statistical analyses of a unique variety of local factors. The research provides a new and exciting way to study the occurrence of debris flows initiated by a “fire hose” mechanism. 相似文献
6.
Bin Yu Yuan Zhu Tao Wang Yuanjing Chen Yunbo Zhu Yongbo Tie Ke Lu 《Natural Hazards》2014,74(2):1141-1161
Many debris flows were triggered within and also outside the Dayi area of the Guizhou Province, China, during a rainstorm in 2011. High-intensity short-duration rainfall was the main triggering factor for these gully-type debris flows which are probably triggered by a runoff-induced mechanism. A revised prediction model was introduced for this kind of gully-type debris flows with factors related to topography, geology, and hydrology (rainfall) and applied to the Wangmo River catchment. Regarding the geological factor, the “soft lithology” and “loose sediments” in the channel were added to the list of the average firmness coefficient for the lithology. Also, the chemical weathering was taken into account for the revised geological factor. Concerning the hydrological factor, a coefficient of variation of rainfall was introduced for the normalization of the rainfall factor. The prediction model for debris flows proposed in this paper delivered three classes of the probability of debris flow occurrence. The model was successfully validated in debris flow gullies with the same initiation mechanism in other areas of southwest China. The generic character of the model is explained by the fact that its factors are partly based on the initiation mechanisms and not only on the statistical analyses of a unique variety of local factors. The research provides a new way to predict the occurrence of debris flows initiated by a runoff-induced mechanism. 相似文献
7.
贵州省望谟县2011年6月6日暴发了特大山洪泥石流,其中暴雨中心所在的打易镇多处暴发泥石流。短历时强降雨激发了沟床起动类型的泥石流。本文通过对贵州望谟河流域群发泥石流的调查,得出该流域的66条沟中,22条沟无沟床起动类型泥石流暴发,25条沟暴发沟床起动类型泥石流,还有19条沟无法确定是否有沟床起动类型泥石流暴发。在前期工作基础上,提出了地质条件和降水条件因子的改进方法;并在前期工作的3大条件（地形条件、地质条件和降水条件）之间的关系基础上,由贵州望谟群发泥石流数据得出改进沟床起动类型泥石流的临界值,提出了泥石流的预报模型。本文模型在我国西南地区的泥石流验证中非常成功,为泥石流的预报提供了一个新方法。预报模型中的地形因子和地质因子还可以判断泥石流流域的暴发频率,为正确地判断泥石流流域的特征打下了基础。预报模型也可以估算泥石流的暴发规模,为定量地预测泥石流危害范围提供了依据。 相似文献
8.
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。 相似文献
9.
泥石流平均流速预测模型及敏感因子研究 总被引：1，自引：0，他引：1
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。 相似文献
10.
Several giant debris flows occurred in southwestern China after the Wenchuan earthquake, causing serious casualties and economic losses. Debris flows were frequently triggered after the earthquake. A relatively accurate prediction of these post-seismic debris flows can help to reduce the consequent damages. Existing debris flow prediction is almost based on the study of the relationship between post-earthquake debris flows and rainfall. The relationship between the occurrence of post-seismic debris flows and characteristic rainfall patterns was studied in this paper. Fourteen rainfall events related to debris flows that occurred in four watersheds in the Wenchuan earthquake area were collected. By analyzing the rainfall data, characteristics of rainfall events that triggered debris flows after the earthquake were obtained. Both the critical maximum rainfall intensity and average rainfall intensity increased with the time. To describe the critical conditions for debris flow initiation, intensity–duration curves were constructed, which shows how the threshold for triggering debris flows increased each year. The time that the critical rainfall intensities of debris flow occurrences return to the value prior to the earthquake could not be estimated due to the absent rainfall data before the earthquake. Rainfall-triggering response patterns could be distinguished for rainfall-induced debris flows. The critical rainfall patterns related to debris flows could be divided on the basis of antecedent rainfall duration and intensity into three categories: (1) a rapid triggering response pattern, (2) an intermediate triggering response pattern, and (3) a slow triggering response pattern. The triggering response patterns are closely related to the initiation mechanisms of post-earthquake debris flows. The main difference in initiation mechanisms and difference in triggering patterns by rainfall is regulated by the infiltration process and determined by a number of parameters, such as hydro-mechanical soil characteristics, the thickness of the soil, and the slope gradient. In case of a rapid triggering response rainfall pattern, the hydraulic conductivity and initial moisture content are the main impact factors. Runoff erosion and rapid loading of solid material is the dominant process. In case of a rainfall pattern with a slow triggering response, the thickness and strength of the soil, high hydraulic conductivity, and rainfall intensity are the impact factors. Probably slope failure is the most dominant process initiating debris flows. In case of an intermediate triggering response pattern, both debris flow initiation mechanisms (runoff erosion and slope failure) can play a role. 相似文献
11.
针对沟床起动型泥石流的诱发因素为高强度短历时的降雨,提出10 min降雨强度是这类泥石流暴发的关键。在1 h预报模型的基础上,基于云南蒋家沟的多年泥石流观测资料,修正了1 h预报模型的降雨参数,并得到了10 min降雨预报模型。10 min降雨预报模型在中国西部的其他流域,如云南浑水沟、贵州望谟县打易区域泥石流沟、四川三滩沟、四川雅安陆王沟和干溪沟、甘肃柳湾沟、甘肃马槽沟等流域的验证中,取得了较好的结果。10 min降雨预报模型是部分建立在泥石流的形成机理上的模型,并不是完全的统计模型,因此该模型也可以用于其他地区的沟床起动类型泥石流预报。 相似文献
12.
【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。 相似文献
13.
汶川地震后,地震灾区泥石流具有暴发临界雨量小,规模大,危险性高的特点。在考虑降雨和地震作用下,采用灰色关联法分析北川县72条泥石流沟的泥石流规模、流域面积、主沟长度、流域相对高差、流域切割密度、不稳定沟床比、年均降雨量和地震烈度8个影响因子的权重,在此基础上建立震区泥石流危险性评价模型并进一步对其进行危险性评价。结果表明:影响因子中,年均降雨量和地震烈度所占权重最大; 运用本文模型得到的评价结果与刘希林模型基本一致,但危险度值相对提高,其中有7条泥石流沟危险度提高一个等级。 相似文献
14.
中巴经济走廊泥石流活动性分析 ——以中巴公路奥布段为例 总被引：1，自引：0，他引：1
中巴经济走廊内的中巴公路奥布段泥石流频发且类型复杂,严重影响着安全出行和贸易流通。在对中巴公路奥布段沿线泥石流沟谷纵剖面形态分析的基础上,揭示其形态指数特征和活动程度,并从区域地形、地质和气象等因素方面探讨了泥石流的活动性差异成因及危害性。研究发现：公路沿线泥石流类型主要包括冰川型和降雨型两种,冰川型泥石流为27条,降雨型为26条。冰川型泥石流活动性强烈,形态指数N ≥ 1的沟谷占冰川型沟谷总数的81%,多数沟谷形态呈下凹状;降雨型泥石流活动性相对较弱,形态指数N ≥ 1的沟谷占其总数的50%,沟谷形态多呈上凸状。研究区大落差地形、不同物源供给和充沛水源条件等对泥石流的发育和活动具有重要影响,也是不同类型泥石流活动性差异的控制因素。研究结果可为研究区泥石流预测和防治提供指导,也可为中巴经济走廊区内交通工程选线和泥石流防治提供参考。 相似文献
15.
Chia-Nan Liu Jia-Jyun Dong Yih-Ferng Peng Hsiao-Fung Huang 《Engineering Geology》2009,104(3-4):241-253
Typhoon Herb in 1996 caused widespread debris flows in central Taiwan. The 7.3 Chi-Chi earthquake on September 21, 1999, which also took place in central Taiwan, induced many landslides in the region. These landslides turned into debris flows when Typhoon Toraji struck Taiwan in 2001. This research selects three regions which suffered a ground motion class of 5, 6, and 7 on the Richter scale during the Chi-Chi earthquake as study areas. Air photos from 1997 and 2001 of these regions are used to map the gully-type debris flows that took place after Typhoons Herb and Toraji, respectively. The gullies adjacent to the debris flow, but without a trace of debris flows, are also mapped as the non-debris flow data. The topography, hydrogeology, and rainfall factors – where debris flow occurred and in which there was no occurrence of debris flows in these gullies were retrieved from DTM, geological maps, and iso-countour maps, and of rainfall through GIS processing. These characteristic are introduced into a probabilistic neural network to build a predicting model for the probability of the occurrence of debris flows. Three series of cross analyses are conducted to compare the probability of the occurrence of debris flows of the same dataset predicted by different prediction models. The results reveal that the susceptibility of debris flows was elevated after the Chi-Chi earthquake struck. The upsurge of susceptibility was more obvious for the regions that received a higher class of ground motion. 相似文献
16.
Prediction method of debris flow by logistic model with two types of rainfall: a case study in the Sichuan, China 总被引：3，自引：3，他引：0
Debris flow is a serious disaster that frequently happens in mountainous area. This study presents an effective method for forecasting it by rainfall, which is one of the important components for prediction. The Sichuan Province is taken as an example. The geographic information system (GIS) is chosen as a tool to estimate the precipitation of hazard point, and use of statistical technique is made to calculate attenuation coefficient of effective antecedent precipitation. With such methodologies, the logistic regression model is used to comparatively establish the prediction model of two forms rainfall combination: (1) intraday rainfall and 10-day previous rainfall, (2) intraday rainfall and two types of effective antecedent rainfall which are short-time-heavy rainfall and long-time-light rainfall. The results indicate that the location of debris flows and the distribution of rainfall are factors interrelated. Secondly, the contribution rate of intraday rainfall is the highest. Thirdly, the second form rainfall combination has a higher prediction accuracy, 2.3% for short-time-heavy rainfall and 2.1% for long-time-light rainfall, which suggests that a moderate improvement is achieved by the rainfall classification. 相似文献
17.
泥石流是一种多发的地质灾害,常对人民生命财产安全带来极大的威胁,其暴发不仅与降雨有关,还与众多地质环境因子相关。本文以流域面积、松散物质比率、沟床平均坡度为地质因子,以最大小时雨强（T）和总降雨量（R）的乘积作为降雨指数,在获取的泥石流地质因子和降雨指数因子综合样本库的基础上,采用遗传规划法建立了泥石流临界降雨指数智能预测模型,克服了以往以雨量为单一指标的预警模型的弊端,模型验证结果显示,泥石流预测精度高、适应性强。 相似文献
18.
Debris flow prediction models based on environmental factors and susceptible subarea classification in Sichuan, China 总被引：1，自引：0，他引：1
Wenbo Xu Wenjuan Yu Shaocai Jing Zhaoxian Wang Guoping Zhang Jianxi Huang 《Natural Hazards》2013,67(2):869-878
Environmental factors account for the occurrence of debris flow, as well as different weights of subareas with different risk levels. Considering the relationship between debris flow and rainfall (including the intraday rainfall and the effective rainfall of the previous 10 days), seven environmental factors, including elevation, slope, aspect, flow accumulation, vegetation coverage, soil, and land use, were added in this study. The whole area of Sichuan Province was divided into subareas according to different risk levels. Debris flow prediction models were then established by using a logistic regression model. Results showed that the prediction accuracy was decreased approximately by 3 % after the environmental factors were introduced to the entire study area. The prediction accuracy of the prediction models that comprised the introduced environmental factors was increased by 22.2, 9.7, and 14.3 % in different susceptible areas (moderately susceptible, highly susceptible, very highly susceptible), respectively, compared with that of the prediction models in which rainfall was only considered. Therefore, the research method that introduced the environmental factors may be used to improve the accuracy of debris flow prediction models based on susceptible area classification. 相似文献
19.
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. 相似文献
20.
Impacts of September 21, 1999 Chi-Chi earthquake on the characteristics of gully-type debris flows in central Taiwan 总被引：3，自引：1，他引：2
Debris flows are more frequent in central Taiwan, because of its mountainous geography. For example, many debris flows were
induced by Typhoon Herb in 1996. The Chi-Chi earthquake with a magnitude of 7.3, which took place in 1999 in central Taiwan,
induced many landslides in this region. Some landslides turned into debris flows when Typhoon Toraji struck Taiwan in 2001.
This study investigates the characteristics of the gullies where debris flows have occurred for a comparison. Aerial photos
of these regions dated in 1997 (before the earthquake) and 2001 (after the earthquake) are used to identify the occurrence
of gully-type debris flows. A Geographic Information System (GIS) is applied to acquire hydrological and geomorphic characteristics:
stream gradient, stream length, catchment gradient, catchment area, form factor, and geology unit of these gullies. These
characteristics in different study regions are presented in a statistical approach. The study of how strong ground motion
affects the debris flows occurrence is conducted. The characteristics of the debris flow gullies triggered by typhoons before
and after the Chi-Chi earthquake are quantitatively compared. The analysis results show that a significant transformation
in the characteristics was induced by the Chi-Chi earthquake. In general, the transformation points out a lower hydrological
and geomorphic threshold to trigger debris flows after the Chi-Chi earthquake. The susceptibility of rock units to strong
ground motion is also examined. The analysis of debris flow density and accumulated rainfall in regions of different ground
motion also reveal that the rainfall threshold decreases after the Chi-Chi earthquake. 相似文献