基于ANP的模糊贝叶斯网络模型及其在海岸带地质灾害风险评价中的应用

海岸带位于海陆交互地带,其独特的地理、地质和环境条件导致其灾害地质现象发育,地质灾害易发性和危险性高。考虑到海岸带的重要经济和社会属性,开展海岸带的地质灾害风险评价显得极为重要。本文首先建立了基于模糊贝叶斯网络的地质灾害风险评价模型,结合网络层次分析法(ANP)确定模糊贝叶斯网络的条件概率,并简化了贝叶斯网络的结构图谱。在此基础上,以辽东半岛东部海岸带作为研究区,以崩塌、滑坡、地面塌陷、海岸侵蚀和海水入侵等5个主要地质灾害类型作为评价对象,开展了基于ANP-模糊贝叶斯网络模型的地质灾害易发性、危险性和风险性评价,并编制了综合地质灾害风险分布图;结果显示,区内高、较高风险区主要分布于研究区的西南部海岸带,面积为249km2,约占全区面积的9.1%。研究成果可为海岸带国土资源开发、经济建设规划、防灾减灾救灾等提供重要参考,对同类地区的海岸带地质灾害风险评价具有一定借鉴意义。     

陕西陇县地质灾害危险性分区评价

在陇县地质灾害详细调查的基础上.通过统计分析确定了各类主要影响因素,采用信息量法和定性评价方法分别进行了地质灾害的危险性评价.建立了地质灾害评价指标体系,确定了地质灾害危险性判别标准,进行了地质灾害危险区划.共划分为高危险区、中危险区、低危险区和极低危险区4个等级,在此基础上,又划分了12个亚区.其中高危险区面积为619.19 km2,占总面积的25.79%,中危险区面积为509.74km2,占总面积的21.23%,低危险区面积为711.75 km2,占总面积的29.65%,极低危险区面积为559.87 km2,占全区面积的23.32%.     

基于加权信息量模型的湖南省麻阳县地质灾害危险性评价与区划

以麻阳县1：5万地质灾害详细调查数据为基础，选择地质灾害点密度、地形地貌、岩土体结构类型、地质构造、降雨、植被、人类工程活动、受威胁人数和潜在经济损失等9个因素作为评价指标，采用层次分析法对不同指标的重要性进行排序与赋权.最后基于加权信息量模型进行麻阳县地质灾害危险性评估及分区评价.研究结果表明：麻阳县主要处于地质灾害低-中等危险区，其中低、中危险区面积分别为984.44 km²和414.08 km²，极低危险区和高危险区分别为81.11 km²和86.57 km².     

Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia

Landslides are one of the most frequent and common natural hazards in Malaysia. Preparation of landslide susceptibility maps is one of the first and most important steps in the landslide hazard mitigation. However, due to complex nature of landslides, producing a reliable susceptibility map is not easy. For this reason, a number of different approaches have been used, including direct and indirect heuristic approaches, deterministic, probabilistic, statistical, and data mining approaches. Moreover, these landslides can be systematically assessed and mapped through a traditional mapping framework using geoinformation technologies. Since the early 1990s, several mathematical models have been developed and applied to landslide hazard mapping using geographic information system (GIS). Among various approaches, fuzzy logic relation for mapping landslide susceptibility is one of the techniques that allows to describe the role of each predisposing factor (landslide-conditioning parameters) and their optimal combination. This paper presents a new attempt at landslide susceptibility mapping using fuzzy logic relations and their cross application of membership values to three study areas in Malaysia using a GIS. The possibility of capturing the judgment and the modeling of conditioning factors are the main advantages of using fuzzy logic. These models are capable to capture the conditioning factors directly affecting the landslides and also the inter-relationship among them. In the first stage of the study, a landslide inventory was complied for each of the three study areas using both field surveys and airphoto studies. Using total 12 topographic and lithological variables, landslide susceptibility models were developed using the fuzzy logic approach. Then the landslide inventory and the parameter maps were analyzed together using the fuzzy relations and the landslide susceptibility maps produced. Finally, the prediction performance of the susceptibility maps was checked by considering field-verified landslide locations in the studied areas. Further, the susceptibility maps were validated using the receiver-operating characteristics (ROC) success rate curves. The ROC curve technique is based on plotting model sensitivity—true positive fraction values calculated for different threshold values versus model specificity—true negative fraction values on a graph. The ROC curves were calculated for the landslide susceptibility maps obtained from the application and cross application of fuzzy logic relations. Qualitatively, the produced landslide susceptibility maps showed greater than 82% landslide susceptibility in all nine cases. The results indicated that, when compared with the landslide susceptibility maps, the landslides identified in the study areas were found to be located in the very high and high susceptibility zones. This shows that as far as the performance of the fuzzy logic relation approach is concerned, the results appeared to be quite satisfactory, the zones determined on the map being zones of relative susceptibility.     

Landslide Susceptibility Mapping Using Fuzzy Logic System and Its Influences on Mainlines in Lashgarak Region,Tehran, Iran

Landslide susceptibility mapping is among the useful tools applied in disaster management and planning development activities in mountainous areas. The susceptibility maps prepared in this research provide valuable information for landslide hazard management in Lashgarak region of Tehran. This study was conducted to, first, prepare landslide susceptibility maps for Lashgarak region and evaluate landslide effect on mainlines and, second, to analyze the main factors affecting landslide hazard increase in the study area in order to propose efficient strategies for landslide hazard mitigation. A GIS-based multi-criteria decision analysis model (fuzzy logic) is used in the present work for scientific evaluation of landslide susceptible areas in Lashgarak region. To this end, ArcGIS, PCIGeomatica, and IDIRISI software packages were used. Eight information layers were selected for information analysis: ground strength class, slope angle, terrain roughness, normalized difference moisture index, normalized difference vegetation index, distance from fault, distance from the river, and distance from the road. Next, eight different scenarios were created to determine landslide susceptibility of the study area using different operators (intersection (AND), union (OR), algebraic sum (SUM), multiplication (PRODUCT), and different fuzzy gamma values) of fuzzy overlay approach. After that, the performance of various fuzzy operators in landslide susceptibility mapping was empirically compared. The results revealed the excellent consistency of landslide susceptibility map prepared using the fuzzy union (OR) operator with landslide distribution map in the study area. Eventually, the accuracy of landslide susceptibility map prepared using the fuzzy union (OR) operator was evaluated using the frequency ratio diagram. The results showed that frequency values of the landslides gradually increase from “low susceptibility” to high “susceptibility” as 88.34% of the landslides are categorized into two “high” and “very high” susceptibility classes, implying the satisfactory consistency between the landslide susceptibility map prepared using fuzzy union (OR) operator and landslide distribution map.     

Landslide susceptibility mapping by using fuzzy logic: a case study of Cham-gardalan catchment,Ilam, Iran

Landslides are introduced as regional movements, which influence different engineering structures such as roads, railways, and dams and cause the person’s death. Identification of landslide zones may decrease the financial losses and human injuries or deaths. This study tries to achieve a landslide susceptibility mapping in Cham-gardalan catchment by weighting the main criteria and the membership functions of fuzzy logic. For this, we applied the best relationship function between the presence and absence of landslides as well as a collection of the elements. At first, the landslide points were identified by the means of some components those of satellite images, topographical (1:50,000) and geographical (1:100,000) maps, field visits, and Google Earth software followed by the preparation of landslide distribution maps. Then, all effective landslide factors such as percentage of slope, slope aspect, height, geology, land uses, distance from roads, distance from drainages, distance from breakage, and precipitation map have been utilized in order to conduct the fuzzy analyses. Landslide susceptibility map was performed by fuzzy operators (Gamma, Product, Sum, Or, And) in the study area. After fuzzificating and weighting, the effective criteria of landslides were determined through fuzzy Gamma operators with the landaus of 0.2, 0.5, 0.8, and 0.9 and by comparing final maps for making an appropriate model of landslide susceptibility mapping. The regional susceptibility map represents the landslide-prone areas in five categories those of very low, low, moderate, high, and very high. Our results indicated that among the applied operators, Gamma with landau of 0.9 can be used as an appropriate method for mapping the landslide susceptibility due to the suitable fuzzification of given criteria based on landslide distribution maps. In addition, the elements of road, percentage of slope, distance from drainage, and geology were recognized as the most important factors for occurring the landslides.     

The application of GIS-based bivariate statistical methods for landslide hazards assessment in the upper Tons river valley,Western Himalaya,India

Landslides are one of the most frequent and common natural hazards in many parts of Himalaya. To reduce the potential risk, the landslide susceptibility maps are one of the first and most important steps in the landslide hazard mitigation. Earth observation satellite and geographical information system-based techniques have been used to derive and analyse various geo-environmental parameters significant to landslide hazards. In this study, a bivariate statistics method was used for spatial modelling of landslide susceptibility zones. For this purpose, thematic layers including landslide inventory, geology, slope angle, slope aspect, geomorphology, slope morphology, drainage density, lineament and land use/land cover were used. A large number of landslide occurrences have been observed in the upper Tons river valley area of Western Himalaya. The result has been used to spatially classify the study area into zones of very high, high, moderate, low and very low landslide susceptibility zones. About 72% of active landslides have been observed to occur in very high and high hazard zones. The result of the analysis was verified using the landslide location data. The validation result shows significant agreement between the susceptibility map and landslide location. The result can be used to reduce landslide hazards by proper planning.     

Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach

. Regional landslide susceptibility assessments pose complex problems. To solve these problems, numerous approaches, such as statistical analysis, geotechnical engineering approach, geomorphologic approach and fuzzy logic, have been employed. However, all the available methods for regional landslide susceptibility assessments have some uncertainties due to a lack of knowledge and variability. Minimizing these uncertainties provides realistic approaches. Use of the fuzzy logic approach to produce a landslide susceptibility map of a landslide-prone area in NW Turkey is the main purpose of the present study. For this purpose, the study includes five main stages, these being the preparation of a landslide inventory of the study area, the application of factor analysis, the extraction of fuzzy if-then rules, the use of a geographical information system, and the control of the reliability of the resulting landslide susceptibility map. Slope angle, slope aspect, land use, weathering depth, water conditions and topographical elevation were considered as landslide conditioning factors for the study area. A total of 23 if-then rules was extracted from the field data. Employing these rules, fuzzified index maps representing each parameter were obtained. Finally, combining these maps, the landslide susceptibility map of the area was prepared. When compared with the landslide susceptibility map, the landslides identified in the area were found to be located in the very high- and high-susceptibility zones. As far as the performance of the fuzzy approach for processing is concerned, the images appear to be quite satisfactory, the zones determined on the map being zones of relative susceptibility.     

南水北调工程北京段PCCP管道施工沿线的地质灾害与防治

重大城市线性工程施工可能会诱发或面临一系列的地质灾害,对其灾害特征、危害及防治方法进行研究具有重要意义.以南水北调工程北京段PCCP管道施工为例,在分析其地质环境背景、地质灾害类型、基本特征、危害性及影响范围的基础上.归纳总结了针对不同类型地质灾害所采用的施工防治措施和方法.重点对施工沿线主要地质灾害如边坡崩塌、特殊地基处理和地面沉降做了深入的探讨.研究成果对PCCP管道后续工程建设和工程运营维护具有一定的现实意义.是对线性工程地质灾害防治进行的有益的探索.     

黄土高原西南部陇县—千阳一带崩塌、滑坡地质灾害的遥感影像特征及其分布规律

渭北高原西段新构造活动强烈,崩塌、滑坡地质灾害频繁发生。通过1：5万专题遥感解译和重点地质调查,全区遥感解译崩塌、滑坡地质灾害点共计611处,其中大型滑坡17处、中型滑坡194处、小型滑坡385处,小型崩塌15处。结合区域地理、地质环境特征,将区内大、中型滑坡地质灾害点划分为5个集中分布区、6个集中分布带。区内崩塌、滑坡地质灾害主要受地形地貌、新构造与岩性条件的控制,以地质灾害点集中分布区、带为基础,参考重点地质灾害点的影响范围及人类活动的状况,将千阳县城关镇—崔家头镇、陇县杜阳镇—千阳县草碧镇、陇县峡口河—杨河沟地区列为地质灾害防治监测重点地区。     

Landslide susceptibility mapping of Tevankarai Ar sub-watershed,Kodaikkanal taluk,India, using weighted similar choice fuzzy model

This paper deals with the landslide susceptibility zonation of Tevankarai Ar sub-watershed using weighted similar choice fuzzy method in a GIS environment. There has been a rapid increase in landslide occurrences in the Kodaikkanal town and area surrounding the town specially in the settlements around the town and road links leading to and from the town. This necessitates a detailed study of slope instability problems in this area. It is observed that these incidences occur frequently during the monsoon and summer showers. Rainfall is identified as the prime triggering factor. Eleven physical factors that cause instability are identified as causative factors from the field investigations and landslide occurrences. Land use pattern, slope gradient, curvature and aspect, weathering index which are evaluated from the weathering ratios of different chemical constituents of the three major lithological variations, soil type, hydraulic conductivity of soil and soil thickness, geomorphology, drainage, and lineament have been utilized to prepare the spatial variation. A weighted similar choice fuzzy model which ranks a set of alternatives by identifying the similarity between the outcome of alternatives and outcome of ideal alternatives is used to rank the causative factors. Each causative factor is classified into sub-categories and rated based on their effect on stimulating the landslide event using qualitative judgment derived from field studies and landslide history. The prepared thematic maps of causative factors are integrated, utilizing the GIS software Arcmap. The outcome has projected the low, moderate, high, and very high landslide susceptibility zones. The high-hazard and very high-hazard areas fall in the northwestern part characterized by croplands and agricultural plantations, while the moderate hazard zones are seen in prominent settlements and low-hazard zones are observed in the sparse settlements and zones of less agricultural activity. The model is verified using the relative landslide density (R) index, and the susceptibility map is found to be consistent with the mapped landslide incidences. The results from this study illustrate that the use of weighted similar choice fuzzy method is suitable for landslide susceptibility mapping on regional scale in growing hill towns as Kodaikkanal town.     

Combining neural network with fuzzy,certainty factor and likelihood ratio concepts for spatial prediction of landslides

The landslide studies can be categorized as pre- and postdisaster studies. The predisaster studies include spatial prediction of potential landslide zones known as landslide susceptibility zonation (LSZ) mapping to identify the areas/locales susceptible to landslide hazard. The LSZ maps provide an assessment of the safety of existing habitations and infrastructural/functional elements and help plan further developmental activities in the hilly regions. Landslides are one of the natural geohazards that affect at least 15% of land area of India. Different types of landslides occur frequently in geodynamical active domains of the Himalayas. In India, various techniques have been developed and adopted for LSZ mapping of different regions. However, the technique for LSZ mapping is not yet standardized. The present research is an attempt in this direction only. In our earlier work (Kanungo et al. 2006), a detailed study on conventional, artificial neural network (ANN)- black box-, fuzzy set-based and combined neural and fuzzy weighting techniques for LSZ mapping in Darjeeling Himalayas has been documented. In this paper, other techniques such as combined neural and certainty factor concept along with combined neural and likelihood ratio techniques have been assessed in comparison with combined neural and fuzzy technique for the preparation of LSZ maps of the same study area in parts of Darjeeling Himalayas. It is observed from the present study that the LSZ map produced using combined neural and fuzzy approach appears to be the most accurate one as in this case only 2.3% of the total area is found to be categorized as very high susceptibility zone and contains 30.1% of the existing landslide area. This approach can serve as one of the key objective approaches for spatial prediction of landslide hazards in hilly terrain.     

南水北调工程北京段PCCP管道施工沿线的地质灾害与防治

重大城市线性工程施工可能会诱发或面临一系列的地质灾害,对其灾害特征、危害及防治方法进行研究具有重要意义。以南水北调工程北京段PCCP管道施工为例,在分析其地质环境背景、地质灾害类型、基本特征、危害性及影响范围的基础上,归纳总结了针对不同类型地质灾害所采用的施工防治措施和方法。重点对施工沿线主要地质灾害如边坡崩塌、特殊地基处理和地面沉降做了深入的探讨。研究成果对PCCP管道后续工程建设和工程运营维护具有一定的现实意义,是对线性工程地质灾害防治进行的有益的探索。     

Comparison of the implementation of rock engineering system and analytic hierarchy process methods,upon landslide susceptibility mapping,using GIS: a case study from the Eastern Achaia County of Peloponnesus,Greece

As landslides are very common in Greece, causing serious problems to the social and economic welfare of many communities, the implementation of a proper hazard analysis system will help the creation of a reliable susceptibility map. Τhis will help local communities to define a safe land use and urban development. The purpose of this study is to compare the implementation of two semi-quantitative landslide assessment approaches, using landslide susceptibility maps compiled in a GIS environment. The compared methods are rock engineering system (RES) and the analytic hierarchy process (AHP). For the landslide susceptibility analysis, the Northeastern part of the Achaia County was examined. This area suffers from many landslides, because of its neighborhood with the tectonically active Corinthian Gulf and its geological setting (Neogene sediments, flysch and other bedrock formations, with local overthrusts). Ten parameters were used in both methodologies, and each one was separated into five categories ranging from 0 to 4, representing their specific conditions derived from the investigation of the landslides in the western part of the study area (ranking area). A layer map was generated for each parameter, using GIS, while the weighting coefficients of each methodology were used for the compilation of RES and AHP final maps of the eastern part of the study area (validating area). By examining these two maps, it is revealed that even though both correctly show the landslide status of the second site, the RES map reveals a better behavior in the spatial distribution of the various landslide susceptibility zones.     

基于改进频率比法的川藏铁路沿线及邻区地质灾害易发性分区评价

川藏铁路是我国目前正在规划建设的重要铁路干线之一,地处地形和地质条件极为复杂的青藏高原东部,复杂的地质背景与脆弱的地质环境造成川藏铁路沿线及邻区地质灾害极为发育,严重威胁着川藏铁路的规划建设。在对地质灾害易发性评价方法分析的基础上,首先对传统的地质灾害易发性评价频率比方法进行改进,克服了传统通用方法中频率比值分布的不连续性,提高了各地质灾害影响因子敏感性的区分度,并减小了因子分级的主观性。利用ROC曲线与空间熵的定量对比验证表明,改进频率比法的地质灾害易发性评价模型优于传统方法。根据地质灾害的发育分布特征,选取地面高程、地形坡度、地形坡向、地形曲率、地形起伏度、工程地质岩组、地震动峰值加速度、断裂密度、水系距离、道路距离、降水量与植被指数等影响地质灾害的主要因素,结合地质灾害调查数据,首先分析各影响因子的地质灾害敏感性,并进一步对川藏铁路沿线及邻区的地质灾害易发性进行评价和分区。评价结果表明,研究区地质灾害的发育分布主要受控于断裂、水系和道路等线状要素,以及地形坡度和地形起伏度等地形地貌因素,并且断裂密度和地形起伏度相较其他因子具有更大的地质灾害敏感性区分度。地质灾害极高易发区和高易发区主要分布于大型水系两岸、道路两侧的高山河谷沿线的狭窄地带,使沿河谷与已有道路规划展布的川藏铁路面临着严重的地质灾害威胁,铁路规划建设部门应加强该地带的地质灾害排查、防治和线路优化工作。     

基于GIS技术和信息量法的地质灾害易发性研究——以海南省昌江县为例

海南省昌江县地质灾害的发育受地质环境条件控制和多种外在因素影响.依据昌江县地质灾害详细调查资料,选择各类地质灾害发育的控制条件和诱发因素作为评价指标,通过建立地质灾害影响因子体系,构建信息量分析模型.通过对各项因子进行量化并使其归一化,运用层次分析法确定评价因子权重系数,利用ArcGIS的空间叠加分析功能,实现对昌江县...     

甘肃省舟曲县"8.8"特大山洪泥石流灾害与气象成因分析及其应对建议

通过调查舟曲地质灾害历史资料.分析降雨与地质灾害的相关性,发现地质灾害与灾害前1～3 h累积降水量、月平均降水量和月暴雨频数明显正相关.然后分析舟曲"8.8"特大山洪泥石流的气象和降水成因.在此基础上,提出舟曲地质灾害的防治措施.     

Landslides susceptibility mapping based on geographical information system,GuiZhou, south-west China

The purpose of this study is to assess the susceptibility of landslides around the area of Guizhou province, in south-west of China, using a geographical information system (GIS). The base map is prepared by visiting the field area and mapping individual landslide at a scale of 1:500,000 topographic maps. In the study, slope, lithology, landslide inventory, tectonic activity, drainage distribution and annual precipitation were taken as independent causal factors. Therefore, six causal factors maps are prepared by collecting information from various authorized sources and converting them in to GIS maps. The susceptibility assessment is based on the qualitative map combination model and trapezoidal fuzzy number weighting (TFNW) approach. Using a predicted map of probability, the study area was classified into four categories of landslide susceptibility: low, moderate, high and very high. In addition, the weighting procedure showed that the TFNW is an efficient method for landslide causal factors weighting.     

Landslide hazard zonation using analytical hierarchy process along National Highway-3 in mid Himalayas of Himachal Pradesh,India

Landslide zonation studies emphasize on preparation of landslide hazard zonation maps considering major instability factors contributing to occurrence of landslides. This paper deals with geographic information system-based landslide hazard zonation in mid Himalayas of Himachal Pradesh from Mandi to Kullu by considering nine relevant instability factors to develop the hazard zonation map. Analytical hierarchy process was applied to assign relative weightages over all ranges of instability factors of the slopes in study area. To generate landslide hazard zonation map, layers in geographic information system were created corresponding to each instability factor. An inventory of existing major landslides in the study area was prepared and combined with the landslide hazard zonation map for validation purpose. The validation of the model was made using area under curve technique and reveals good agreement between the produced hazard map and previous landslide inventory with prediction accuracy of 79.08%. The landslide hazard zonation map was classified by natural break classifier into very low hazard, low hazard, moderate hazard, high hazard and very high landslide hazard classes in geographic information system depending upon the frequency of occurrence of landslides in each class. The resultant hazard zonation map shows that 14.30% of the area lies in very high hazard zone followed by 15.97% in high hazard zone. The proposed model provides the best-fit classification using hierarchical approach for the causative factors of landslides having complex structure. The developed hazard zonation map is useful for landslide preparedness, land-use planning, and social-economic and sustainable development of the region.     

Landslide Hazard Zonation using Remote Sensing and GIS: a case study of Dikrong river basin, Arunachal Pradesh, India

Landslides are among the most costly and damaging natural hazards in mountainous regions, triggered mainly under the influence of earthquakes and/or rainfall. In the present study, Landslide Hazard Zonation (LHZ) of Dikrong river basin of Arunachal Pradesh was carried out using Remote Sensing and Geographic Information System (GIS). Various thematic layers namely slope, photo-lineament buffer, thrust buffer, relative relief map, geology and land use / land cover map were generated using remote sensing data and GIS. The weighting-rating system based on the relative importance of various causative factors as derived from remotely sensed data and other thematic maps were used for the LHZ. The different classes of thematic layers were assigned the corresponding rating value as attribute information in the GIS and an “attribute map” was generated for each data layer. Each class within a thematic layer was assigned an ordinal rating from 0 to 9. Summation of these attribute maps were then multiplied by the corresponding weights to yield the Landslide Hazard Index (LHI) for each cell. Using trial and error method the weight-rating values have been re-adjusted. The LHI threshold values used were: 142, 165, 189 and 216. A LHZ map was prepared showing the five zones, namely “very low hazard”, “low hazard”, “moderate hazard”, “high hazard” and “very high hazard” by using the “slicing” operation.