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1.
This study constructs a hazard map for ground subsidence around abandoned underground coal mines (AUCMs) at Samcheok City in Korea using a probability (frequency ratio) model, a statistical (logistic regression) model, and a Geographic Information System (GIS). To evaluate the factors related to ground subsidence, an image database was constructed from a topographical map, geological map, mining tunnel map, Global Positioning System (GPS) data, land use map, lineaments, digital elevation model (DEM) data, and borehole data. An attribute database was also constructed from field investigations and reports on the existing ground subsidence areas at the study site. Nine major factors causing ground subsidence were extracted from the probability analysis of the existing ground subsidence area: (1) depth of drift; (2) DEM and slope gradient; (3) groundwater level, permeability, and rock mass rating (RMR); (4) lineaments and geology; and (5) land use. The frequency ratio and logistic regression models were applied to determine each factor’s rating, and the ratings were overlain for ground subsidence hazard mapping. The ground subsidence hazard map was then verified and compared with existing subsidence areas. The verification results showed that the logistic regression model (accuracy of 95.01%) is better in prediction than the frequency ratio model (accuracy of 93.29%). The verification results showed sufficient agreement between the hazard map and the existing data on ground subsidence area. Analysis of ground subsidence with the frequency ratio and logistic regression models suggests that quantitative analysis of ground subsidence near AUCMs is possible.  相似文献   

2.
用光学遥感数据和地理信息系统(GIS)分析了马来西亚Selangor地区的滑坡灾害。通过遥感图像解译和野外调查,在研究区内确定出滑坡发生区。通过GIS和图像处理,建立了一个集地形、地质和遥感图像等多种信息的空间数据库。滑坡发生的因素主要为:地形坡度、地形方位、地形曲率及与排水设备距离;岩性及与线性构造距离;TM图像解译得到的植被覆盖情况;Landsat图像解译得到的植被指数;降水量。通过建立人工神经网络模型对这些因素进行分析后得到滑坡灾害图:由反向传播训练方法确定每个因素的权重值,然后用该权重值计算出滑坡灾害指数,最后用GIS工具生成滑坡灾害图。用遥感解译和野外观测确定出的滑坡位置资料验证了滑坡灾害图,准确率为82.92%。结果表明推测的滑坡灾害图与滑坡实际发生区域足够吻合。  相似文献   

3.
Hazard maps of ground subsidence around abandoned underground coal mines (AUCMs) in Samcheok, Korea, were constructed using fuzzy ensemble techniques and a geographical information system (GIS). To evaluate the factors related to ground subsidence, a spatial database was constructed from topographic, geologic, mine tunnel, land use, groundwater, and ground subsidence maps. Spatial data, topography, geology, and various ground-engineering data for the subsidence area were collected and compiled in a database for mapping ground-subsidence hazard (GSH). The subsidence area was randomly split 70/30 for training and validation of the models. The relationships between the detected ground-subsidence area and the factors were identified and quantified by frequency ratio (FR), logistic regression (LR) and artificial neural network (ANN) models. The relationships were used as factor ratings in the overlay analysis to create ground-subsidence hazard indexes and maps. The three GSH maps were then used as new input factors and integrated using fuzzy-ensemble methods to make better hazard maps. All of the hazard maps were validated by comparison with known subsidence areas that were not used directly in the analysis. As the result, the ensemble model was found to be more effective in terms of prediction accuracy than the individual model.  相似文献   

4.
This paper summarizes findings of landslide hazard analysis on Penang Island, Malaysia, using frequency ratio, logistic regression, and artificial neural network models with the aid of GIS tools and remote sensing data. Landslide locations were identified and an inventory map was constructed by trained geomorphologists using photo-interpretation from archived aerial photographs supported by field surveys. A SPOT 5 satellite pan sharpened image acquired in January 2005 was used for land-cover classification supported by a topographic map. The above digitally processed images were subsequently combined in a GIS with ancillary data, for example topographical (slope, aspect, curvature, drainage), geological (litho types and lineaments), soil types, and normalized difference vegetation index (NDVI) data, and used to construct a spatial database using GIS and image processing. Three landslide hazard maps were constructed on the basis of landslide inventories and thematic layers, using frequency ratio, logistic regression, and artificial neural network models. Further, each thematic layer’s weight was determined by the back-propagation training method and landslide hazard indices were calculated using the trained back-propagation weights. The results of the analysis were verified and compared using the landslide location data and the accuracy observed was 86.41, 89.59, and 83.55% for frequency ratio, logistic regression, and artificial neural network models, respectively. On the basis of the higher percentages of landslide bodies predicted in very highly hazardous and highly hazardous zones, the results obtained by use of the logistic regression model were slightly more accurate than those from the other models used for landslide hazard analysis. The results from the neural network model suggest the effect of topographic slope is the highest and most important factor with weightage value (1.0), which is more than twice that of the other factors, followed by the NDVI (0.52), and then precipitation (0.42). Further, the results revealed that distance from lineament has the lowest weightage, with a value of 0. This shows that in the study area, fault lines and structural features do not contribute much to landslide triggering.  相似文献   

5.
This paper presents landslide susceptibility analysis around the Cameron Highlands area, Malaysia using a geographic information system (GIS) and remote sensing techniques. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys. Topographical, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten landslide occurrence factors were selected as: topographic slope, topographic aspect, topographic curvature and distance from drainage, lithology and distance from lineament, soil type, rainfall, land cover from SPOT 5 satellite images, and the vegetation index value from SPOT 5 satellite image. These factors were analyzed using an advanced artificial neural network model to generate the landslide susceptibility map. Each factor’s weight was determined by the back-propagation training method. Then, the landslide susceptibility indices were calculated using the trained back-propagation weights, and finally, the landslide susceptibility map was generated using GIS tools. The results of the neural network model suggest that the effect of topographic slope has the highest weight value (0.205) which has more than two times among the other factors, followed by the distance from drainage (0.141) and then lithology (0.117). Landslide locations were used to validate the results of the landslide susceptibility map, and the verification results showed 83% accuracy. The validation results showed sufficient agreement between the computed susceptibility map and the existing data on landslide areas.  相似文献   

6.
Ground subsidence around abandoned underground coal mines can cause much loss of life and property. We analyze factors that can affect ground subsidence around abandoned mines in Jeongahm in Kangwon-do by sensitivity analysis in geographic information system (GIS). Spatial data for the subsidence area, topography and geology and various ground engineering data were collected and used to make a factor raster database for a ground subsidence hazard map. To determine the importance of extracted subsidence-related factors, frequency ratio model and sensitivity analysis were employed. Sensitivity analysis is a method for comparing the combined effects of all factors except one. Sensitivity analysis and its verification showed that using all factors provided 91.61% accuracy. The best accuracy was achieved by not considering the groundwater depth (92.77%) and the worst by not considering the lineament (85.42%). The results show that the distance from the lineament and the distance from the drift highly affected the occurrence of ground subsidence, and the groundwater depth, land use and rock mass rating had the least effects. Thus, we determined causes of ground subsidence in the study area and this information could help in the prediction of ground subsidence in other areas.  相似文献   

7.
The purpose of this study was to develop techniques for landslide susceptibility using artificial neural networks and then to apply these to the selected study area at Janghung in Korea. Landslide locations were identified from interpretation of satellite images and field survey data, and a spatial database of the topography, soil, forest, and land use. Thirteen landslide-related factors were extracted from the spatial database. These factors were then used with an artificial neural network to analyze landslide susceptibility. Each factor's weight was determined by the back-propagation training method. Five different training sets were applied to analyze and verify the effect of training. Then the landslide susceptibility indices were calculated using the back-propagation weights, and susceptibility maps were constructed from Geographic Information System (GIS) data for the five cases. Landslide locations were used to verify results of the landslide susceptibility maps and to compare them. The artificial neural network proved to be an effective tool for analyzing landslide susceptibility.  相似文献   

8.
Ground subsidence triggered by salt mining from deposits located beneath the city of Tuzla (Bosnia and Herzegovina) is one of the major dangers acting on a very densely urbanized area since 1950, when the salt deposit exploitation by means of boreholes began. As demonstrated in this paper, subsidence induced several hazard factors such as severe ground deformations, the arising of deep and superficial fractures and a very fast water table rise, connected with the brine extraction, now affecting several districts. The above mentioned factors have been quantified by the use of geomatic methodologies, including field surveys and analysis of geographical data. In order to estimate the historical sinking rates, authors processed the large (and never before processed) amount of topographical data collected during two periods; from 1956 to 1991, and from 1992 to 2003, with only poor data collected. Afterward, traditional surveys were completely and definitively stopped. The analysis reveals a cumulative subsidence as high as 12 m during the whole period, causing damage to buildings and infrastructures within an area that includes a large portion of the historical town, at present almost entirely destroyed. Modern sinking rates have been monitored with static GPS whereas the presence of superficial fractures monitored with kinematic GPS. Factors related to the presence of deep fractures and water table rise have been evaluated by curvature analysis techniques and piezometric data respectively. Finally, hazard factors have been combined in a risk map using the GIS (Geographical Information System) map algebra capabilities and a simple multicriteria decision analysis (MDA). In order to do that, a vulnerability map has been derived on the basis of information reported on a couple of recently sensed high resolution satellite imageries. The final risk, arisen from the combination of single hazard factors and vulnerability map, highlights critical scenarios and unsuspected threatening that are under consideration by the local decision makers and urban planners. In particular, as highlighted in the final risk map, the present-day water table rise, triggered by the decrease in brine pumping, is seriously posing a threat to a portion of the city which is not the most involved in ground deformations.  相似文献   

9.
Coal mining subsidence is a common human geological disaster that was particularly conspicuous in China. It seriously restricts the sustainable development of mining areas, and it not only damages land resources but also triggers a series of ecological and environmental problems that may result in social and economic issues. This report studied the coal mining subsidence area of Longkou in Shandong province and uses digital elevation data (DEM) of the mining area before subsidence in 1978 as the baseline elevation. Through image algorithms, we obtained coal mining subsidence region data for 1984, 1996, 2000, and 2004. And with spatial data sources of the same period of TM/ETM+ and SPOT5 remote sensing images, BP artificial neural network (BPNN) classification is used to extract surface landscape information in the subsidence area. With the support of GIS technology, superimposing subsidence area on the surface landscape—using the largest landscape ecology patch index, landscape shape index, landscape condensation index, and the index of landscape distribution—report analyzes the mining landscape changes before and after subsidence. This study also carries on exploratory research with the landscape changes, thereby providing a scientific basis for integrated prevention and treatment.  相似文献   

10.
遗传算法优化BP网络在滑坡灾害预测中的应用研究   总被引：1，自引：0，他引：1       下载免费PDF全文
在陕西省宝鸡市附近长寿沟地区滑坡详细调查和遥感解译的基础上,完成了1∶10000滑坡编目图。通过使用GIS的水文分析功能,运用正反DEM技术,将长寿沟地区划分为216个自然斜坡单元,其中包括123个滑坡单元和93个未发生滑坡单元,分析滑坡发生与坡高、坡度、坡向、坡形、人类工程活动和水文地质条件影响因子之间的统计规律。利用经遗传算法优化后的BP神经网络对80个滑坡样本和40个未滑坡样本进行训练学习,然后再利用训练好的网络对预测样本进行评价分析。结果表明:43个已滑坡单元中只有3个被误判为无滑坡,正确率为9302%,53个未滑坡单元中有10个被预测为滑坡,正确率为8113%,总体正确率为8646%。通过对被预测为滑坡的10个斜坡单元进行分析,发现这些单元在坡形、坡高等影响因素的组合上已经具备了发生滑坡的条件,虽然目前没有发生滑坡,但作为潜在的滑坡危险区,可以为滑坡灾害预测预报和防灾减灾工作提供参考。  相似文献   

11.
The likelihood ratio, logistic regression, and artificial neural networks models are applied and verified for analysis of landslide susceptibility in Youngin, Korea, using the geographic information system. From a spatial database containing such data as landslide location, topography, soil, forest, geology, and land use, the 14 landslide-related factors were calculated or extracted. Using these factors, landslide susceptibility indexes were calculated by likelihood ratio, logistic regression, and artificial neural network models. Before the calculation, the study area was divided into two sides (west and east) of equal area, for verification of the models. Thus, the west side was used to assess the landslide susceptibility, and the east side was used to verify the derived susceptibility. The results of the landslide susceptibility analysis were verified using success and prediction rates. The verification results showed satisfactory agreement between the susceptibility map and the existing data on landslide locations.  相似文献   

12.
In this paper, we evaluate the predictive performance of an adaptive neuro-fuzzy inference system (ANFIS) using six different membership functions (MF). In combination with a geographic information system (GIS), ANFIS was used for land subsidence susceptibility mapping (LSSM) in the Marand plain, northwest Iran. This area is prone to droughts and low groundwater levels and subsequent land subsidence damages. Therefore, a land subsidence inventory database was created from an extensive field survey. Areas of land subsidence or areas showing initial signs of subsidence were used for training, while one-third of inventory database were reserved for testing and validation. The inventory database randomly divided into three different folds of the same size. One of the folds was chosen for testing and validation. Other two folds was used for training. This process repeated for every fold in the inventory dataset. Thereafter, land subsidence related factors, such as hydrological and topographical factors, were prepared as GIS layers. Areas susceptible to land subsidence were then analyzed using the ANFIS approach, and land subsidence susceptibility maps were created, whereby six different MFs were applied. Lastly, the results derived from each MF were validated with those areas of the land subsidence database that were not used for training. Receiver operating characteristics (ROC) curves were drawn for all LSSMs, and the areas under the curves were calculated. The ROC analyses for the six LSSMs yielded very high prediction values for two out of the six methods, namely the difference of DsigMF (0.958) and GaussMF (0.951). The integration of ANFIS and GIS generally led to high LSSM prediction accuracies. This study demonstrated that the choice of training dataset and the MF significantly affects the results.  相似文献   

13.
This study is aimed at the evaluation of the hazard of soil erosion and its verification at Boun, Korea, using a Geographic Information System (GIS) and remote sensing. Precipitation, topographic, soil, and land use data were collected, processed, and constructed into a spatial database using GIS and remote sensing data. Areas that had suffered soil erosion were analysed and mapped using the Universal Soil Loss Equation (USLE). The factors that influence soil erosion are rainfall erosivitiy (R) from the precipitation database, soil erodibility (K) from the soil database, slope length and steepness (LS) from the topographic database, and crop and management (C) and conservation supporting practices (P) from the land use database. Land use was classified from Landsat Thematic Mapper satellite images. The soil erosion map verified use of the landslide location data. Landslide locations were identified in the Boun area from interpretation of aerial photographs and field surveys.  相似文献   

14.
GIS地表塌陷计算的有限棱柱法及三维数据模型   总被引：2，自引：0，他引：2  
具有强大信息管理和可视化功能的GIS已在地下硐室开挖、矿山采掘、边坡等许多工程领域中得到了广泛应用。笔者给出了一种用于地下开挖变形描述和应力分析的方法——有限棱柱法，并就该方法与GIS集成中的数据模型和数据组织方法进行了探讨，建立了基于GIS的虚拟地质体地下开挖变形描述与评价计算的三维可视化系统，并对多语言集成模式和数据可视化方法进行了探讨。同时还给出了该方法的工程应用实例，对该方法的可靠性和有效性进行了验证。  相似文献   

15.
岩溶地面塌陷的影响因素很多,发展过程也复杂。在众多的对岩溶地面塌陷的评价方法中,神经网络具有自学习、自适应与高度非线性映射的特点,是一种非常有效的评价手段。在徐州岩溶石地面塌陷的评价中,成功地运用了人工神经网络技术,它具有的强大非线性映射能力,能够建立评价因子和评价对象之间的关系,正确选取评价因子,避免主观判断取值,从而得出可靠的预测模型和岩溶塌陷危险性分区图。  相似文献   

16.
运城市地面沉降SBAS-InSAR监测和敏感性GIS分析   总被引：1，自引：0，他引：1  
以地裂缝分布密集的运城市典型地面沉降区为研究区,采用小基线SBAS-DInSAR算法,利用覆盖该区域的8幅ASAR影像进行干涉处理,获得该区域地面沉降信息,初步揭示了研究区地面沉降的空间分布特征。在此基础上,搜集利用研究区地质资料,结合GIS空间分析方法,分析了断层、地裂缝等构造因素与地面沉降的关系,建立了研究区地面沉降灾害敏感性分区图,对该区域地面沉降地裂缝灾害的防治工作具有指导作用。  相似文献   

17.
广东沿海陆地地质灾害区划   总被引：7，自引：2，他引：5  
研究中使用了主要地质灾害（地震、崩塌、滑坡、泥石流、地面沉降、地面塌陷、地基下沉、地裂缝、水土流失、港口淤积等）大量的野外调查和文献资料的实际数据,根据综合分析并运用“灾害密度”和“灾害强度”2种指标,将广东沿海陆地划分出9个地质灾害一级区及其所属的32个二级分区,其中包括10个重灾区、10个中灾区和12个弱灾区。首次编制了基于数据库和GIS的1：50万广东沿海陆地主要地质灾害类型与区划图,为地质灾害发育规律的理论研究和国民经济建设的实际应用提供了基础信息和实际数据。分区的结果揭示了地质灾害空间分布特征及其与地质环境和人类活动的关系。  相似文献   

18.
Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia   总被引：15，自引：0，他引：15  
This paper deals with landslide hazards and risk analysis of Penang Island, Malaysia using Geographic Information System (GIS) and remote sensing data. Landslide locations in the study area were identified from interpretations of aerial photographs and field surveys. Topographical/geological data and satellite images were collected and processed using GIS and image processing tools. There are ten landslide inducing parameters which are considered for landslide hazard analysis. These parameters are topographic slope, aspect, curvature and distance from drainage, all derived from the topographic database; geology and distance from lineament, derived from the geologic database; landuse from Landsat satellite images; soil from the soil database; precipitation amount, derived from the rainfall database; and the vegetation index value from SPOT satellite images. Landslide susceptibility was analyzed using landslide-occurrence factors employing the probability-frequency ratio model. The results of the analysis were verified using the landslide location data and compared with the probabilistic model. The accuracy observed was 80.03%. The qualitative landslide hazard analysis was carried out using the frequency ratio model through the map overlay analysis in GIS environment. The accuracy of hazard map was 86.41%. Further, risk analysis was done by studying the landslide hazard map and damageable objects at risk. This information could be used to estimate the risk to population, property and existing infrastructure like transportation network.  相似文献   

19.
Monitoring land subsidence in Semarang,Indonesia   总被引：1，自引：0，他引：1  
Semarang is one of the biggest cities in Indonesia and nowadays suffering from extended land subsidence, which is due to groundwater withdrawal, to natural consolidation of alluvium soil and to the load of constructions. Land subsidence causes damages to infrastructure, buildings, and results in tides moving into low-lying areas. Up to the present, there has been no comprehensive information about the land subsidence and its monitoring in Semarang. This paper examines digital elevation model (DEM) and benchmark data in Geographic Information System (GIS) raster operation for the monitoring of the land subsidence in Semarang. This method will predict and quantify the extent of subsidence in future years. The future land subsidence prediction is generated from the expected future DEM in GIS environment using ILWIS package. The procedure is useful especially in areas with scarce data. The resulting maps designate the area of land subsidence that increases rapidly and it is predicted that in 2020, an area of 27.5 ha will be situated 1.5–2.0 m below sea level. This calculation is based on the assumption that the rate of land subsidence is linear and no action is taken to protect the area from subsidence.  相似文献   

20.
Salt mining induced ground subsidence is a major hazard in the city of Tuzla (Northeastern sector of Bosnia and Herzegovina) and its surroundings since 1950, when solution mining of salt deposits by boreholes began. An analysis of the large (and never before processed) amount of topographical data collected during two periods: from 1956 to the Balkan War, and from 1992 to 2003 has been made. The analysis reveals a cumulative subsidence as high as 12 m during the whole period, causing damage to buildings and infrastructures within an area that includes a large portion of the historical town. Human-induced subsidence, (with rates up to 40 cm/year in the most developed area), has been investigated to recognize the areas affected by the sinking phenomenon and to produce a subsidence hazard. The time series of topographical observations have been enlarged by conducting new surveys in the urban area by modern space-geodesy methodologies, such as static relative GPS (Global Positioning System) and high resolution satellite imageries. The GPS monitoring started in 2004 and detected a decrease in the subsidence rates to 20 cm/year related to the reduction of salt exploitation. There is close correlation between the average subsidence rate and the annual amount of salt extracted.  相似文献   

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