Ensemble of ground subsidence hazard maps using fuzzy logic

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.     

Application of logistic regression and fuzzy operators to landslide susceptibility assessment in Azdavay (Kastamonu,Turkey)

Landslides and their assessments are of great importance since they damage properties, infrastructures, environment, lives and so on. Particularly, landslide inventory, susceptibility, and hazard or risk mapping have become important issues in the last few decades. Such maps provide useful information and can be produced by qualitative or quantitative methods. The work presented in this paper aimed to assess landslide susceptibility in a selected area, covering 570.625 km² in the Western Black Sea region of Turkey, by two quantitative methods. For this purpose, in the first stage, a detailed landslide inventory map was prepared by extensive field studies. A total of 96 landslides were mapped during these studies. To perform landslide susceptibility analyses, six input parameters such as topographical elevation, lithology, land use, slope, aspect and distance to streams were considered. Two quantitative methods, logistic regression and fuzzy approach, were used to assess landslide susceptibility in the selected area. For the fuzzy approach, the fuzzy and, or, algebraic product, algebraic sum and gamma operators were considered. At the final stage, 18 landslide susceptibility maps were produced by the logistic regression and fuzzy operators in a GIS (Geographic Information System) environment. Two performance indicators such as ROC (relative operating characteristics) and cosine amplitude method (r _ij ) were used to validate the final susceptibility maps. Based on the analyses, the landslide susceptibility map produced by the fuzzy gamma operator with a level of 0.975 showed the best performance. In addition, the maps produced by the logistic regression, fuzzy algebraic product and the higher levels of gamma operators showed more satisfactory results, while the fuzzy and, or, algebraic sum maps were not sufficient to provide reliable outputs.     

Application and verification of fuzzy algebraic operators to landslide susceptibility mapping

The aim of this study was to apply and to verify the use of fuzzy logic to landslide susceptibility mapping in the Gangneung area, Korea, using a geographic information system (GIS). For this aim, in the study, a data-derived model (frequency ratio) and a knowledge-derived model (fuzzy operator) were combined. Landslide locations were identified by changing the detection technique of KOMPSAT-1 images and checked by field studies. For landslide susceptibility mapping, maps of the topography, lineaments, soil, forest, and land cover were extracted from the spatial data sets, and the eight factors influencing landslide occurrence were obtained from the database. Using the factors and the identified landslide, the fuzzy membership values were calculated. Then fuzzy algebraic operators were applied to the fuzzy membership values for landslide susceptibility mapping. Finally, the produced map was verified by comparing with existing landslide locations for calculating prediction accuracy. Among the fuzzy operators, in the case in which the gamma operator (λ = 0.975) showed the best accuracy (84.68%) while the case in which the fuzzy or operator was applied showed the worst accuracy (66.50%).     

Assessment of ground subsidence hazard near an abandoned underground coal mine using GIS

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.     

Prediction of ground subsidence in Samcheok City,Korea using artificial neural networks and GIS

This study shows the construction of a hazard map for presumptive ground subsidence around abandoned underground coal mines (AUCMs) at Samcheok City in Korea using an artificial neural network, with a geographic information system (GIS). To evaluate the factors governing ground subsidence, an image database was constructed from a topographical map, geological map, mining tunnel map, global positioning system (GPS) data, land use map, digital elevation model (DEM) data, and borehole data. An attribute database was also constructed by employing field investigations and reinforcement working reports for the existing ground subsidence areas at the study site. Seven major factors controlling ground subsidence were determined from the probability analysis of the existing ground subsidence area. Depth of drift from the mining tunnel map, DEM and slope gradient obtained from the topographical map, groundwater level and permeability from borehole data, geology and land use. These factors were employed by with artificial neural networks to analyze ground subsidence hazard. Each factor’s weight was determined by the back-propagation training method. Then the ground subsidence hazard indices were calculated using the trained back-propagation weights, and the ground subsidence hazard map was created by GIS. Ground subsidence locations were used to verify results of the ground subsidence hazard map and the verification results showed 96.06% accuracy. The verification results exhibited sufficient agreement between the presumptive hazard map and the existing data on ground subsidence area. An erratum to this article can be found at     

基于GIS的青海高寒区矿山地质环境影响程度模糊评价

青海省的矿产资源主要分布于青藏高原高寒区,处于生态环境脆弱地区,具有独特的地质环境条件,生态环境保护与矿业的开发之间矛盾突出。本文以青海省东部大通河上游的江仓煤矿四井田为例,将影响矿山地质环境质量的主要矿山地质环境地质要素概括为自然因子和人为因子二大类。为尽可能实现多因素的影响和评价因子的量化,将评价因子划分为三级,建立了矿山地质环境影响程度评价指标体系。在GIS支持下,建立影响程度评价因子数据库。利用GIS空间栅格叠加分析功能,采用三级模糊综合评判方法对矿山地质环境影响程度进行了综合评价,编制了矿山地质环境影响程度评价图。基于GIS的矿山地质环境影响程度模糊评价方法将GIS技术和模糊数学理论引入矿山地质环境影响程度评价研究之中,达到了定性、定量以及定位相结合,从而可为受损的矿山地质环境实施科学保护与治理提供更加精确的信息。     

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.     

Ensemble-based landslide susceptibility maps in Jinbu area, Korea

Ensemble techniques were developed, applied and validated for the analysis of landslide susceptibility in Jinbu area, Korea using the geographic information system (GIS). Landslide-occurrence areas were detected in the study by interpreting aerial photographs and field survey data. Landslide locations were randomly selected in a 70/30 ratio for training and validation of the models, respectively. Topography, geology, soil and forest databases were also constructed. Maps relevant to landslide occurrence were assembled in a spatial database. Using the constructed spatial database, 17 landslide-related factors were extracted. The relationships between the detected landslide locations and the factors were identified and quantified by frequency ratio, weight of evidence, logistic regression and artificial neural network models and their ensemble models. The relationships were used as factor ratings in the overlay analysis to create landslide susceptibility indexes and maps. Then, the four landslide susceptibility maps were used as new input factors and integrated using the frequency ratio, weight of evidence, logistic regression and artificial neural network models as ensemble methods to make better susceptibility maps. All of the susceptibility maps were validated by comparison with known landslide locations that were not used directly in the analysis. As the result, the ensemble-based landslide susceptibility map that used the new landslide-related input factor maps showed better accuracy (87.11% in frequency ratio, 83.14% in weight of evidence, 87.79% in logistic regression and 84.54% in artificial neural network) than the individual landslide susceptibility maps (84.94% in frequency ratio, 82.82% in weight of evidence, 87.72% in logistic regression and 81.44% in artificial neural network). All accuracy assessments showed overall satisfactory agreement of more than 80%. The ensemble model was found to be more effective in terms of prediction accuracy than the individual model.     

榆神府矿区地面塌陷特征及环境问题

在遥感解译的基础上,实地调查了神木县煤矿开采区地面塌陷现状,阐述了地面塌陷分布、发育特征及发育规模。共发现大面积地面塌陷30处。地面塌陷规模以中型为主,稳定性以较差为主,险情等级以小型为主,均为高强度采煤造成。单个塌陷区最大面积27.67 km2,煤炭开采强度与塌陷发育程度关系密切。论述了石圪台村、榆神路煤矿、蛇疙瘩村、榆家梁煤矿、后柳塔村、沙沟茆村等典型村镇地面塌陷特征。地面塌陷导致的地质环境问题日益突出,截至2015年4月30日,已经导致了48次1.5~3.3级的塌陷地震,诱发区域性地下水水位下降、井泉干涸与河流断流、水域面积萎缩,并进而产生一系列生态环境问题。      

急倾斜煤层开采地表沉陷的渐近灰色预测

煤层开采所引起的地表沉陷是一种严重的矿区地质灾害。煤层的倾角、厚度等物理条件是地表沉陷的主要影响因素。在水平煤层或缓倾斜煤的开采过程中，由于地层倾角小，地表沉陷具有较完整的规律性，其预测效果也比较理想。但是，在急倾斜煤层的开采中，由于地层倾角较大，赋存条件和地质体物理力学性质的差异，增强了地表下沉的非线性特征，使地表沉陷具有不确定的表现规律。文章对重庆市南桐矿区东林矿的地表沉陷非线性特征进行了探讨，得到了东林煤矿地表下沉曲线的分形维数是1．07。在岩层移动这个系统当中，既含有已知的又含有未知的或非确定的信息，可以作为一个灰色系统来研究。岩层控制系统的状态、结构和边界条件难以精确描述，属本征性灰色系统。文章针对东林煤矿地表下沉曲线非线性较弱的性质，提出用一种基于GM(1，1)的渐近预测模型对东林煤矿42个月的地表下沉量时间序列进行探讨。结果表明，这种模型对急倾斜层开采地表沉陷的预测是一种行之有效的方法。通过对其他工程实例的应用分析，进一步证明这种渐近的灰色预测方法具有相对较高的精度，是一种比较实用的地表沉陷预测方法，具有广泛的工程实用空间。     

基于四旋翼无人机的宁夏羊场湾煤矿采煤沉陷量监测

小型无人机遥感技术具有成本低、操作灵活便利等优点,在地质调查中的作用愈来愈重要。采煤地表沉陷量变形监测是掌控采煤地表岩移变形规律和治理塌陷的关键性工作。重点探索四旋翼无人机遥感技术监测在羊场湾煤矿Y120212工作面采煤沉陷量的监测研究,通过野外踏勘与控制点布设、无人机航线规划与执行、4D产品制作的工作程序和监测方法,探索无人机遥感技术监测在矿山地质塌陷监测的应用。研究结果表明,通过对无人机遥感技术生成的DSM处理,经过多期地面高程的对比,得到Y120212工作面最大沉陷量达6.5m。结合分析、对比,无人机遥感技术可以实现采煤塌陷区地表沉陷变形监测,进而形成和发展了煤矿地面塌陷新的监测技术。     

Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling�CNarayanghat road section in Nepal Himalaya

Landslide susceptibility maps are vital for disaster management and for planning development activities in the mountainous country like Nepal. In the present study, landslide susceptibility assessment of Mugling?CNarayanghat road and its surrounding area is made using bivariate (certainty factor and index of entropy) and multivariate (logistic regression) models. At first, a landslide inventory map was prepared using earlier reports and aerial photographs as well as by carrying out field survey. As a result, 321 landslides were mapped and out of which 241 (75?%) were randomly selected for building landslide susceptibility models, while the remaining 80 (25?%) were used for validating the models. The effectiveness of landslide susceptibility assessment using GIS and statistics is based on appropriate selection of the factors which play a dominant role in slope stability. In this case study, the following landslide conditioning factors were evaluated: slope gradient; slope aspect; altitude; plan curvature; lithology; land use; distance from faults, rivers and roads; topographic wetness index; stream power index; and sediment transport index. These factors were prepared from topographic map, drainage map, road map, and the geological map. Finally, the validation of landslide susceptibility map was carried out using receiver operating characteristic (ROC) curves. The ROC plot estimation results showed that the susceptibility map using index of entropy model with AUC value of 0.9016 has highest prediction accuracy of 90.16?%. Similarly, the susceptibility maps produced using logistic regression model and certainty factor model showed 86.29 and 83.57?% of prediction accuracy, respectively. Furthermore, the ROC plot showed that the success rate of all the three models performed more than 80?% accuracy (i.e. 89.15?% for IOE model, 89.10?% for LR model and 87.21?% for CF model). Hence, it is concluded that all the models employed in this study showed reasonably good accuracy in predicting the landslide susceptibility of Mugling?CNarayanghat road section. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose.     

Mining geohazards susceptibility and risk mapping: The case of the Amyntaio open-pit coal mine,West Macedonia,Greece

During the last decades, numerous methodologies for the construction of both susceptibility and risk maps have been developed in order to identify and mitigate geohazards such as landslides and secondarily land subsidence phenomena caused by the collapse of natural or man-made underground cavities. According to literature review, the conducted research concerning the assessment of land subsidence risk due to the overexploitation of the aquifer is still at an early stage. This study focuses οn the land subsidence phenomenon due to the overexploitation of the aquifer occurring in the Amyntaio basin in West Macedonia, hosting the active Amyntaio open-pit coal mine. This phenomenon has caused significant damage to settlements, farmlands and infrastructure. In order to construct susceptibility and risk maps knowledge of the geological, geotechnical, hydrogeological and tectonic settings of the study area is required. The proposed methodology for the production of a susceptibility and a risk map of land subsidence was based on the semi-quantitative method Weighted Linear Combination (WLC). The results were evaluated with an extensive field survey action recording, besides the above-mentioned settings, the spatial distribution of surface ruptures. The excellent agreement between the produced maps and the findings of the field survey, proved the added value of the maps, assigning them as crucial tools for the management of land subsidence phenomena.     

锡西—澄南典型地面沉降区地面沉降风险评价

选择长三角苏锡常地面沉降最为典型地段—无锡西部至江阴南部地区,系统分析了地面沉降现状及成因机理,建立了地面沉降风险评价指标体系。结合该区实际对体系中的评价指标进行了优选,依据地面沉降分层标监测数据、各评价指标要素对地面沉降灾害的贡献率分别对地面沉降易发性和易损性指标权重进行了修正。在地面沉降易发性、易损性评价基础上,利用GIS进行了地面沉降风险评价,根据评价结果提出了地面沉降风险控制规划建议。     

废弃采空区裂隙带高度预测模型及应用

煤炭开采引起覆岩破断及地表下沉,覆岩及地表运移规律可反映裂隙带高度的动态演化过程。因地表下沉滞后于煤炭开采,对于废弃采空区,长期压实作用导致裂隙带高度较采动期间有所降低。基于地表点下沉速度的阶段特征将裂隙带高度的演化过程分为2个阶段,第1阶段裂隙带发育对应岩层破断逐步向上传递的过程,第2阶段裂隙带高度降低对应离层及裂隙闭合、断裂岩层受压后变形回弹及破碎岩体自然压实的过程。着眼于压实作用对裂隙带高度的影响,根据煤层采厚、垮落带和裂隙带岩层变形量及地表下沉值之间的定量关系,建立了第2阶段裂隙带高度预测模型,并结合太平煤矿实测结果进行验证,采用控制变量法分析了单一因素影响下废弃采空区裂隙带高度的演化特征。结果表明：废弃采空区裂隙带高度受控于垮落带块体强度、垮落带初始碎胀系数、采动期间裂隙带高度最大值及对应的垮落带高度、煤层埋深、地表最终下沉量等因素,太平煤矿采后15 a的裂隙带高度实测值11.36～13.00 m与理论预测值12.75 m吻合度较高,模型的可靠性得到验证。最后,应用此预测模型对武安煤矿（关停矿井）2002－2003年采空区裂隙带高度开展理论计算,结合地空瞬变电磁探测确定了地面瓦斯抽采钻孔理想的终孔位置并成功开展了地面钻孔瓦斯抽采试验。     

Quantitative landslide susceptibility mapping at Pemalang area,Indonesia

For quantitative landslide susceptibility mapping, this study applied and verified a frequency ratio, logistic regression, and artificial neural network models to Pemalang area, Indonesia, using a Geographic Information System (GIS). Landslide locations were identified in the study area from interpretation of aerial photographs, satellite imagery, and field surveys; a spatial database was constructed from topographic and geological maps. The factors that influence landslide occurrence, such as slope gradient, slope aspect, curvature of topography, and distance from stream, were calculated from the topographic database. Lithology was extracted and calculated from geologic database. Using these factors, landslide susceptibility indexes were calculated by frequency ratio, logistic regression, and artificial neural network models. Then the landslide susceptibility maps were verified and compared with known landslide locations. The logistic regression model (accuracy 87.36%) had higher prediction accuracy than the frequency ratio (85.60%) and artificial neural network (81.70%) models. The models can be used to reduce hazards associated with landslides and to land-use planning.     

辽宁省铁-法煤田地表沉陷变形研究

针对铁-法煤田因采矿引发的地面沉降和地面塌陷两种地质灾害进行研究,认为采法、采区、采深、采高、顶板管理是影响沉陷灾害的直接因素;岩性、构造、水文地质条件及矿坑降水是影响沉陷灾害的间接因素。其中矿坑降水是不可忽视的重要因素。根据地下水降深、降落漏斗影响半径、岩土工程地质性质,计算降水引发的地表沉降值、水平移动、倾斜、曲率现状值。现有的地表变形曲线为采矿和降水引发地表变形曲线的叠加。可得出可靠的灾害危险性分区。     

Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran

The main goal of this study was to investigate the application of the weights-of-evidence and certainty factor approaches for producing landslide susceptibility maps of a landslide-prone area (Haraz) in Iran. For this purpose, the input layers of the landslide conditioning factors were prepared in the first stage. The landslide conditioning factors considered for the study area were slope gradient, slope aspect, altitude, lithology, land use, distance from streams, distance from roads, distance from faults, topographic wetness index, stream power index, stream transport index and plan curvature. For validation of the produced landslide susceptibility maps, the results of the analyses were compared with the field-verified landslide locations. Additionally, the receiver operating characteristic curves for all the landslide susceptibility models were constructed and the areas under the curves were calculated. The landslide locations were used to validate results of the landslide susceptibility maps. The verification results showed that the weights-of-evidence model (79.87%) performed better than certainty factor (72.02%) model with a standard error of 0.0663 and 0.0756, respectively. According to the results of the area under curve evaluation, the map produced by weights-of-evidence exhibits satisfactory properties.     

Predictive landslide susceptibility mapping using spatial information in the Pechabun area of Thailand

For predictive landslide susceptibility mapping, this study applied and verified probability model, the frequency ratio and statistical model, logistic regression at Pechabun, Thailand, using a geographic information system (GIS) and remote sensing. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys, and maps of the topography, geology and land cover were constructed to spatial database. The factors that influence landslide occurrence, such as slope gradient, slope aspect and curvature of topography and distance from drainage were calculated from the topographic database. Lithology and distance from fault were extracted and calculated from the geology database. Land cover was classified from Landsat TM satellite image. The frequency ratio and logistic regression coefficient were overlaid for landslide susceptibility mapping as each factor’s ratings. Then the landslide susceptibility map was verified and compared using the existing landslide location. As the verification results, the frequency ratio model showed 76.39% and logistic regression model showed 70.42% in prediction accuracy. The method can be used to reduce hazards associated with landslides and to plan land cover.     

矿井层间滑动构造定量评价——以潘集一矿为例

煤层及其顶底板中滑动现象极为普遍。层滑构造是导致煤层不稳定的主要因素,常常造成煤层厚度的突然变化,甚至形成无煤区,严重影响煤炭生产。为了掌握并了解矿区层滑构造的分布规律及发育强度,采用模糊综合评判法,取煤层层滑断层密度、煤层异常指数、断层密度、断层的强度指数、构造煤的发育强度指数、岩层组合关系量化参数6个评价指标,对矿区层间滑动构造复杂程度进行评价。结果表明:采用模糊综合评判法所得评价结果与矿区揭露结果相似。结合矿区地质条件预测深部未采区层滑构造发育程度取得了良好的效果,这为矿区生产部署提供了参考依据。