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.     

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.     

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     

Sensitivity analysis for the GIS-based mapping of the ground subsidence hazard near abandoned underground coal mines

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.     

Application of a fuzzy operator to susceptibility estimations of coal mine subsidence in Taebaek City,Korea

Quantitative determination of locations vulnerable to ground subsidence at mining regions is necessary for effective prevention. In this paper, a method of constructing subsidence susceptibility maps based on fuzzy relations is proposed and tested at an abandoned underground coal mine in Korea. An advantage of fuzzy combination operators over other methods is that the operation is mathematically and logically easy to understand and its implementation to GIS software is simple and straightforward. A certainty factor analysis was used for estimating the relative weight of eight major factors influencing ground subsidence. The relative weight of each factor was then converted into a fuzzy membership value and integrated as a subsidence hazard index using fuzzy combination operators, which produced coal mine subsidence susceptibility maps. The susceptibility maps were compared with the reported ground subsidence areas, and the results showed high accuracy between our prediction and the actual subsidence. Based on the root mean square error and accuracy in terms of success rates, fuzzy γ-operator with a low γ value and fuzzy algebraic product operator, specifically, are useful for ground subsidence prediction. Comparing the results of a fuzzy γ-operator and a conventional logistic regression model, the performance of the fuzzy approach is comparative to that of a logistic regression model with improved computational. A field survey done in the area supported the method’s reliability. A combination of certainty factor analysis and fuzzy relations with a GIS is an effective method to determine locations vulnerable to coal mine subsidence.     

山西长治市崔蒙地区采煤地面塌陷初步研究

长治市崔蒙地区因煤矿采空而引发地面塌陷,塌陷积水而使大量农田弃耕,当地经济发展受到极大的影响。本文在地面塌陷地质灾害勘察并查明地面塌陷现状基础上,分析了崔蒙地面塌陷的形成机理,并通过常规移动延续时间、残余移动变形时间估算结合塌陷区地表移动监测资料分析研究,认为地面塌陷仍将持续,只是变形速度较缓慢,不致诱发地裂缝,继续造成土地破坏或地表水体漏失的可能性较小。     

Combining landslide susceptibility maps obtained from frequency ratio,logistic regression,and artificial neural network models using ASTER images and GIS

Landslide-related factors were extracted from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, and integrated techniques were developed, applied, and verified for the analysis of landslide susceptibility in Boun, Korea, using a geographic information system (GIS). Digital elevation model (DEM), lineament, normalized difference vegetation index (NDVI), and land-cover factors were extracted from the ASTER images for analysis. Slope, aspect, and curvature were calculated from a DEM topographic database. Using the constructed spatial database, the relationships between the detected landslide locations and six related factors were identified and quantified using frequency ratio (FR), logistic regression (LR), and artificial neural network (ANN) models. These relationships were used as factor ratings in an overlay analysis to create landslide susceptibility indices and maps. Three landslide susceptibility maps were then combined and applied as new input factors in the FR, LR, and ANN models to make improved susceptibility maps. All of the susceptibility maps were verified by comparison with known landslide locations not used for training the models. The combined landslide susceptibility maps created using three landslide-related input factors showed improved accuracy (87.00% in FR, 88.21% in LR, and 86.51% in ANN models) compared to the individual landslide susceptibility maps (84.34% in FR, 85.40% in LR, and 74.29% in ANN models) generated using the six factors from the ASTER images.     

A seismic landslide hazard analysis with topographic effect,a case study in the 99 Peaks region,Central Taiwan

It has been known that ground motion amplitude will be amplified at mountaintops; however, such topographic effects are not included in conventional landslide hazard models. In this study, a modified procedure that considers the topographic effects is proposed to analyze the seismic landslide hazard. The topographic effect is estimated by back analysis. First, a 3D dynamic numerical model with irregular topography is constructed. The theoretical topographic amplification factors are derived from the dynamic numerical model. The ground motion record is regarded as the reference motion in the plane area. By combining the topographic amplification factors with the reference motions, the amplified acceleration time history and amplified seismic intensity parameters are obtained. Newmark’s displacement model is chosen to perform the seismic landslide hazard analysis. By combining the regression equation and the seismic parameter of peak ground acceleration and Arias intensity, the Newmark’s displacement distribution is generated. Subsequently, the calculated Newmark’s displacement maps are transformed to the hazard maps. The landslide hazard maps of the 99 Peaks region, Central Taiwan are evaluated. The actual landslide inventory maps triggered by the 21 September 1999, Chi-Chi earthquake are compared with the calculated hazard maps. Relative to the conventional procedure, the results show that the proposed procedures, which include the topographic effect can obtain a better result for seismic landslide hazard analysis. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

广东沿海陆地地质灾害区划

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。     

Mapping landslide susceptibility with frequency ratio,statistical index,and weights of evidence models: a case study in northern Iran

The main objective of this study is to investigate potential application of frequency ratio (FR), weights of evidence (WoE), and statistical index (SI) models for landslide susceptibility mapping in a part of Mazandaran Province, Iran. First, a landslide inventory map was constructed from various sources. The landslide inventory map was then randomly divided in a ratio of 70/30 for training and validation of the models, respectively. Second, 13 landslide conditioning factors including slope degree, slope aspect, altitude, plan curvature, stream power index, topographic wetness index, sediment transport index, topographic roughness index, lithology, distance from streams, faults, roads, and land use type were prepared, and the relationships between these factors and the landslide inventory map were extracted by using the mentioned models. Subsequently, the multi-class weighted factors were used to generate landslide susceptibility maps. Finally, the susceptibility maps were verified and compared using several methods including receiver operating characteristic curve with the areas under the curve (AUC), landslide density, and spatially agreed area analyses. The success rate curve showed that the AUC for FR, WoE, and SI models was 81.51, 79.43, and 81.27, respectively. The prediction rate curve demonstrated that the AUC achieved by the three models was 80.44, 77.94, and 79.55, respectively. Although the sensitivity analysis using the FR model revealed that the modeling process was sensitive to input factors, the accuracy results suggest that the three models used in this study can be effective approaches for landslide susceptibility mapping in Mazandaran Province, and the resultant susceptibility maps are trustworthy for hazard mitigation strategies.     

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.     

Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models

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.     

A case study for the integration of predictive mineral potential maps

This study aims to elaborate on the mineral potential maps using various models and verify the accuracy for the epithermal gold (Au) — silver (Ag) deposits in a Geographic Information System (GIS) environment assuming that all deposits shared a common genesis. The maps of potential Au and Ag deposits were produced by geological data in Taebaeksan mineralized area, Korea. The methodological framework consists of three main steps: 1) identification of spatial relationships 2) quantification of such relationships and 3) combination of multiple quantified relationships. A spatial database containing 46 Au-Ag deposits was constructed using GIS. The spatial association between training deposits and 26 related factors were identified and quantified by probabilistic and statistical modelling. The mineral potential maps were generated by integrating all factors using the overlay method and recombined afterwards using the likelihood ratio model. They were verified by comparison with test mineral deposit locations. The verification revealed that the combined mineral potential map had the greatest accuracy (83.97%), whereas it was 72.24%, 65.85%, 72.23% and 71.02% for the likelihood ratio, weight of evidence, logistic regression and artificial neural network models, respectively. The mineral potential map can provide useful information for the mineral resource development.     

Probability-weighted hazard maps for comparing different flood risk management strategies: a case study

The study proposes an original methodology for producing probability-weighted hazard maps based on an ensemble of numerical simulations. These maps enable one to compare different strategies for flood risk management. The methodology was applied over a 270-km² flood-prone area close to the left levee system of a 28-km reach of the river Reno (Northern Central Italy). This reach is characterised by the presence of a weir that allows controlled flooding of a large flood-prone area during major events. The proposed probability-weighted hazard maps can be used to evaluate how a structural measure such as the mentioned weir alters the spatial variability of flood hazard in the study area. This article shows an application by constructing two different flood hazard maps: a first one which neglects the presence of the weir using a regular levee system instead, and a second one that reflects the actual geometry with the weir. Flood hazard maps were generated by combining the results of several inundation scenarios, simulated by coupling 1D- and 2D-hydrodynamic models.     

Landslide detection and susceptibility mapping in the Sagimakri area, Korea using KOMPSAT-1 and weight of evidence technique

The purpose of this study is to detect landslide locations from satellite images and use them for landslide susceptibility mapping in the Sagimakri area, Korea using a geographic information system and a data-driven weight of evidence model. The landslide location areas were identified from Korea multipurpose satellite images by means of change detection technique and further verified by extensive field survey. Subsequently, landslide locations were randomly selected in a 70:30 ratio for training and validation of the model, respectively. A spatial database was constructed, which is composed of topography, forest, soil, and land cover, and 14 landslide-related factors were extracted from the database. The relationships between the detected landslide locations and the factors were identified and quantified by weights of evidence model. Tests of conditional independence were performed for the selection of factors, allowing five different combinations of factors to be analyzed. The relationships were used as the contrast values, W ⁺ and W ^? of factor ratings in the overlay analysis to create landslide susceptibility indexes and maps. The results of the analysis were validated by comparison with known landslide locations that were not used directly in the analysis.     

Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models

This study applied, tested and compared a probability model, a frequency ratio and statistical model, a logistic regression to Damre Romel area, Cambodia, using a geographic information system. For landslide susceptibility mapping, landslide locations were identified in the study area from interpretation of aerial photographs and field surveys, and a spatial database was constructed from topographic maps, geology and land cover. The factors that influence landslide occurrence, such as slope, aspect, curvature and distance from drainage were calculated from the topographic database. Lithology and distance from lineament were extracted and calculated from the geology database. Land cover was classified from Landsat TM satellite imagery. The relationship between the factors and the landslides was calculated using frequency ratio and logistic regression models. The relationships, frequency ratio and logistic regression coefficient were overlaid to make landslide susceptibility map. Then the landslide susceptibility map was compared with known landslide locations and tested. As the result, the frequency ratio model (86.97%) and the logistic regression (86.37%) had high and similar prediction accuracy. The landslide susceptibility map can be used to reduce hazards associated with landslides and to land cover planning.     

Proposed seismic hazard maps of Sumatra and Java islands and microzonation study of Jakarta city,Indonesia

This paper presents the development of spectral hazard maps for Sumatra and Java islands, Indonesia and microzonation study for Jakarta city. The purpose of this study is to propose a revision of the seismic hazard map in Indonesian Seismic Code SNI 03-1726-2002. Some improvements in seismic hazard analysis were implemented in the analysis by considering the recent seismic activities around Java and Sumatra. The seismic hazard analysis was carried out using 3-dimension (3-D) seismic source models (fault source model) using the latest research works regarding the tectonic setting of Sumatra and Java. Two hazard levels were analysed for representing 10% and 2% probability of exceedance (PE) in 50 years ground motions for Sumatra and Java. Peak ground acceleration contour maps for those two hazard levels and two additional macrozonation maps for 10% PE in 50 years were produced during this research. These two additional maps represent short period (0.2 s) and long-period (1.0 s) spectra values at the bedrock. Microzonation study is performed in order to obtain ground motion parameters such as acceleration, amplification factor and response spectra at the surface of Jakarta. The analyses were carried out using nonlinear approach. The results were used to develop contour of acceleration at the surface of Jakarta. Finally, the design response spectra for structural design purposes are proposed in this study.     

Regional prediction of landslide hazard using probability analysis of intense rainfall in the Hoa Binh province, Vietnam

The main objective of this study is to assess regional landslide hazards in the Hoa Binh province of Vietnam. A landslide inventory map was constructed from various sources with data mainly for a period of 21 years from 1990 to 2010. The historic inventory of these failures shows that rainfall is the main triggering factor in this region. The probability of the occurrence of episodes of rainfall and the rainfall threshold were deduced from records of rainfall for the aforementioned period. The rainfall threshold model was generated based on daily and cumulative values of antecedent rainfall of the landslide events. The result shows that 15-day antecedent rainfall gives the best fit for the existing landslides in the inventory. The rainfall threshold model was validated using the rainfall and landslide events that occurred in 2010 that were not considered in building the threshold model. The result was used for estimating temporal probability of a landslide to occur using a Poisson probability model. Prior to this work, five landslide susceptibility maps were constructed for the study area using support vector machines, logistic regression, evidential belief functions, Bayesian-regularized neural networks, and neuro-fuzzy models. These susceptibility maps provide information on the spatial prediction probability of landslide occurrence in the area. Finally, landslide hazard maps were generated by integrating the spatial and the temporal probability of landslide. A total of 15 specific landslide hazard maps were generated considering three time periods of 1, 3, and 5 years.     

Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models

This paper presents landslide hazard analysis at Cameron area, Malaysia, using a geographic information system (GIS) and remote sensing data. Landslide locations were identified from interpretation of aerial photographs and field surveys. Topographical and geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. The factors chosen that influence landslide occurrence are topographic slope, topographic aspect, topographic curvature, and distance to rivers, all from the topographic database; lithology and distance to faults were taken from the geologic database; land cover from TM satellite image; the vegetation index value was taken from Landsat images; and precipitation distribution from meteorological data. Landslide hazard area was analyzed and mapped using the landslide occurrence factors by frequency ratio and bivariate logistic regression models. The results of the analysis were verified using the landslide location data and compared with the probabilistic models. The validation results showed that the frequency ratio model (accuracy is 89.25%) is better in prediction of landslide than bivariate logistic regression (accuracy is 85.73%) model.     

Predictive Modeling of Mining Induced Ground Subsidence with Survival Analysis and Online Sequential Extreme Learning Machine

Mining induced land subsidence is one of the most hazardous geological phenomenon. Predictive modeling of the ground subsidence has attracted increased interest and is crucial to the hazard prevention. In this research, a data-driven approach integrated with survival analysis to model the mining-induced subsidence is studied. The data used in this research is collected in Fuxin, Liaoning Province, China and it contains multiple variables from different subsided locations. First, a survival analysis is conducted using the Cox proportional hazard model to evaluate the importance of variables considered. p values of all variables are computed and the important variables are selected. Next, data-driven models including k-nearest neighbor, support vector machine, back-propagated neural network, random forest, extreme learning machine, and online sequential extreme learning machine are constructed to predict the subsidence values and horizontal movement. Two evaluation matrices namely MAPE and RMSE are introduced to evaluate the performances of the data-driven models. Computational results demonstrate that online sequential extreme learning machine is capable of accurately predict the mining induced subsidence and surface deformation.