Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS

Land subsidence is one of the frequent geological hazards worldwide. Urban areas and agricultural industries are the entities most affected by the consequences of land subsidence. The main objective of this study was to estimate the land subsidence (sinkhole) hazards at the Kinta Valley of Perak, Malaysia, using geographic information system and remote sensing techniques. To start, land subsidence locations were observed by surveying measurements using GPS and using the tabular data, which were produced as coordinates of each sinkhole incident. Various land subsidence conditioning factors were used such as altitude, slope, aspect, lithology, distance from the fault, distance from the river, normalized difference vegetation index, soil type, stream power index, topographic wetness index, and land use/cover. In this article, a data-driven technique of an evidential belief function (EBF), which is in the category of multivariate statistical analysis, was used to map the land subsidence-prone areas. The frequency ratio (FR) was performed as an efficient bivariate statistical analysis method in order compare it with the acquired results from the EBF analysis. The probability maps were acquired and the results of the analysis validated by the area under the (ROC) curve using the testing land subsidence locations. The results indicated that the FR model could produce a 71.16 % prediction rate, while the EBF showed better prediction accuracy with a rate of 73.63 %. Furthermore, the success rate was measured and accuracies of 75.30 and 79.45 % achieved for FR and EBF, respectively. These results can produce an understanding of the nature of land subsidence as well as promulgate public awareness of such geo-hazards to decrease human and economic losses.     

Spatial prediction of wildfire probability in the Hyrcanian ecoregion using evidential belief function model and GIS

Accurate estimates of wildfire probability and production of distribution maps are the first important steps in wildfire management and risk assessment. In this study, geographical information system (GIS)-automated techniques were integrated with the quantitative data-driven evidential belief function (EBF) model to predict spatial pattern of wildfire probability in a part of the Hyrcanian ecoregion, northern Iran. The historical fire events were identified using earlier reports and MODIS hot spot product as well as by carrying out multiple field surveys. Using the GIS-based EBF model, the relationships among existing fire events and various predictor variables predisposing fire ignition were analyzed. Model results were used to produce a distribution map of wildfire probability. The derived probability map revealed that zones of moderate, high, and very high probability covered nearly 60% of the landscape. Further, the probability map clearly demonstrated that the probability of a fire was strongly dependent upon human infrastructure and associated activities. By comparing the probability map and the historical fire events, a satisfactory spatial agreement between the five probability levels and fire density was observed. The probability map was further validated by receiver operating characteristic using both success rate and prediction rate curves. The validation results confirmed the effectiveness of the GIS-based EBF model that achieved AUC values of 84.14 and 81.03% for success and prediction rates, respectively.     

Landslide susceptibility zonation in Greece

The objective of this study is to perform a preliminary national-scale assessment of the landslide susceptibility in Greece using a landslide inventory derived from historical archives. The effects of controlling factors on landslide susceptibility combined with multivariate statistics have been evaluated using GIS aided mapping techniques. Thousand six hundred thirty-five landslide occurrences, mainly earth slides obtained from Public Authorities archives, covering a long time period were recorded and digitally stored using a spatial relational database management system. Ten landslide predisposing factors (predictors) were identified, while digital thematic maps on the spatial distribution of those factors were generated. The correlation between the landslide locations and predictor classes was analyzed by using the Landslide Relative Frequency. R-mode factor analysis was applied to study the interrelations between predictors (independent variables) while weighting coefficients were determined. Landslide susceptibility was derived from an algorithm which modeled the influence of predictors, and a susceptibility map was compiled. The landslide susceptibility map was verified using a data set of 375 new landslide locations. It is the first comprehensive attempt to illustrate the landslide susceptibility in the total country based on the interpretation of historical data only.     

A comparative study on the landslide susceptibility mapping using evidential belief function and weights of evidence models

The purpose of this study is to produce landslide susceptibility map of a landslide-prone area (Daguan County, China) by evidential belief function (EBF) model and weights of evidence (WoE) model to compare the results obtained. For this purpose, a landslide inventory map was constructed mainly based on earlier reports and aerial photographs, as well as, by carrying out field surveys. A total of 194 landslides were mapped. Then, the landslide inventory was randomly split into a training dataset; 70% (136 landslides) for training the models and the remaining 30% (58 landslides) was used for validation purpose. Then, a total number of 14 conditioning factors, such as slope angle, slope aspect, general curvature, plan curvature, profile curvature, altitude, distance from rivers, distance from roads, distance from faults, lithology, normalized difference vegetation index (NDVI), sediment transport index (STI), stream power index (SPI), and topographic wetness index (TWI) were used in the analysis. Subsequently, landslide susceptibility maps were produced using the EBF and WoE models. Finally, the validation of landslide susceptibility map was accomplished with the area under the curve (AUC) method. The success rate curve showed that the area under the curve for EBF and WoE models were of 80.19% and 80.75% accuracy, respectively. Similarly, the validation result showed that the susceptibility map using EBF model has the prediction accuracy of 80.09%, while for WoE model, it was 79.79%. The results of this study showed that both landslide susceptibility maps obtained were successful and would be useful for regional spatial planning as well as for land cover planning.     

Landslide susceptibility zonation of the Chamoli region, Garhwal Himalayas, using logistic regression model

A remote sensing and Geographic Information System-based study has been carried out for landslide susceptibility zonation in the Chamoli region, part of Garhwal Himalayas. Logistic regression has been applied to correlate the presence of landslides with independent physical factors including slope, aspect, relative relief, land use/cover, lithology, lineament, and drainage density. Coefficients of the categories of each factor have been obtained and used to assess the landslide probability value to ultimately categorize the area into various landslide susceptibility zones; very low, low, moderate, high, and very high. The results show that 71.13% of observed landslides fall in 21.96% of predicted very high and high susceptibility zone, which in fact should be the case. Furthermore, lineament first buffer category (0–500 m) and the east and south aspects are the most influential in causing landslides in the region.     

Landslide susceptibility zonation using statistical and machine learning approaches in Northern Lecco,Italy

This study deals with landslide susceptibility mapping in the northern part of Lecco Province, Lombardy Region, Italy. In so doing, a valid landslide inventory map and thirteen predisposing factors (including elevation, slope aspect, slope degree, plan curvature, profile curvature, distance to waterway, distance to road, distance to fault, soil type, land use, lithology, stream power index, and topographic wetness index) form the spatial database within geographic information system. The used predictive models comprise a bivariate statistical approach called frequency ratio (FR) and two machine learning tools, namely multilayer perceptron neural network (MLPNN) and adaptive neuro-fuzzy inference system (ANFIS). These models first use landslide and non-landslide records for comprehending the relationship between the landslide occurrence and predisposing factors. Then, landslide susceptibility values are predicted for the whole area. The accuracy of the produced susceptibility maps is measured using area under the curve (AUC) index, according to which, the MLPNN (AUC?=?0.916) presented the most accurate map, followed by the ANFIS (AUC?=?0.889) and FR (AUC?=?0.888). Visual interpretation of the susceptibility maps, FR-based correlation analysis, as well as the importance assessment of predisposing factors, all indicated the significant contribution of the road networks to the crucial susceptibility of landslide. Lastly, an explicit predictive formula is extracted from the implemented MLPNN model for a convenient approximation of landslide susceptibility value.

     

基于GIS的概率比率模型的滑坡易发性评价

以中国1∶50万区域环境地质调查(以地质灾害为主)、700个县(市)地质灾害调查与区划调查等(成果)资料为基础,选取地形起伏度、地貌类型、工程地质岩组、地震动峰值加速度、年平均降雨量、土地利用程度综合指数6个评价因子,采用概率比率模型,1 km×1 km评价单元,计算得到全国滑坡易发性评价指数图,并验证了结果的可靠性。进行了易发程度分区,最终得到高易发区、中易发区、低易发区和不易发区4个分区,完成了全国滑坡易发程度分区图。研究表明,概率比率模型方法可以客观、定量地评价滑坡易发性,适用于大区域易发性评价。     

Landslide susceptibility zonation mapping using logistic regression and its validation in Hashtchin Region,northwest of Iran

Landslide susceptibility zonation mapping assists researchers greatly to understand the spatial distribution of slope failure probability in a region. Being extremely useful in reducing landslide hazards, such maps could simply be produced using both qualitative and quantitative methods. In the present study, a multivariate statistical method called ‘logistic regression’ was used to assess landslide susceptibility in Hashtchin region, situated in west of Alborz Mountainsnorthwest of Iran. In this study, two independent variables, categorical (predictor) and continuous, were drawn on together in the model. To identify the region’s landslides use was made of aerial photographs, field studies and topographic maps. To prepare the database of factors affecting the region’s landslides and to determine landslide zones, geographic information system (GIS) was used. Using such information, landslide susceptibility modeling was accomplished. The data related to factors causing landslides were extracted as independent variables in each cell (in 50 m×50 m cells). Then, the whole data were input into the SPSS, Version 18. The prepared database was later analyzed using logistic regression, the forward stepwise method and based on maximum likelihood estimation. Regression equation was determined using obtained constants and coefficients and the landslide susceptibility of the area in grid-cells (pixels) was computed between 0 and 0.9954. The Receiver Operating Characteristic (ROC) curve was used to assess the accuracy of the logistic regression model. The predicting ability of the model was 84.1% given the area under ROC curve. Finally, the degree of success of landslide susceptibility zonation mapping was estimated to be 79%.     

Landslide susceptibility mapping of vicinity of Yaka Landslide (Gelendost,Turkey) using conditional probability approach in GIS

On 19 February 2007, a landslide occurred on the Alaard?ç Slope, located 1.6 km south of the town of Yaka (Gelendost, Turkey.) Subsequently, the displaced materials transformed into a mud flow in E?lence Creek and continued 750 m downstream towards the town of Yaka. The mass poised for motion in the Yaka Landslide source area and its vicinity, which would be triggered to a kinetic state by trigger factors such as heavy or sustained rainfall and/or snowmelt, poise a danger in the form of loss of life and property to Yaka with its population of 3,000. This study was undertaken to construct a susceptibility mapping of the vicinity of the Yaka Landslide’s source area and to relate it to movement of the landslide mass with the goal of prevention or mitigation of loss of life and property. The landslide susceptibility map was formulated by designating the relationship of the effecting factors that cause landslides such as lithology, gradient, slope aspect, elevation, topographical moisture index, and stream power index to the landslide map, as determined by analysis of the terrain, through the implementation of the conditional probability method. It was determined that the surface area of the Goksogut formation, which has attained lithological characteristics of clayey limestone with a broken and separated base and where area landslides occur, possesses an elevation of 1,100–1,300 m, a slope gradient of 15 °–35 ° and a slope aspect between 0 °–67.5 ° and 157 °–247 °. Loss of life and property may be avoided by the construction of structures to check the debris mass in E?lence Creek, the cleaning of the canal which passes through Yaka, the broadening of the canal’s base area, elevating the protective edges along the canal and the establishment of a protective zone at least 10-m wide on each side of the canal to deter against damage from probable landslide occurrence and mud flow.     

Landslide susceptibility mapping using GIS and weighted overlay method: a case study from North Jordan

Road instability along the Jerash–Amman highway was assessed using the weighted overlay method in Geographic Information System environment. The landslide susceptibility map was developed from nine contributing parameters. The map of landslide susceptibility was classified into five zones: very low (very stable), low (stable), moderate (moderately stable), high (unstable), and very high (highly unstable). The very high susceptibility and high susceptibility zones covered 15.14% and 31.81% of the study area, respectively. The main factors that made most parts of study area prone to landslides include excessive drainage channels, road cuts, and unfavorable rock strata such as marl and friable sandstone intercalated with clay and highly fractured limestone. Fracture zones are a major player in land instability. The moderate and high susceptibility zones are the most common in urban (e.g., Salhoub and Gaza camp) and agricultural areas. About 34% of the urban areas and 28.82% of the agricultural areas are characterized by the high susceptibility zone. Twenty percent of the Jerash–Amman highway length and 58% of the overall highway length are located in the very high susceptibility zone. The landslide susceptibility map was validated by the recorded landslides. More than 80 of the inventoried landslides are in unstable zones, which indicate that the selected causative factors are relevant and the model performs properly.     

基于GIS的北京市延庆县地质灾害易发性区域划分

随着灾害科学研究的深入，区域地质灾害已成为其重要的研究领域。文章利用遥感技术及GPS工具获取地质灾害的特征信息，在对地质灾害的成因背景分析基础上，运用GIS空间分析功能和地质灾害危险性评价、评估理论构建了地质灾害发育度模型。以北京市延庆县为实验区，采用ArcEngine＆．NET进行易发性分区程序的编写，计算研究区域内单元网格的发育度值。为了克服调查数据的局限性和人为因素，在计算发育度时引入修正系数，从延庆县DEM数据中提取单元格网内的地形坡度值，根据坡度值区间确定修正系数。将发育度计算结果按照一定规律、原则聚类。进行地质灾害易发性区域划分，取得了与实际情况较为一致的结果。基于“3S”技术及灾害地质条件，采用地质灾害发育度模型，可以较好地用于区域地质灾害易发性区域的划分，并能为防灾、减灾提供重要信息。     

Landslide susceptibility mapping of the Sera River Basin using logistic regression model

Of the natural hazards in Turkey, landslides are the second most devastating in terms of socio-economic losses, with the majority of landslides occurring in the Eastern Black Sea Region. The aim of this study is to use a statistical approach to carry out a landslide susceptibility assessment in one area at great risk from landslides: the Sera River Basin located in the Eastern Black Sea Region. This paper applies a multivariate statistical approach in the form of a logistics regression model to explore the probability distribution of future landslides in the region. The model attempts to find the best fitting function to describe the relationship between the dependent variable, here the presence or absence of landslides in a region and a set of independent parameters contributing to the occurrence of landslides. The dependent variable (0 for the absence of landslides and 1 for the presence of landslides) was generated using landslide data retrieved from an existing database and expert opinion. The database has information on a few landslides in the region, but is not extensive or complete, and thus unlike those normally used for research. Slope, angle, relief, the natural drainage network (including distance to rivers and the watershed index) and lithology were used as independent parameters in this study. The effect of each parameter was assessed using the corresponding coefficient in the logistic regression function. The results showed that the natural drainage network plays a significant role in determining landslide occurrence and distribution. Landslide susceptibility was evaluated using a predicted map of probability. Zones with high and medium susceptibility to landslides make up 38.8 % of the study area and are located mostly south of the Sera River Basin and along streams.     

滑坡灾害空间预测支持向量机模型及其应用

随着GIS技术在滑坡灾害空间预测研究中的广泛应用,滑坡灾害空间预测模型成为研究的热点问题。在总结滑坡灾害空间预测研究现状的基础上,简要介绍了两类和单类支持向量机的基本原理。以香港自然滑坡空间预测为例,采用两类和单类支持向量机进行滑坡灾害空间预测,并与Logistic回归模型进行了比较。结果表明,两类支持向量机模型优于Logistic回归模型,而Logistic回归模型优于单类支持向量机模型。