Adaptation and comparison of expert opinion to analytical hierarchy process for landslide susceptibility mapping

In this study, an attempt was made to re-evaluate and compare landslide susceptibility in a landslide-prone area in the West Black Sea Region of Turkey, using expert opinion and the analytical hierarchy process (AHP). In order to compare the results with a landslide susceptibility study undertaken previously in the same region using the artificial neural network (ANN) method, slope angle, slope aspect, topographical elevation, topographical shape, water conditions and vegetation cover parameters were taken into consideration. Experts were asked to rate their pairwise importance and their feedback was used in the AHP to produce a landslide susceptibility map of the study region. Its validity was tested using relation value (r _ij ) and the areal frequency distribution of the actual landslides in the area. The results were satisfactory and similar to those achieved in the previous ANN study. It is concluded that AHP can be a useful methodology in landslide susceptibility assessment.      

基于GIS和层次分析法的青海甘德县地质灾害危险性评价

该文选取了灾害点密度、工程活动、降雨条件、威胁财产和威胁人数等13项因素,以青海甘德县315个地质灾害点作为样本数据,建立多层次评价指标体系的地质灾害危险性评价指标体系,采用层次分析法赋权法计算评价指标权重,基于GIS平台采用多因素综合法计算了甘德县地质灾害危险性并分区。结果表明:甘德县地质灾害主要以低危险性为主,占有总面积的49.2%;中危险区占总面积的24.7%;高危险区占有总面积的26.1%。高危险区主要分布于交通道路沿线和黄河干流左岸等人类居住集中区,因此,需因地制宜地制定防治方案。本文基于层次分析法的地质灾害危险性评价体系适用性较好,对类似的青藏高原地区的地质灾害危险性评价具有一定参考意义。     

Application of GIS-based statistical modeling for landslide susceptibility mapping in the city of Azazga,Northern Algeria

Landslide susceptibility mapping is a necessary tool in order to manage the landslides hazard and improve the risk mitigation. In this research, we validate and compare the landslide susceptibility maps (LSMs) produced by applying four geographic information system (GIS)-based statistical approaches including frequency ratio (FR), statistical index (SI), weights of evidence (WoE), and logistic regression (LR) for the urban area of Azazga. For this purpose, firstly, a landslide inventory map was prepared from aerial photographs and high-resolution satellite imagery interpretation, and detailed fieldwork. Seventy percent of the mapped landslides were selected for landslide susceptibility modeling, and the remaining (30%) were used for model validation. Secondly, ten landslide factors including the slope, aspect, altitude, land use, lithology, precipitation, distance to drainage, distance to faults, distance to lineaments, and distance to roads have been derived from high-resolution Alsat 2A satellite images, aerial photographs, geological map, DEM, and rainfall database. Thirdly, we established LSMs by evaluating the relationships between the detected landslide locations and the ten landslides factors using FR, SI, LR, and WoE models in GIS. Finally, the obtained LSMs of the four models have been validated using the receiver operating characteristics curves (ROCs). The validation process indicated that the FR method provided more accurate prediction (78.4%) in generating LSMs than the SI (78.1%),WoE (73.5%), and LR (72.1%) models. The results revealed also that all the used statistical models provided good accuracy in landslide susceptibility mapping.

     

A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China

Bulletin of Engineering Geology and the Environment - The main objective of the current study is to apply a random forest (RF) data-driven model and prioritization of landslide conditioning factors...     

A hydro-chemical study of a mountainous watershed: the Ganga, India

A hydro-chemical study has been carried out on a 37-km stretch of the River Ganga from Deoprayag to Rishikesh (India) during the period from April 1999 to March 2000. The assessment of sediment and nutrient load has been considered to evaluate the current state of pollution through real time measurements. The values of pH and conductance are well within the limits prescribed for drinking water. The maximum suspended sediment concentrations of 1,405 and 2,002 mg/L were recorded at Deoprayag and Rishikesh, respectively, during the rainy season. A large amount of sediment and nutrient load is transported from the watershed during the rainy season. Concentrations of N(O3-)-N and N(H3-)-N at Deoprayag varied from 0.30 to 0.50 and 0.02 to 0.12 mg/L, respectively, depending on season. Examination of the results showed clearly that N(H3-)-N was generally low as compared to N03-N. Depending on the pH and temperature of soils, NH4+ and NO3- ions are produced in the watershed through ammonification and nitrification of organic matter and mobilized into rivers through run-off. Dissolved N and P from fertilizer application, sewage and non-point source run-off contribute significant quantities of these nutrients in river water. The nitrate and phosphate are transported from the cropland either by being adsorbed on to soil particles that are subsequently eroded, or dissolved in runoff water from agricultural land. The data generated through the study will be useful for development and management planning of the hilly watershed.     

A comparative study on machine learning modeling for mass movement susceptibility mapping (a case study of Iran)

Mass movements are among the most dangerous natural hazards in mountainous regions. The present study employs machine learning (ML) models for mass movement susceptibility mapping (MMSM) in Iran based on a comprehensive dataset of 864 mass movements which include debris flow, landslide, and rockfall during the last 42 years (1977–2019) as well as 12 conditional factors. The results of validation stage show that RF (random forest) is the most viable model for mass movement susceptibility maps. In addition, MARS (multivariate adaptive regression splines), MDA (mixture discriminant additive), and BRT (boosted regression trees) models also provide relatively accurate results. Results of the AUC for validation of produced maps were 0.968, 0.845, 0.828, and 0.765 for RF, MARS, MDA, and BRT, respectively. Based on MMSM generated by RF model, 32% of study area is identified to be under high and very high susceptibility classes. Most of the endangered areas for mass movement are in the west and central parts of the Chaharmahal and Bakhtiari Province. In addition, our findings indicate that elevation, slope angle, distance from roads, and distance from faults are critical factors for mass movement. Our results provide a perspective view for decision makers to mitigate natural hazards.

     

Rainfall thresholding and susceptibility assessment of rainfall-induced landslides: application to landslide management in St Thomas, Jamaica

The parish of St Thomas has one of the highest densities of landslides in Jamaica, which impacts the residents, local economy and the built and natural environment. These landslides result from a combination of steep slopes, faulting, heavy rainfall and the presence of highly weathered volcanics, sandstones, limestones and sandstone/shale series and are particularly prevalent during the hurricane season (June–November). The paper reports a study of the rainfall thresholds and landslide susceptibility assessment to assist the prediction, mitigation and management of slope instability in landslide-prone areas of the parish.     

Spatial data analysis and application of evidential belief functions to shallow landslide susceptibility mapping at Mt. Umyeon,Seoul, Korea

Predictive mapping of landslide occurrence at the regional scale was performed at Mt. Umyeon, in the southern part of Seoul, Korea, using an evidential belief function (EBF) model. To generate the landslide susceptibility map, approximately 90 % of 163 actual landslide locations were randomly selected as a training set, and about 10 % of them were used as a validation set. Spatial data sets relevant to landslide occurrence (topographic factors, hydrologic factors, forest factors, soil factors, and geologic factors) were analyzed in a geographic information system environment. In this study, landslide susceptibility was assessed on the basis of mass function assignment (belief, disbelief, uncertainty, and plausibility) and integration within a data-driven approach. The most representative of the resulting integrated susceptibility maps (the belief map) was validated using the receiver operating characteristic (ROC) method. The verification result showed that the model had an accuracy of 74.3 % and a predictive accuracy of 88.1 %. The frequency ratio (FR) model was also used for comparison with the EBF model. Prediction and success rates of 72.1 and 85.9 % were achieved using the FR model. The validation results showed satisfactory agreement between the susceptibility map and the existing landslide location data. The EBF model was more accurate than the FR model for landslide prediction in the study area. The results of this study can be used to mitigate landslide-induced hazards and for land-use planning.     

What makes a location attractive for the housing consumer? Preliminary findings from metropolitan Helsinki and Randstad Holland using the analytical hierarchy process

What determines the attractiveness of a location within a given housing market? The study reports some preliminary cross-country evidence on housing consumer preferences, based on expert elicited preference profiles generated by an analytic hierarchy process (AHP). The findings indicate fundamental differences between the two housing market contexts: metropolitan Helsinki (in 1998) and Randstad Holland (in 2003). In Helsinki housing quality and a spacious environment are always taken for granted. Therefore, an important choice criterion is location, particularly two aspects of it: accessibility and ?8pleasantness?9. The latter aspect characterises a pluralist consumer preference formation; it is based on various individual and idiosyncratic lifestyles that depend on value orientations. In Randstad Holland the situation is somewhat different. There, for most housing consumers, the functionality and spaciousness of the house matters more than its location. Furthermore, the tangible ?8hard?9 characteristics have more weight than the intangible ?8soft?9 ones when it comes to evaluating the physical surroundings. The image of the municipality does not matter as much as the neighbourhood factors. The difference is particularly evident for new developments in suburban areas. This has potential implications for the building industry when deciding on the most feasible strategy for production.     

Spatial prediction of landslide susceptibility using data mining-based kernel logistic regression,naive Bayes and RBFNetwork models for the Long County area (China)

The main goal of this study is to assess and compare three advanced machine learning techniques, namely, kernel logistic regression (KLR), naïve Bayes (NB), and radial basis function network (RBFNetwork) models for landslide susceptibility modeling in Long County, China. First, a total of 171 landslide locations were identified within the study area using historical reports, aerial photographs, and extensive field surveys. All the landslides were randomly separated into two parts with a ratio of 70/30 for training and validation purposes. Second, 12 landslide conditioning factors were prepared for landslide susceptibility modeling, including slope aspect, slope angle, plan curvature, profile curvature, elevation, distance to faults, distance to rivers, distance to roads, lithology, NDVI (normalized difference vegetation index), land use, and rainfall. Third, the correlations between the conditioning factors and the occurrence of landslides were analyzed using normalized frequency ratios. A multicollinearity analysis of the landslide conditioning factors was carried out using tolerances and variance inflation factor (VIF) methods. Feature selection was performed using the chi-squared statistic with a 10-fold cross-validation technique to assess the predictive capabilities of the landslide conditioning factors. Then, the landslide conditioning factors with null predictive ability were excluded in order to optimize the landslide models. Finally, the trained KLR, NB, and RBFNetwork models were used to construct landslide susceptibility maps. The receiver operating characteristics (ROC) curve, the area under the curve (AUC), and several statistical measures, such as accuracy (ACC), F-measure, mean absolute error (MAE), and root mean squared error (RMSE), were used for the assessment, validation, and comparison of the resulting models in order to choose the best model in this study. The validation results show that all three models exhibit reasonably good performance, and the KLR model exhibits the most stable and best performance. The KLR model, which has a success rate of 0.847 and a prediction rate of 0.749, is a promising technique for landslide susceptibility mapping. Given the outcomes of the study, all three models could be used efficiently for landslide susceptibility analysis.

     

Soft systems methodology and integrated flood management: a study of the Adayar watershed,Chennai, India

This paper applies Soft Systems Methodology (SSM) within the context of action research and Integrated Flood Management (IFM). A case study from the Adayar watershed, Chennai, is provided as an example of how SSM can be used to understand complex situations and as a problem‐solving strategy for flood management. Flat topography, uncontrolled urban development, population growth, sand bar formation at the river mouth and low tidal action render complexity to flood management in Chennai. For effective flood management, a participatory and integrated approach, which includes stakeholders in the decision‐making process and an enabling institutional set‐up, is essential. As part of an integrated approach, the relationship between various organizations and the public is identified. SSM is an approach for addressing fuzzy problematic situations involving human activity. In this paper, SSM techniques like ‘Rich Picture Diagrams’ and ‘CATWOE analysis’ and participatory action research tools like ‘pairwise ranking’ and ‘force field analysis’ were investigated. Two workshops were conducted to define and explore the problematic situation, the role of various actors involved in the problem, to develop the conceptual model, to rank decision‐making criteria, and to analyse the forces for and against to solve the problem. The flood management approach provided in this paper can be used by government agencies and policy makers to manage floods.     

Regional back analysis of landslide events using TRIGRS model and rainfall threshold: an approach to estimate landslide hazard for Kodagu,India

Bulletin of Engineering Geology and the Environment - The mountain model developed by Fookes et al. (Eng Geol 21:1–152, 1985) comprised four terrain zones applicable to the Himalayan...     

A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling

The main aim of this study is to propose a novel hybrid intelligent model named MBSVM which is an integration of the MultiBoost ensemble and a support vector machine (SVM) for modeling of susceptibility of landslides in the Uttarakhand State, Northern India. Firstly, a geospatial database for the study area was prepared, which includes 391 historical landslides and 16 landslide-affecting factors. Then, the sensitivity of different combinations of these factors for modeling was validated using the forward elimination technique. The MBSVM landslide model was built using the datasets generated from the best selected factors and validated utilizing the area under the receiver operating characteristic (ROC) curve (AUC), statistical indexes, and the Wilcoxon signed-rank test. Results show that this novel hybrid model has good performance both in terms of goodness of fit with the training dataset (AUC = 0.972) and the capability to predict landslides with the testing dataset (AUC = 0.966). The efficiency of the proposed model was then validated by comparison with logistic regression (LR), a single SVM, and another hybrid model of the AdaBoost ensemble and an SVM (ABSVM). Comparison results show that the MBSVM outperforms the LR, single SVM, and hybrid ABSVM models. Thus, the proposed model is a promising and good alternative tool for landslide hazard assessment in landslide-prone areas.

     

A method for quick assessment of earthquake-triggered landslide hazards: a case study of the Mw6.1 2014 Ludian,China earthquake

Rapid assessment of the distribution of earthquake-triggered landslides is an important component of effective disaster mitigation. The effort should be based on both seismic landslide susceptibility and the ground shaking intensity, which is usually measured by peak ground acceleration (PGA). In this paper, we address this issue by analyzing data from the Mw6.1 2014 Ludian, China earthquake. The Newmark method of rigid-block modeling was applied to calculate the critical acceleration of slopes in the study area, which serve as measurement of slope stability under seismic load. The assessment of earthquake-triggered landslide hazard was conducted by comparing these critical accelerations with the distribution of known PGA values. The study area was classified into zones of five levels of landslide hazard: high, moderate high, moderate, light, and very light. Comparison shows that the resulting landslide hazard zones agree with the actual distribution of earthquake-triggered landslides. Nearly 70% of landslides are located in areas of high and moderately high hazard, which occupy only 17% of the study region. This paper demonstrates that using PGA, combined with the analysis of seismic landslide susceptibility, allows a reliable assessment of earthquake-triggered landslides hazards. This easy-operation mapping method is expected to be helpful in emergency preparedness planning, as well as in seismic landslide hazard zoning.

     

Performance assessment model development and parameter acquisition for analysis of the transport of natural radionuclides in a Mediterranean watershed

This paper describes the methodology developed to construct a model for predicting the behaviour of the natural radioisotopes of U, Th and Ra in a Mediterranean watershed. The methodology includes the development of the performance assessment model, obtaining water flow and radiological parameters based on experimental data and analysis of results. The model, which accounts for both water flows and mass balances of the radionuclides in a semi-natural environment, provides assessments of radionuclide behaviour in grassland and agricultural soils, rivers and reservoirs, including the processes of radionuclide migration through land and water and interactions between both. From field and laboratory data, it has been possible to obtain parameters for the driving processes considered in the model, water fluxes, source term definition, soil to plant transfer factors and distribution coefficient values. Ranges of parameter values obtained have shown good agreement with published literature data. This general methodological approach was developed to be extended to other radionuclides for the modelling of a biosphere watershed in the context of performance assessment of a High Level Waste (HLW) repository under Mediterranean climate conditions, as well as for forecasting radionuclide transport under similar Mediterranean conditions that will occur in the future in other areas. The application of sensitivity and uncertainty analysis was intended to identify key uncertainties with the aim of setting priorities for future research. The model results for the activity concentration in the reservoir indicate that for (238)U and (230)Th the most relevant parameter is the initial concentrations of the radionuclides in the reservoir sediments. However, for (226)Ra the most important parameter is the precipitation rate over the whole watershed.