Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh

Landslides are a common hazard in the highly urbanized hilly areas in Chittagong Metropolitan Area (CMA), Bangladesh. The main cause of the landslides is torrential rain in short period of time. This area experiences several landslides each year, resulting in casualties, property damage, and economic loss. Therefore, the primary objective of this research is to produce the Landslide Susceptibility Maps for CMA so that appropriate landslide disaster risk reduction strategies can be developed. In this research, three different Geographic Information System-based Multi-Criteria Decision Analysis methods—the Artificial Hierarchy Process (AHP), Weighted Linear Combination (WLC), and Ordered Weighted Average (OWA)—were applied to scientifically assess the landslide susceptible areas in CMA. Nine different thematic layers or landslide causative factors were considered. Then, seven different landslide susceptible scenarios were generated based on the three weighted overlay techniques. Later, the performances of the methods were validated using the area under the relative operating characteristic curves. The accuracies of the landslide susceptibility maps produced by the AHP, WLC_1, WLC_2, WLC_3, OWA_1, OWA_2, and OWA_3 methods were found as 89.80, 83.90, 91.10, 88.50, 90.40, 95.10, and 87.10 %, respectively. The verification results showed satisfactory agreement between the susceptibility maps produced and the existing data on the 20 historical landslide locations

Landslide Susceptibility Mapping Using Statistical Methods along the Asian Highway, Bhutan

In areas prone to frequent landslides, the use of landslide susceptibility maps can greatly aid in the decision-making process of the socio-economic development plans of the area. Landslide susceptibility maps are generally developed using statistical methods and geographic information systems. In the present study, landslide susceptibility along road corridors was considered, since the anthropogenic impacts along a road in a mountainous country remain uniform and are mainly due to road construction. Therefore, we generated landslide susceptibility maps along 80.9 km of the Asian Highway (AH48) in Bhutan using the information value, weight of evidence, and logistic regression methods. These methods have been used independently by some researchers to produce landslide susceptibility maps, but no comparative analysis of these methods with a focus on road corridors is available. The factors contributing to landslides considered in the study are land cover, lithology, elevation, proximity to roads, drainage, and fault lines, aspect, and slope angle. The validation of the method performance was carried out by using the area under the curve of the receiver operating characteristic on training and control samples. The area under the curve values of the control samples were 0.883, 0.882, and 0.88 for the information value, weight of evidence, and logistic regression models, respectively, which indicates that all models were capable of producing reliable landslide susceptibility maps. In addition, when overlaid on the generated landslide susceptibility maps, 89.3%, 85.6%, and 72.2% of the control landslide samples were found to be in higher-susceptibility areas for the information value, weight of evidence, and logistic regression methods, respectively. From these findings, we conclude that the information value method has a better predictive performance than the other methods used in the present study. The landslide susceptibility maps produced in the study could be useful to road engineers in planning landslide prevention and mitigation works along the highway

Earthquake‐induced landslide scenarios for seismic microzonation. Application to the Accumoli area (Rieti, Italy)

Scenarios of earthquake-induced landslides are necessary for seismic microzonation (SM) studies since they must be integrated with the mapping of instability areas. The PARSIFAL (Probabilistic Approach to pRovide Scenarios of earthquake‐Induced slope FAiLures) approach provides extensive analyses, over tens to thousands of square kilometers, and is designed as a fully comprehensive methodology to output expected scenarios which depend on seismic input and saturation conditions. This allows to attribute a rating, in terms of severity level, to the landslide-prone slope areas in view of future engineering studies and designs. PARSIFAL takes into account first-time rock- and earth-slides as well as re-activations of existing landslides performing slope stability analyses of different failure mechanisms. The results consist of mapping earthquake-induced landslide scenarios in terms of exceedance probability of critical threshold values of co-seismic displacements (P[D≥Dc|a(t),ay]). PARSIFAL was applied in the framework of level 3 SM studies over the municipality area of Accumoli (Rieti, Italy), strongly struck by the 2016 seismic sequence of Central Apennines. The use of the PARSIFAL was tested for the first time to screen the Susceptibility Zones (ZSFR) from the Attention Zones (ZAFR) in the category of the unstable areas, according to the guidelines by Italian Civil Protection. The results obtained were in a GIS-based mapping representing the possibility for a landslide to be induced by an earthquake (with a return period of 475 years) in three different saturation scenarios (i.e. dry, average, full). Only 41% of the landslide-prone areas in the Municipality of Accumoli are existing events, while the remaining 59% is characterized by first-time earth- or rock-slides. In dry conditions, unstable conditions or P[D≥Dc|a(t),ay]>0 were for 54% of existing landslides, 17% of first-time rock-slides and 1% of first-time earth- slides. In full saturation conditions, the findings are much more severe since unstable conditions or P[D≥Dc|a(t),ay]>0 were found for 58% of the existing landslides and for more than 80% of first-time rock- and earth-slides. Moreover, comparison of the total area of the ZAFR versus ZSFR, resulted in PARSIFAL screening reducing of 22% of the mapped ZAFR

An integrated user-friendly ArcMAP tool for bivariate statistical modeling in geoscience applications

Modelling and classification difficulties are fundamental issues in natural hazard assessment. A geographic information system (GIS) is a domain that requires users to use various tools to perform different types of spatial modelling. Bivariate statistical analysis (BSA) assists in hazard modelling. To perform this analysis, several calculations are required and the user has to transfer data from one format to another. Most researchers perform these calculations manually by using Microsoft Excel or other programs. This process is time-consuming and carries a degree of uncertainty. The lack of proper tools to implement BSA in a GIS environment prompted this study. In this paper, a user-friendly tool, bivariate statistical modeler (BSM), for BSA technique is proposed. Three popular BSA techniques, such as frequency ratio, weight-of-evidence (WoE), and evidential belief function (EBF) models, are applied in the newly proposed ArcMAP tool. This tool is programmed in Python and created by a simple graphical user interface (GUI), which facilitates the improvement of model performance. The proposed tool implements BSA automatically, thus allowing numerous variables to be examined. To validate the capability and accuracy of this program, a pilot test area in Malaysia is selected and all three models are tested by using the proposed program. Area under curve (AUC) is used to measure the success rate and prediction rate. Results demonstrate that the proposed program executes BSA with reasonable accuracy. The proposed BSA tool can be used in numerous applications, such as natural hazard, mineral potential, hydrological, and other engineering and environmental applications

Spatio-statistical comparative approaches for landslide susceptibility modeling: case of Mae Phun, Uttaradit Province, Thailand

In this study for landslide susceptibility modeling, three quantitative techniques, i.e., frequency ratio (FR), information value (IFV), and weight of evidence (WOE), were evaluated and compared. For this purpose, landslide inventory map was prepared using visual interpretation on SPOT-5 image and field survey was carried out for ground truthing of landslide sites and total 677 landslides were identified. The inventory map was divided into training and validation datasets, and from total, 473 landslides (70%) were for training to run the model and 30% (204 landslides) for validation purpose. Total 11 landslide conditioning factors were used in this study that are: elevation, slope, aspect, curvature, plan curvature, profile curvature, land use/land cover (LULC), topographic wetness index (TWI), stream power index (SPI), proximity to road, and proximity to stream. Three different landslide susceptibility maps were produced based on analyzing the relationship of landslides with conditioning factors using FR, IFV, and WOE in GIS environment. The results of FR model indicated that almost 40% of the total study area fall in high to very high landslide susceptibility zones, while in WOE and IFV models, it was found almost 50% of the total area. The landslide susceptibility maps were validated using prediction and success rate curve techniques. The prediction rate curve gives us a glimpse of future landslides based on present landslide susceptibility maps. The results obtained from validation showed that the area under curve (AUC) based on prediction rate curve for FR, IFV, and WOE was 80.78%, 72.88%, and 72.33%, respectively. However, the AUC obtained through success rate curve for the models in this study was 74.60%, 75.04%, and 72.54% for FR, IFV, and WOE, respectively. Moreover, the evaluation of landslide density test and seed cell index area (SCAI) indicated that calculated and classified landslide susceptibility maps are in a good agreement with the field conditions. Thus, it was observed from this study that the frequency ratio has better accuracy as compared to information value and weight of evidence, but in success rate curve, almost all the models showed the same results. Consequently, it can be concluded that the susceptibility maps produced from FR, IFV, and WOE are in good agreement because more than two-third of landslides falls in high and very high susceptibility zones of each model. From this study, it was found that slope angle, elevation, land use/land cover, and roads play a major influencing role in the occurrence of landslide in the study area. The maps produced based on these landslide susceptibility models provide a base for engineers and land use planner to develop landslide management strategies before any development on slopes

Implementing Landslide Susceptibility Map at Watershed Scale of Lompobattang Mountain South Sulawesi, Indonesia

This study attempts to predict future landslide occurrence at watershed scale and calculate the potency of landslide for each sub-watershed at Lompobatang Mountain. In order to produce landslide susceptibility map (LSM) using the statistical model on the watershed scale, we identified the landslide with landslide inventories that occurred in the past, and predict the prospective future landslide occurrence by correlating it with landslide causal factors. In this study, six parameters were used namely, distance from fault, slope, aspect, curvature, distance from river and land use. This research proposed the weight of evidence (WoE) model to produce a landslide susceptibility map. Success and predictive rate were also used to evaluate the accuracy by using Area under curve (AUC) of Receiver operating characteristic (ROC). The result is useful for land use planner and decision makers, in order to devise a strategy for disaster mitigation

Soil erosion in the Alps : causes and risk assessment

The issue of soil erosion in the Alps has long been neglected due to the low economic value of the agricultural land. However, soil stability is a key parameter which affects ecosystem services like slope stability, water budgets (drinking water reservoirs as well as flood prevention), vegetation productivity, ecosystem biodiversity and nutrient production. In alpine regions, spatial estimates on soil erosion are difficult to derive because the highly heterogeneous biogeophysical structure impedes measurement of soil erosion and the applicability of soil erosion models. However, remote sensing and geographic information system (GIS) methods allow for spatial estimation of soil erosion by direct detection of erosion features and supply of input data for soil erosion models. Thus, the main objective of this work is to address the problem of soil erosion risk assessment in the Alps on catchment scale with remote sensing and GIS tools. Regarding soil erosion processes the focus is on soil erosion by water (here sheet erosion) and gravity (here landslides). For these two processes we address i) the monitoring and mapping of the erosion features and related causal factors ii) soil erosion risk assessment with special emphasis on iii) the validation of existing models for alpine areas. All investigations were accomplished in the Urseren Valley (Central Swiss Alps) where the valley slopes are dramatically affected by sheet erosion and landslides. For landslides, a natural susceptibility of the catchment has been indicated by bivariate and multivariate statistical analysis. Geology, slope and stream density are the most significant static landslide causal factors. Static factors are here defined as factors that do not change their attributes during the considered time span of the study (45 years), e.g. geology, stream network. The occurrence of landslides might be significantly increased by the combined effects of global climate and land use change. Thus, our hypothesis is that more recent changes in land use and climate affected the spatial and temporal occurrence of landslides. The increase of the landslide area of 92% within 45 years in the study site confirmed our hypothesis. In order to identify the cause for the trend in landslide occurrence time-series of landslide causal factors were analysed. The analysis revealed increasing trends in the frequency and intensity of extreme rainfall events and stocking of pasture animals. These developments presumably enhanced landslide hazard. Moreover, changes in land-cover and land use were shown to have affected landslide occurrence. For instance, abandoned areas and areas with recently emerging shrub vegetation show very low landslide densities. Detailed spatial analysis of the land use with GIS and interviews with farmers confirmed the strong influence of the land use management practises on slope stability. The definite identification and quantification of the impact of these non-stationary landslide causal factors (dynamic factors) on the landslide trend was not possible due to the simultaneous change of several factors. The consideration of dynamic factors in statistical landslide susceptibility assessments is still unsolved. The latter may lead to erroneous model predictions, especially in times of dramatic environmental change. Thus, we evaluated the effect of dynamic landslide causal factors on the validity of landslide susceptibility maps for spatial and temporal predictions. For this purpose, a logistic regression model based on data of the year 2000 was set up. The resulting landslide susceptibility map was valid for spatial predictions. However, the model failed to predict the landslides that occurred in a subsequent event. In order to handle this weakness of statistic landslide modelling a multitemporal approach was developed. It is based on establishing logistic regression models for two points in time (here 1959 and 2000). Both models could correctly classify >70% of the independent spatial validation dataset. By subtracting the 1959 susceptibility map from the 2000 susceptibility map a deviation susceptibility map was obtained. Our interpretation was that these susceptibility deviations indicate the effect of dynamic causal factors on the landslide probability. The deviation map explained 85% of new independent landslides occurring after 2000. Thus, we believe it to be a suitable tool to add a time element to a susceptibility map pointing to areas with changing susceptibility due to recently changing environmental conditions or human interactions. In contrast to landslides that are a direct threat to buildings and infrastructure, sheet erosion attracts less attention because it is often an unseen process. Nonetheless, sheet erosion may account for a major proportion of soil loss. Soil loss by sheet erosion is related to high spatial variability, however, in contrast to arable fields for alpine grasslands erosion damages are long lasting and visible over longer time periods. A crucial erosion triggering parameter that can be derived from satellite imagery is fractional vegetation cover (FVC). Measurements of the radiogenic isotope Cs-137, which is a common tracer for soil erosion, confirm the importance of FVC for soil erosion yield in alpine areas. Linear spectral unmixing (LSU), mixture tuned matched filtering (MTMF) and the spectral index NDVI are applied for estimating fractional abundance of vegetation and bare soil. To account for the small scale heterogeneity of the alpine landscape very high resolved multispectral QuickBird imagery is used. The performance of LSU and MTMF for estimating percent vegetation cover is good (r²=0.85, r²=0.71 respectively). A poorer performance is achieved for bare soil (r²=0.28, r²=0.39 respectively) because compared to vegetation, bare soil has a less characteristic spectral signature in the wavelength domain detected by the QuickBird sensor. Apart from monitoring erosion controlling factors, quantification of soil erosion by applying soil erosion risk models is done. The performance of the two established models Universal Soil Loss Equation (USLE) and Pan-European Soil Erosion Risk Assessment (PESERA) for their suitability to model erosion for mountain environments is tested. Cs-137 is used to verify the resulting erosion rates from USLE and PESERA. PESERA yields no correlation to measured Cs-137 long term erosion rates and shows lower sensitivity to FVC. Thus, USLE is used to model the entire study site. The LSU-derived FVC map is used to adapt the C factor of the USLE. Compared to the low erosion rates computed with the former available low resolution dataset (1:25000) the satellite supported USLE map shows “hotspots” of soil erosion of up to 16 t ha-1 a-1. In general, Cs-137 in combination with the USLE is a very suitable method to assess soil erosion for larger areas, as both give estimates on long-term soil erosion. Especially for inaccessible alpine areas, GIS and remote sensing proved to be powerful tools that can be used for repetitive measurements of erosion features and causal factors. In times of global change it is of crucial importance to account for temporal developments. However, the evaluation of the applied soil erosion risk models revealed that the implementation of temporal aspects, such as varying climate, land use and vegetation cover is still insufficient. Thus, the proposed validation strategies (spatial, temporal and via Cs-137) are essential. Further case studies in alpine regions are needed to test the methods elaborated for the Urseren Valley. However, the presented approaches are promising with respect to improve the monitoring and identification of soil erosion risk areas in alpine regions

Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling-Narayanghat 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-Narayanghat 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-Narayanghat road section. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose.ArticleNATURAL HAZARDS. 65(1):135-165 (2013)journal articl

Landslide riskscapes in the Colorado Front Range: a quantitative geospatial approach for modeling human-environment interactions

2021 Spring.Includes bibliographical references.This research investigated the application of riskscapes to landslides in the context of geospatial inquiry. Riskscapes are framed as a landscape of risk to represent risk spatially. Geospatial models for landslide riskscapes were developed to improve our understanding of the spatial context for landslides and their risks as part of the system of human-environment interactions. Spatial analysis using Geographic Information Systems (GIS) leveraged modeling methods and the distributed properties of riskscapes to identify and preserve these spatial relationships. This dissertation is comprised of four separate manuscripts. These projects defined riskscapes in the context of landslides, applied geospatial analyses to create a novel riskscape model to introduce spatial autocorrelation methods to the riskscape framework, compared geostatistical analysis methods in these landslide riskscape assessments, and described limitations of spatial science identified in the riskscape development process. The first project addressed the current literature for riskscapes and introduced landslides as a measurable feature for riskscapes. Riskscapes are founded in social constructivist theory and landslide studies are frequently based on quantitative risk assessment practices. The uniqueness of a riskscape is the inclusion of human geography and environmental factors, which are not consistently incorporated in geologic or natural hazard studies. I proposed the addition of spatial theory constructs and methods to create spatially measurable products. I developed a conceptual framework for a landslide riskscape by describing the current riskscape applications as compared to existing landslide and GIS risk model processes. A spatial modeling formula to create a weighted sum landslide riskscape was presented as a modification to a natural hazard risk equation to incorporate the spatial dimension of risk factors. The second project created a novel method for three geospatial riskscapes as an approach to model landslide susceptibility areas in Boulder and Larimer Counties, Colorado. This study synthesized physical and human geography to create multiple landslide riskscape models using GIS methods. These analysis methods used a process model interface in GIS. Binary, ranked, and human factor weighted sum riskscapes were created, using frequency ratio as the basis for developing a weighting scheme. Further, spatial autocorrelation was introduced as a recommended practice to quantify the spatial relationships in landslide riskscape development. Results demonstrated that riskscapes, particularly those for ranked and human factor riskscapes, were highly autocorrelated, non-random, and exhibited clustering. These findings indicated that a riskscape model can support improvements to response modeling, based on the identification of spatially significant clustering of hazardous areas. The third project extended landslide riskscapes to measurable geostatistical comparisons using geostatistical tools within a GIS platform. Logistic regression, weights of evidence, and probabilistic neural networks methods were used to analyze the weighted sum landslide riskscape models using ArcGIS and Spatial Data Modeler (ArcSDM). Results showed weights of evidence models performed better than both logistic regression and neural networks methods. Receiver Operator Characteristic (ROC) curves and Area Under the Curve validation tests were performed and found the weights of evidence model performed best in both posterior probability prediction and AUC validation. A fourth project was developed based on the limitations discovered during the analytical process evaluations from the riskscape model development and geostatistical analysis. This project reviewed the issues with data quality, the variations in results predicated on the input parameters within the analytical toolsets, and the issues surrounding open-source application tools. These limitations stress the importance of parameter selection in a geospatial analytical environment. These projects collectively determined methods for riskscape development related to landslide features. The models presented demonstrate the importance and influence of spatial distributions on landslide riskscapes. Based on the proposed conceptual framework of a spatial riskscape for landslides, weighted sum riskscapes can provide a basis for prioritization of resources for landslides. Ranked and human factor riskscapes indicate the need to provide planning and protection for areas at increased risk for landslides. These studies provide a context for riskscapes to further our understanding of the benefits and limitations of a quantitative riskscape approach. The development of a methodological framework for quantitative riskscape models provides an approach that can be applied to other hazards or study areas to identify areas of increased human-environment interaction. Riskscape models can then be evaluated to inform mitigation and land-use planning activities to reduce impacts of natural hazards in the anthropogenic environment