High Susceptibility Zones Research Articles

Hazard risk assessment of landslide-prone sub-Himalayan region by employing geospatial modeling approach

Ecological processes such as landslide disaster are primarily impacted by both geological and land use activities happening at different spatiotemporal scale. They specifically impact the socioeconomic development in the sub-Himalayan region of Pakistan. Ecological risk assessment and restrictive zonation mapping are therefore prerequisites for mitigation and compensation of landslide losses and risks. The current study has mapped landslide vulnerable Guoien Nullah road located in sub-Himalaya ranges of Pakistan via Landslide Hazard Zonation (LHZ) technique. Landslide active points were identified through field surveys, verified through the readings taken from Global Positioning System and mapped through geographic information system-based software. Seven landslide activating factors, i.e., slope angle, lithology, precipitation, tilt direction, land use/land cover, Normalized Differentiation Vegetation Index and elevation, were evaluated. Analytical hierarchical process (AHP) was used to assign weights to each activating factor. Precipitation, followed by lithology, and slope angle were found most influential in instigating landslide as indicated by AHP. Furthermore, three susceptibility zones were obtained using the weighted overlay method, i.e., high hazard susceptibility zone, moderate hazard susceptibility zone and low hazard susceptibility zone. The study concluded that 69% of the total landslide occurred in the high susceptibility zone which comprised of 36% of the total study area. Ground control points on active landslide sites authenticated the LHZ generated map. These maps help in restrictive zoning in the high hazard zone area that is considered important for engineers, geologists, and land use planners for future area-specific developmental projects. Besides, the current study will aid disaster management authorities, developers, policy makers and researchers in identifying the hazard disposal of the selected area.

Landslide susceptibility assessment in mountainous area: a case study of Sichuan–Tibet railway, China

In the southeastern Tibetan Plateau with complex geological conditions, the frequent and disastrous geological hazards have posed a severe challenge to the construction and operation safety of the planning Sichuan–Tibet railway. Based on the remote sensing and field investigation, the detailed landslide inventory of Jiacha–Langxian segment of Sichuan–Tibet railway was established. After analyzing the general development characteristics of landslides, a total of seven causative variables were selected as input parameters to evaluate landslide susceptibility using the weight of the evidence model. Combined with the probability prediction map and field validation, the landslide susceptibility was classified into four categories: very high, high, moderate and low susceptibility. Based on the landslide susceptibility assessment map, the very high and high susceptibility zones are mainly distributed on the both sides of the Yarlung Zangbo river and its tributaries, and the moderate and low susceptibility zones are located 5 km north to the river. Considering the terrain and landslide disaster prone situation in this region, the planning railway line in tunnel on the north bank of the Yarlung Zangbo river is considered to be reasonable. As for the subgrade and bridge section of the railway, especially for tunnel entrance, necessary slope reinforcement should be carried out. Considering the objective safety threat from landslides to the railway, we hold the opinion that it is necessary to compare the scheme of changing route and taking engineering protection measures in the Langzhen–Langxian segment.

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.

Landslide susceptibility zonation mapping using GIS-based frequency ratio model with multi-class spatial data-sets in the Adwa-Adigrat mountain chains, northern Ethiopia

Different organizations reported that natural hazards related disasters killed hundreds of people and damaged infrastructures estimated millions of dollars every year worldwide. The purpose of this research was to generate landslide susceptibility zonation (LSZ) map for the first time and understand the main landside conditioning factors in the Adwa-Adigrat mountain chain (about 2540 km2). At initial stage, landslide inventory map was prepared using classical fieldwork, stereo aerial photographic analysis and interpretations (API) and Google Earth™ (GE) image interpretations. A total of 546 landslides were identified, located and delineated. Rock fall was the dominant type of landslide (170), while rock slide was the smallest (only 28 rock slides). Nearly 7.6% of the total study area is found to be affected by past and active landslides. Five spatial data-sets or landslide conditioning factors: lithology, land use and land cover, distance from streams, distance from lineament/faults, slope aspect and slope angle maps were prepared and analyzed. Frequency ratio (FR) model was used to generate LSZ map. The generated LSZ map was classified into very low, low, moderate, high and very high susceptibility zones. The validation and verification of the LSZ map was carried out using the accuracy statistics approach and the area under the curve (AUC) methods. Both methods confirmed the reliability and good fit of the FR method for the present study area. It was observed that 73.8% of the past landslides fell in the high and very high susceptibility zones. In mountain chains, field studies for landslide inventory and LSZ mapping is important. The produced LSZ map can help to predict and mitigate landslide related damages and risks, and future land use planning.

Landslide Susceptibility Zonation Using Bivariate Models, Around Tehri Reservoir, Uttarakhand, India

Uttarakhand, the 27th state of India, is highly susceptible to landslides probably owing to its 86% area in Himalayan terrain. In recent times, however, the landslide incidents have increased leaps and bounds mainly due to unprecedented human interventions in the form of settlements, farming, road construction, myriad of hydroelectricity projects. One such case study is done in the current study around the Tehri Dam reservoir, Uttarakhand, India. Landslide causative factors such as slope, aspect, lithology, geology and geomorphology are derived using remote sensing techniques. Thereafter, two methods, Information Value (IV) and weight of evidence (WofE) model were applied and the output was reclassified into five zones viz. very low, low, moderate, high and very high. The validation of these models was performed using area under curve (AUC) analysis, which shows the accuracy of WofE model was 83% while that of IV model was 81%. Both WofE and IV susceptibility map shows 1.95% area under very high susceptibility zone which mostly covers the area bordering the reservoir hence implementing reservoir rim to be most prone for landslides.

Landslide Susceptibility Evaluation and Management Using Different Machine Learning Methods in The Gallicash River Watershed, Iran

This analysis aims to generate landslide susceptibility maps (LSMs) using various machine learning methods, namely random forest (RF), alternative decision tree (ADTree) and Fisher’s Linear Discriminant Function (FLDA). The results of the FLDA, RF and ADTree models were compared with regard to their applicability for creating an LSM of the Gallicash river watershed in the northern part of Iran close to the Caspian Sea. A landslide inventory map was created using GPS points obtained in a field analysis, high-resolution satellite images, topographic maps and historical records. A total of 249 landslide sites have been identified to date and were used in this study to model and validate the LSMs of the study region. Of the 249 landslide locations, 70% were used as training data and 30% for the validation of the resulting LSMs. Sixteen factors related to topographical, hydrological, soil type, geological and environmental conditions were used and a multi-collinearity test of the landslide conditioning factors (LCFs) was performed. Using the natural break method (NBM) in a geographic information system (GIS), the LSMs generated by the RF, FLDA, and ADTree models were categorized into five classes, namely very low, low, medium, high and very high landslide susceptibility (LS) zones. The very high susceptibility zones cover 15.37% (ADTree), 16.10% (FLDA) and 11.36% (RF) of the total catchment area. The results of the different models (FLDA, RF, and ADTree) were explained and compared using the area under receiver operating characteristics (AUROC) curve, seed cell area index (SCAI), efficiency and true skill statistic (TSS). The accuracy of models was calculated considering both the training and validation data. The results revealed that the AUROC success rates are 0.89 (ADTree), 0.92 (FLDA) and 0.97 (RF) and predication rates are 0.82 (ADTree), 0.79 (FLDA) and 0.98 (RF), which justifies the approach and indicates a reasonably good landslide prediction. The results of the SCAI, efficiency and TSS methods showed that all models have an excellent modeling capability. In a comparison of the models, the RF model outperforms the boosted regression tree (BRT) and ADTree models. The results of the landslide susceptibility modeling could be useful for land-use planning and decision-makers, for managing and controlling the current and future landslides, as well as for the protection of society and the ecosystem.

GIS-based landslide susceptibility mapping using heuristic and bivariate statistical methods for Iva Valley and environs Southeast Nigeria.

Udi-Iva Valley region of Enugu state has the most concentration of landslide in Southeastern Nigeria. Detailed field investigations alongside satellite image studies were employed to delineate nine landslide conditioning factors. Lithology, elevation, slope, aspect, curvature, distance from drainage, distance from road, land cover, and distance from lineament have been chosen as the landslide causative factors in the study area. This study presents a susceptibility mapping of landslides involving a combined bivariate statistical: frequency ratio (FR) and heuristic analytical hierarchy process (AHP) approach integrated in GIS environment. Validation and cross-validation of the susceptibility maps thus produced was achieved with the aid of landslide density approach in combination with prediction rate curve to check for the uniformity in the class areas in the susceptibility model produced. The analytical hierarchy process (AHP) produced results in which the lithology and slope factors had highest weights of 0.17 and 0.14 respectively. A strong correlation was observed in the lithology and slope conditioning factors; this is evident in the results of the FR approach with 10.68 and 6.86 FR values respectively. The landslide susceptibility maps were classified into five classes: very low susceptibility, low susceptibility, medium susceptibility, high susceptibility and very high susceptibility. Prediction rate curve was used to assess the predictive potential of the landslide susceptibility models, the result showed area under curve values of 70.49% for AHP and 72.09% for FR method. The similarity in the landslide density distribution in the susceptibility class, indicates a correlation between the generated susceptibility model and field observations. The statistical and heuristic methods employed have produced positive results; this was confirmed by the fact that all the 300 landslides were found to have occurred within the high susceptibility and very high susceptibility zones respectively.

GIS based frequency ratio method for landslide susceptibility mapping at Da Lat City, Lam Dong province, Vietnam

Landslide susceptibility mapping of the city of Da Lat, which is located in the landslide prone area of Lam Dong province of Central Vietnam region, was carried out using GIS based frequency ratio (FR) method. There are number of methods available but FR method is simple and widely used method for landslide susceptibility mapping. In the present study, eight topographical and geo-environmental landslide-conditioning factors were used including slope, elevation, land use, weathering crust, soil, lithology, distance to geology features, and stream density in conjunction with 70 past landslide locations. The results show that 6.27% of the area is in the very low susceptibility area, 21.03% in the low susceptibility area, 27.09% in the moderate susceptibility area and 27.41% of the area is in the high susceptibility zone and 18.21% in the very high susceptibility zone. The landslide susceptibility map produced in this study helps to assist decision makers in proper land use management and planning.

Landslide susceptibility mapping by fuzzy gamma operator and GIS, a case study of a section of the national road n°11 linking Mateur to Béja (Nortshern Tunisia)

The purpose of this paper is to map the landslide susceptibility using the fuzzy gamma operator and GIS for the section of the national road n°11 linking Mateur to Beja. To minimize the subjectivity of the fuzzy logic approach, the frequency ratio was used to calculate the fuzzy membership. To locate the 147 landslides, Google Earth extracts analysis, and field survey has been done. In this cartography, nine factors controlling landslides were used: slope, aspect, curvature plane, curvature profile, distance to faults, distance to rivers, land use, rainfall, and lithology. Once the fuzziness is calculated in each factor using the frequency ratio (FR), the fuzzy gamma operator can be applied to these nine factors to produce the landslide susceptibility map. Finally, the choice of the nearest susceptibility map of reality is made by applying the ROC curve. By the analysis of the area under curve (AUC), it is seen that the prediction accuracy of model proves that the gamma value of 0.90 yielded the better prediction of landslide susceptibility map (0.89). Finally, our area of interest was classified based on the Jenks natural breaks classification method into five classes, such as non-susceptible zone, low susceptible zone, moderate susceptible zone, high susceptible zone, and very high susceptible zone. Landslide susceptibility mapping based on the fuzzy gamma operator presents a very acceptable result with a reliability of 89%.

Exposure and loss assessment of soil liquefaction in coastal area of Kulon Progo, Indonesia

Soil Liquefaction is a phenomenon of loss of strength of the granural soil layers due to increased pore water stress caused by earthquake shocks. Soil liquefaction can cause material and life damage if occurs in the developed area. Kulon Progo Regency based on the Atlas of Liquefaction Susceptibility Zones in 2019, has high susceptibility zones, which has the potential for flow liquefaction, lateral spreading, vertical displacement, and sand boil. This study aims to assess the exposure and loss index in liquefaction hazard zone based on the characteristics of land use and social demographic. The exposure index is obtained from overlaying between susceptibility map and liquefaction exposure variables, when the loss assessment is done by simulating the losses in several earthquake moment magnitude scenarios. Study results show that high exposure surrounding the residential zone in the south of the Wates Urban Area and the construction location of the Yogyakarta International Airport. There are settlement areas potentially affected by lateral spreading in Glagah, Karangwuni, Banaran, and Karangsewu Villages. While the results of the loss assessment show that transport infrastructure and residential buildings are the most affected objects when liquefaction phenomena occur due to the earthquake. Managing the expansion of settlement area through zoning regulation and technical engineering approach is needed to reduce losses due to future liquefaction phenomenon.

FOREST FIRE SUSCEPTIBILITY MODELLING USING REMOTE SENSING DATA, GIS AND AHP ANALYSIS (CASE STUDY: SOUHAN, ALGERIA)

The ecosystem loses significant surfaces of forest every year because of wildfires, In Algeria, the majority of wildfires occurrences particularly during summertime, to the point of becoming a matter of national concern due the great losses of vegetal covers they cause every year. in this article we will focus on the municipality of Souhane, largely composed of forest areas and greatly affected by wildfires almost every year. The purpose of our study is to obtain forest fire susceptibility map in this municipality, and this, by combining a geographic information system and the AHP method, several studies have proven the effectiveness of this technique on large areas, in our study, we are willing to verify its usefulness at the scale of a small municipality and its surroundings, and this, to determine the susceptibility of the entire zone to wildfires, in order to do so, we selected six particular factors, calculating than their weights using the AHP method. The obtained results indicated that 49.5 % of the total surface of this municipality is included in both high and very high susceptibility zones, The wildfires that occurred during the year 2019 confirming the relevance of the results obtained, moreover, the calculation of the combustion index has shown that the areas most affected by the fire are those located once again, in the so-called areas of high and very high susceptibility.

Groundwater Salinity Susceptibility Mapping Using Classifier Ensemble and Bayesian Machine Learning Models

Risk and susceptibility mapping of groundwater salinity (GWS) are challenging tasks for groundwater quality monitoring and management. Advancement of accurate prediction systems is essential for the identification of vulnerable areas in order to raise awareness about the potential salinity susceptibility and protect the groundwater and top-soil in due time. In this study, three machine learning models of Stochastic Gradient Boosting (StoGB), Rotation Forest (RotFor), and Bayesian Generalized Linear Model (Bayesglm) are developed for building prediction models and their performance evaluated in the delineation of salinity susceptibility maps. Both natural and human effective factors (16 features) were used as predictors for groundwater salinity modeling and were randomly divided into the training (80%) and testing (20%) datasets. The models were evaluated using testing datasets after calibration using the selected features by recursive feature elimination (RFE) method. The RFE indicated that modeling with 8 features had better performance among 1 to 16 features (Accuracy = 0.87). Results of the groundwater salinity prediction highlighted that StoGB had a good performance, whereas the RotFor and Bayesglm had an excellent performance based on the Kappa values (>0.85). Although spatial prediction of the models was different, all of the models indicated that central parts of the region have a very high susceptibility which matches with agricultural areas, lithology map, the locations with low depth to groundwater, low slope, and elevation. Additionally, areas near to the Maharlu lake and locations with a high decline in groundwater are also located in the very high susceptibility zone, which can confirm the effects of saltwater intrusion. The susceptibility maps produced in this study are of utmost importance for water security and sustainable agriculture.

Landslide susceptibility assessment in a lesser Himalayan road corridor (India) applying fuzzy AHP technique and earth-observation data

The Kalsi-Chakrata road corridor, located in the Lesser Himalayas, experiences several landslides every year, resulting in a considerable amount of damage to roads, assets, and other infrastructure and even loss of lives. During the monsoon season (June–August), the disruption of routes due to landslide resulted in economic losses and barred villagers from accessing critical and essential facilities, thus impacting the livelihood of the communities residing along the road corridor. Hence immediate requirement was to systematically assess the landslide susceptibility for the study area that would support in the preparation of the planning and mitigation goal, both short and long term. The present study adopted the fuzzy analytic hierarchy process (fuzzy AHP) method integrated with geospatial technology that may be highly effective for landslide susceptibility assessment in the landslide-prone Lesser Himalayas. The use of validated landslide inventory data and high-resolution remote sensing images for selection and mapping of landslide conditioning factors gratifies the geospatial aspect of local variations across the Kalsi-Chakrata road corridor. For fuzzy AHP model setup, prominent landslide contributing factors viz., slope, aspect, altitude, lithology, proximity to road, fault & drainage, Stream Power Index (SPI), Topographical Wetness Index (TWI), rainfall, land use/land cover (LULC), soil, and seismicity, were mapped and classified into significant classes. The resultant landslide susceptibility map (LSM) shows that about 55% (45.23 km2) of the study area was categorized as a very high and high landslide susceptibility zone. Of this, about 21% (17.7 km2) was within very high LSM zone, while 33% (27.5 km2) fell under high landslide susceptibility categories. Approximately 17.6% (14.5 km2) areas fall within the moderately susceptible zone, where chances of future landslides may be amplified without periodic observation and prospective study. At the village level, it was observed that Jhutaya village, located nearer foothill of Lesser Himalayas, is most susceptible to landslide followed by Dhaira, Chapanu, and Sairi villages. The fuzzy AHP model shows 86.52% accuracy in landslide prediction evaluated through the ROC curve (at 95% confidence level). Hence, the output LSI may be referred by the planners and engineers for monitoring, prevention, and mitigation of landslide hazards and development of infrastructure in the Kalsi-Chakrata road corridor, and the fuzzy AHP methodology adopted may be applied in other areas of the Lesser Himalayas.

A Novel Ensemble Approach for Landslide Susceptibility Mapping (LSM) in Darjeeling and Kalimpong Districts, West Bengal, India

Landslides are among the most harmful natural hazards for human beings. This study aims to delineate landslide hazard zones in the Darjeeling and Kalimpong districts of West Bengal, India using a novel ensemble approach combining the weight-of-evidence (WofE) and support vector machine (SVM) techniques with remote sensing datasets and geographic information systems (GIS). The study area currently faces severe landslide problems, causing fatalities and losses of property. In the present study, the landslide inventory database was prepared using Google Earth imagery, and a field investigation carried out with a global positioning system (GPS). Of the 326 landslides in the inventory, 98 landslides (30%) were used for validation, and 228 landslides (70%) were used for modeling purposes. The landslide conditioning factors of elevation, rainfall, slope, aspect, geomorphology, geology, soil texture, land use/land cover (LULC), normalized differential vegetation index (NDVI), topographic wetness index (TWI), sediment transportation index (STI), stream power index (SPI), and seismic zone maps were used as independent variables in the modeling process. The weight-of-evidence and SVM techniques were ensembled and used to prepare landslide susceptibility maps (LSMs) with the help of remote sensing (RS) data and geographical information systems (GIS). The landslide susceptibility maps (LSMs) were then classified into four classes; namely, low, medium, high, and very high susceptibility to landslide occurrence, using the natural breaks classification methods in the GIS environment. The very high susceptibility zones produced by these ensemble models cover an area of 630 km2 (WofE& RBF-SVM), 474 km2 (WofE& Linear-SVM), 501km2 (WofE& Polynomial-SVM), and 498 km2 (WofE& Sigmoid-SVM), respectively, of a total area of 3914 km2. The results of our study were validated using the receiver operating characteristic (ROC) curve and quality sum (Qs) methods. The area under the curve (AUC) values of the ensemble WofE& RBF-SVM, WofE & Linear-SVM, WofE & Polynomial-SVM, and WofE & Sigmoid-SVM models are 87%, 90%, 88%, and 85%, respectively, which indicates they are very good models for identifying landslide hazard zones. As per the results of both validation methods, the WofE & Linear-SVM model is more accurate than the other ensemble models. The results obtained from this study using our new ensemble methods can provide proper and significant information to decision-makers and policy planners in the landslide-prone areas of these districts.

LANDSLIDE SUSCEPTIBILITY ASSESSMENT AROUND BABADAĞ (DENİZLİ) TOWN USING LOGISTIC REGRESSION METHOD

In this study landslide susceptibility assessments were evaluated using logistic regression method around the Babadağ town extending to 1133 km2. Almost 300 slide type landslides corresponding 44km2 were identified in the area. Some of the residential areas, agricultural lands and lifeline systems has been severely damaged by landslides and more than five hundreds dwellings were evacuated in the region. Among the landslide conditioning factors; elevation, slope, plan and profile curvatures, proximity to faults and rivers and geological maps have been prepared in Geographical Information Systems environment with spatial resolution of 25 m The performance of the susceptibility map when compared to the landslide inventory revealed that high and very high susceptible zones correspond to 23 % of the study area including 72.13% of the recorded landslides.

Analyzing the effects of tectonic and lithology on the occurrence of landslide along Zagros ophiolitic suture: a case study of Sarv-Abad, Kurdistan, Iran

Assessment of the landslide susceptibility can provide useful information in the management and mitigation of the negative consequences of this phenomenon. The purpose of this study was to map the landslide susceptibility and to highlight the effects of tectonic and lithology. The landslide-causing factors including distance from faults, lithology, slope gradient, slope aspect, distance from drainages, distance from roads, land use, and precipitation were used for the landslide susceptibility mapping by using analytical hierarchical process (AHP) method. Based on the landslide susceptibility zonation map, the study area was grouped into five susceptibility classes including very low, low, moderate, high, and very high. High values of landslide density in very high and high susceptible zone, and the high percentage of area under the success rate curve (72.65%) show that the AHP method has accurately predicted the landslide susceptibility zonation map of the study area. Data reveal that most landslides are close to the Zagros Main Recent Fault (MRF) and the Main Zagros Reverse Fault (MZRF) that merge with each other along the trend of Azad River. These faults have locally produced zones of extension and contraction that have caused the intense faulting and fracturing of rocks and hence the intensification of the landslides. The Oligo-Miocene marl (with high potential of plasticity, expanding, and cracking), the Oligo-Miocene limestone (with steep dip slopes and strike-slip faults) that overlies the Oligo-Miocene marls as a lubricant, and serpentinites (with high capacity of flexibility, plasticity, and sheeting) are three important lithological units that are more prone to landsliding.

Application of logistic regression (LR) and frequency ratio (FR) models for landslide susceptibility mapping in Relli Khola river basin of Darjeeling Himalaya, India

Landslide is one of the important disasters taking place on earth, which may be either a natural or man-made process. Landslides are more active and disastrous in hilly and mountainous regions. The present study aims to identify the landslide susceptibility areas in the Relli river basin in Darjeeling Himalaya using logistic regression (LR) and frequency ratio (FR) models. The GIS techniques have been used for landslide susceptibility mapping. A total number of 67 landslide locations have been identified from Google Earth images and multiple field surveys. 70% of landslide locations have been randomly selected and used as training data set for preparing landslide susceptibility map, and the remaining 30% have been used as validation data set. For the present study, 20 different factors like drainage density, drainage texture, infiltration number, stream frequency, stream junction frequency, stream power index, lithology, soil, relative relief, slope, maximum relief, drainage intensity, ruggedness number, rainfall, dissection index, aspect, relief class, and distance from stream, topographic wetness index and land use land cover have been used. The application of the logistic regression and frequency ratio model has demonstrated that the lower catchment of the basin has been widely dominated by the most landslide susceptibility areas than other parts of the catchment. Almost 6.92 sq km (4.05%) and 7.44 sq km (4.36%) areas out of 170.61 sq km area of the basin have been observed as very high and high landslide susceptibility categories, respectively, for FR model and 5.75 sq km (3.37%) and 1.86 sq km (1.09%) areas have been under very high and high landslide susceptibility zones for LR model. Finally, the ROC curve has been used to validate the models. The prediction capabilities of the models seem significant as the area under the curve value ranges from 75 to 81%.

Applying a Series and Parallel Model and a Bayesian Networks Model to Produce Disaster Chain Susceptibility Maps in the Changbai Mountain area, China

The aim of this project was to produce an earthquake–landslide debris flow disaster chain susceptibility map for the Changbai Mountain region, China, by applying data-driven model series and parallel model and Bayesian Networks model. The accuracy of these two models was then compared. Parameters related to the occurrence of landslide and debris flow disasters, including earthquake intensity, rainfall, elevation, slope, slope aspect, lithology, distance to rivers, distance to faults, land use, and the normalized difference vegetation index (NDVI), were chosen and applied in these two models. Disaster chain susceptibility zones created using the two models were then contrasted and verified using the occurrence of past disasters obtained from remote sensing interpretations and field investigations. Both disaster chain susceptibility maps showed that the high susceptibility zones are situated within a 10 km radius around the Tianchi volcano, whereas the northern and southwestern sections of the study area comprise primarily very low or low susceptibility zones. The two models produced similar and compatible results as indicated by the outcomes of basic linear correlation and cross-correlation analyses. The verification results of the ROC curves were found to be 0.7727 and 0.8062 for the series and parallel model and BN model, respectively. These results indicate that the two models can be used as a preliminary base for further research activities aimed at providing hazard management tools, forecasting services, and early warning systems.

Use of Very High-Resolution Optical Data for Landslide Mapping and Susceptibility Analysis along the Karnali Highway, Nepal

The Karnali highway is a vital transport link and the only primary roadway that connects the remote Karnali region to the lowlands in Mid-Western Nepal. Every year there are reports of landslides blocking the road, making this area largely inaccessible. However, little effort has focused on systematically identifying landslides and landslide-prone areas along this highway. In this study, landslides were mapped with an object-based approach from very high-resolution optical satellite imagery obtained by the DigitalGlobe constellation in 2012 and PlanetScope in 2018. Landslides ranging from 10 to 30,496 m2 were detected within a 3 km buffer along the highway. Most of the landslides were located at lower elevations (between 500–1500 m) and on steep south-facing slopes. Landslides tended to cluster closer to the highway, near drainage channels and away from faults. Landslides were also most prevalent within the Kuncha Formation geologic class, and the forested and agricultural land cover classes. A susceptibility map was then created using a logistic regression methodology to highlight patterns in landslide activity. The landslide susceptibility map showed a good prediction rate with an area under the curve (AUC) of 0.90. A total of 33% of the study arealies in high/very high susceptibility zones. The map highlighted the lower elevated areas between Bangesimal and Manma towns with the Kuncha Formation geologic class as being the most hazardous. The banks of the Karnali River, its tributaries and areas near the highway were also highly susceptible to landslides. The results highlight the potential of very high-resolution optical imagery for documenting detailed spatial information on landslide occurrence, which enables susceptibility assessment in remote and data scarce regions such as the Karnali highway.

Assessment of landslide susceptibility along the Araniko Highway in Poiqu/Bhote Koshi/Sun Koshi Watershed, Nepal Himalaya

Landslide susceptibility assessment along the Araniko highway was done using the relationship between the landslide causative factor and presence/absence of landslide using linear discriminant analysis, and divided into Low, Medium, High, and Very High susceptibility zone. The spatial analysis of landslide distribution with its conditioning factors depicts 40°-60° of the slope, South-east and South direction of aspect, 0–5 km North from MCT, 10–20 km of distance from the epicentre, where barren land and forest area are found most susceptible to a landslide. This research can help undertake the proper mitigation and adaptation measures for the landslide risk along the Araniko highway.