High Susceptibility Zones Research Articles

Study on landslide susceptibility mapping with different factor screening methods and random forest models.

The number of input factors affects the prediction accuracy of a model. Factor screening plays an important role as the starting point for data input. The aim of this study is to explore the influence of different factor screening methods on the prediction results. Taking the 2014 landslide inventory of Jingdong County as an example, a landslide database was constructed based on 136 landslide events and 11 selected factors, which were randomly divided into a training dataset and a test dataset according to a ratio of 7:3. Four factor screening methods, namely, the information gain ratio (IGR), GeoDetector, Pearson correlation coefficient and multicollinearity test (MT), were selected to screen the factors. A random forest (RF) model was then used in combination with each factor set for landslide susceptibility mapping (LSM). Finally, accuracy validation was performed using confusion matrices and ROC curves. The results show that factor screening is beneficial in improving the accuracy of the resulting model compared to the original model. Second, the IGR_RF model had the highest AUC value (0.9334), which was higher than that of the MT_RF model without factor screening (0.9194), and the IGR_RF model predicted the most landslides in the very high susceptibility zone (51.22%), indicating the good prediction performance of the IGR_RF model. Finally, the factor weighting analysis revealed that NDVI, elevation and aspect had the greatest influence on landslides in Jingdong County and that curvature had the least influence on landslides. This study can provide a reference for factor screening in LSM.

Distribution characteristics of snow and evaluation of snowdrift susceptibility along the Xinku section of Yining-Aksu Railway in China

The frequent occurrence of snowdrift hazards in the Tianshan region of China often poses a serious threat to transportation corridors, such as roads and railways. Revealing and understanding the characteristics of snow distribution and using a mixed model to assess the susceptibility to snowdrifts can effectively and quickly evaluate the regional risk of the snowdrift. In this study, the key factors affecting the formation of a snowdrift in the study area, such as snow, are analyzed by utilizing remote sensing interpretation, meteorological monitoring, and field investigation along the Xinyuan-Kutaikelike railway section of Yining to Aksu Railway in China as the study area object. Based on the multi-source data, ten snowdrift susceptibility evaluation factors were extracted with the help of GIS. On this basis, three susceptibility models were used to evaluate the snowdrift susceptibility, namely the subjective evaluation method – Analytic Network Process (ANP) method, the objective evaluation method - CRITIC weighting method, and the combination of subjective and objective Game theory combination method. The evaluation showed that the susceptibility indices obtained by the ANP, CRITIC, and Game theory combination were in the ranges of [0.243, 0.746], [0.202, 0.751], and [0.249, 0.735] respectively, with the percentages of the area of medium susceptibility and above being 67.3%, 67.2%, and 67.5%. All three models exhibited suitable robustness. The predicted results of the three models were verified by the field survey, and it was found that the results of the Game theory combined model were in good agreement with the field survey, which allows it to be suitable for the study of snowdrift susceptibility zoning in the study area. The results revealed that the medium-high susceptibility areas of a snowdrift were mainly in the grassland area of the Small Yultuz Basin and the A'erxian-Bayinguoleng area. The high susceptibility zones were predominately located in the Nalati Ridge, the northern ridge of the Koktag Mountains, the northern ridge of the Huola Mountains, and the pre-mountain alluvial and floodplain areas of these mountain systems. This study provides a scientific basis and methodology for preventing and mitigating snowdrift disasters in cold regions, specifically for major engineering constructions such as transportation corridors.

Insights into large landslide mechanisms in tectonically active Agadir, Morocco: The significance of lithological, geomorphological, and soil characteristics

Landslide susceptibility assessment is crucial for land use planning, infrastructure development, and hazard mitigation, particularly in tectonically active regions where lithology and soil characteristics significantly influence slope stability. This study aims to evaluate landslide susceptibility in the Agadir Ida-Ou-Tanane province, a tectonically active area, using a hierarchical multicriteria analysis approach. Eight factors influencing landslide occurrence were considered: lithology, slope, LULC, distance to main faults, hypsometry, drainage density, road density, and aspect. Pairwise comparison matrices and expert opinion were employed to determine the relative importance of these factors. The consistency ratio (CR) was calculated to ensure the reliability of the judgments. The resulting landslide susceptibility map reveals a spatial distribution ranging from very low to very high susceptibility zones. Lithology, mainly clay, and limestone formations, emerged as the most influential factor due to the active tectonic context of the study area. The model was validated using observed landslide locations and the Area Under the Curve (AUC) method, yielding a reasonable validation accuracy. The findings indicate that integrating these factors, with a particular emphasis on lithology and soil characteristics, effectively identifies areas prone to landslides, making the resulting map a valuable tool for land use planning and risk management in the Agadir Ida-Ou-Tanane region. This study contributes to understanding landslide susceptibility in tectonically active areas and provides a practical foundation for future research and decision-making. Regularly monitoring and updating the landslide susceptibility map is necessary to ensure its continued relevance and utility for stakeholders.

Comparative analysis of certainty factor and analytic hierarchy process for landslide susceptibility zonation in parts of Solan, Himachal Pradesh, India

The state of Himachal Pradesh in India is one of the most important hotspots when it comes to landslides; and Kandaghat, a tehsil in the Solan district of Himachal Pradesh having religious and tourism importance, is substantially affected by frequent landslides causing road blocking. In the present study, the analytic hierarchy process (AHP) and certainty factor (CF) techniques, which form part of the geographic information system (GIS)-based landslide susceptibility models, were used to prepare a landslide susceptibility map for the Kandaghat region, for which, as a preliminary step, an inventory of 214 live landslides was prepared from the Bhukosh data directory. The landslide inventory was cross-verified on the Google Earth platform. About nine landslide causative factors (slope, curvature, aspect, soil, rainfall, land use–land cover, lithology, drainage density and lineament density) were considered for the study area, and against the backdrop of these, the corresponding thematic maps were prepared and used in turn for the preparation of the final landslide susceptibility map. Based on the two mentioned techniques, the thematic maps were assigned weights according to their prominence and dynamic processes in the study area. The model performance for each method was evaluated using the area under the curve (AUC), and the accuracies for the AHP and CF were ascertained as, respectively, 81% and 85.6%. The Himalayan terrains are significantly prone to landslides, and this study outlines the characteristics of one of the important Himalayan towns in terms of vulnerability for landslides, together with providing its classification in terms of slope deformation susceptibility; this procedure can help direct attention towards areas needing to be classified under high to very high landslide susceptibility zones.

INFLUENCE OF DIGITAL ELEVATION MODEL RESOLUTION ON MAPPING TERRITORY SUSCEPTIBILITY TO LANDSLIDE DEVELOPMENT

Link for citation: Van B. Duong, Fomenko I.K., Duc T. Ta, Trung K. Nguyen, Zerkal O.V., Gorobtsov D.N., Hong D. Vu. Influence of digital elevation model resolution on mapping territory susceptibility to landslide development. Bulletin of the Tomsk Polytechnic University. Geo Аssets Engineering, 2023, vol. 334, no. 8, рр. 164-181. In Rus. The relevance. Landslides are one of the most hazardous natural disasters in the world, causing significant economic damage and human deaths. For 20 years (1995–2014), 3876 landslides killed 163658 people and injured 11689 people, as reported by the U. Haque study (2019), based on the data from 128 countries. Excluding events triggered by earthquakes, the total number of fatal landslides from 2004 to 2016 was 4862, and most landslides (75 %) occurred in Asia. In Vietnam, particularly in the northern mountainous regions, landslides frequently caused considerable losses of life and property. The systematic evaluation of landslide hazards is a crucial component of Vietnam's disaster prevention strategy. The main aim of this study is to assess the landslide susceptibility of the Batxat district, Laocai province (Vietnam), using four digital elevation model resolutions (10, 30, 40, and 60 m) and nine landslide causative factors: slope aspect, elevation, slope, distance to roads, distance to faults, distance to drainage, average monthly precipitation, land use, and weathering crust. Object of the study is the landslide susceptibility in Batxat district, Laocai province, Vietnam. Methods: Certainty Factor method, fractal method. Results. The performed analyses indicated a statistically significant relationship between the distribution of landslides and the landslide causative factors in the study area. As a result, the territory was divided into five zones according to its susceptibility to the landslide process: very low, low, moderate, high, and very high. The very low landslide susceptibility zone is less than 10 % of the study area, while the very high susceptibility zone varies from 14,95 to 18,32 %, depending on the digital elevation model spatial resolution. Analysis of the Receiver Operating Characteristics curve revealed that all models, independent of digital elevation model resolution, have good prediction efficiency, with the area under the receiver operating characteristic curve over 70 %. In addition, the Receiver operating characteristic and fractal analysis results indicated that the model with a digital elevation model spatial resolution of 60 m has the highest accuracy. This significant conclusion may be explained by the fact that the accuracy of the landslide susceptibility assessment result is dependent not only on the digital elevation model resolution but also on the ratio of the digital elevation model resolution and the average area of landslides in the study area. This conclusion proves that it is necessary to depict landslide locations and their detailed morphological characteristics on the landslide inventory maps.

Application of index of entropy and Geospatial techniques for landslide prediction in Lunglei district, Mizoram, India

The present study focuses on developing a landslide susceptibility zonation (LSZ) using GIS-based bivariate statistical model in the Lunglei district of Mizoram. Initially, 17 factors were selected after calculating the multicollinearity test for LSZ. A landslide inventory map was created based on 234 historic landslide events, which were randomly divided into training (70%) and testing (30%) datasets. Using the Index of Entropy (IOE) model, nine causative factors were identified as having significant weightage for LSZ: elevation, slope, aspect, curvature, normalized difference vegetation index, geomorphology, distance to road, distance to lineament, and distance to river. On the other hand, factors such as land use land cover, stream power index, terrain ruggedness index, terrain roughness, topographic wetness index, annual rainfall, topographic position index, and geology had negligible weightage. Based on the relative importance of the causative factors, two models were developed: scenario 1, which considered nine factors, and scenario 2, which considered all 17 factors. The results revealed that 16% and 14% of the district area were identified as very highly landslide prone in scenario 1 and scenario 2, respectively. The high susceptibility zone accounted for 26% and 25% of the area in scenario 1 and scenario 2, respectively. To assess the accuracy of the models, a receiver operating characteristic (ROC) curve and quality sum ratio method was performed using 30% of the testing landslide data and an equal number of non-landslide data points. The area under the curve (AUC) for scenario 1 and scenario 2 were 0.947 and 0.922, respectively, indicating higher efficiency for scenario 1. The quality sum ratios were 0.435 and 0.43 for scenario 1 and scenario 2, respectively. Based on these results, the LSZ mapping from scenario 1 is considered suitable for policymakers to address development and risk reduction associated with landslides.

Artificial neural network for flood susceptibility mapping in Bangladesh

The objective of the research is to investigate flood susceptibility in the Sylhet division of Bangladesh. Eight influential factors (i.e., elevation, slope, aspect, curvature, TWI, SPI, roughness, and LULC) were applied as inputs to the model. In this work, 1280 samples were taken at different locations based on flood and non-flood characteristics; of these, 75% of the inventory dataset was used for training and 25% for testing. An artificial neural network was applied to develop a flood susceptibility model, and the results were plotted on a map using ArcGIS. According to the finding, 40.98% (i.e., 499433.50 hectors) of the study area is found within the very high-susceptibility zone, and 37.43% (i.e., 456168.76 hectors) are in the highly susceptible zone. Only 6.52% and 15% of the area were found in low and medium flood susceptibility zones, respectively. The results of model validation show that the overall prediction rate is around 89% and the overall model success rate is around 98%. The study's findings assist policymakers and concerned authorities in making flood risk management decisions in order to mitigate the negative impacts.

A hybrid machine learning model for landslide-oriented risk assessment of long-distance pipelines

Preparation of pipeline risk zoning is essential for pipeline construction and safe operation. Landslides are one of the main sources of risk to the safe operations of oil and gas pipelines in mountainous areas. This work aims to propose a quantitative assessment model of landslide-induced long-distance pipeline risk by analyzing historical landslide hazard data along oil and gas pipelines. Using the Changshou-Fuling-Wulong-Nanchuan (CN) gas pipeline dataset, two independent assessments were carried out: landslide susceptibility assessment and pipeline vulnerability assessment. Firstly, the study combined the recursive feature elimination and particle swarm optimization-AdaBoost method (RFE-PSO-AdaBoost) to develop a landslide susceptibility mapping model. The RFE method was used to select the conditioning factors, while PSO was used to tune the hyper-parameters. Secondly, considering the angular relationship between the pipelines and landslides, and the segmentation of the pipelines using the fuzzy clustering (FC), the CRITIC method (FC-CRITIC) was combined to develop a pipeline vulnerability assessment model. Accordingly, a pipeline risk map was obtained based on pipeline vulnerability and landslide susceptibility assessment. The study results show that almost 35.3% of the slope units were in extremely high susceptibility zones, 6.68% of the pipelines were in extremely high vulnerability areas, the southern and eastern pipelines segmented in the study area were located in high risk areas and coincided well with the distribution of landslides. The proposed hybrid machine learning model for landslide-oriented risk assessment of long-distance pipelines can provide a scientific and reasonable risk classification for new planning or in service pipelines to avoid landslide-oriented risk and ensure their safe operation in mountainous areas.

GIS-based machine learning algorithm for flood susceptibility analysis in the Pagla river basin, Eastern India

The unique characteristics of drainage conditions in the Pagla river basin cause flooding and harm the socioeconomic environment. The main purpose of this study is to investigate the comparative utility of six machine learning algorithms to improve flood susceptibility and ensemble techniques' capability to elucidate the underlying patterns of floods and make a more accurate prediction of flood susceptibilities in the Pagla river basin. In the present scenario, the frequency of flood conditions in this study area becomes high with heavy and sudden rainfall, so it is essential to study flood mitigation and measure. At first, a spatial flood database was built with 200 flood locations and sixteen flood influencing factors, and its process with the help of the Geographic Information System (GIS) environment and build up different models applying the machine learning techniques. It has found different flood susceptibility zone using machine learning-based Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), Reduced Error Pruning Tree (REPTree), Logistic Regression (LR), and Bagging helping GIS environment and the model validation using the Receiver Operating Characteristic (ROC) Curve. Afterward, ensemble all the models to gate comparative accuracy of the flood zone. The calculated areas are under the very high flood susceptibility zone 8.69%, 14.92%, 14.17%, 12.98%, 14.65%, 13.24% and 13.41% for ANN, SVM, RF, REPTree, LR and Bagging, respectively. Finally, ROC curve, the Standard Error (SE), and the Confidence Interval (CI) at 95 per cent were used to assess and compare the performance of the models. The obtained results indicate that all models are highly accepted Area Under Curve (AUC) of ROC between 0.889 (LR) to 0.926 (Ensemble). From the estimation of the accuracy of the applied methods using ROC, it is found that the Ensemble model has the higher capability compared to the other applied models in projecting flood susceptibility in ​the ​study ​area. It has the highest area under the ROC curve the AUC values are 0.918 and 0.926, the SE (0.023, 034), and the narrowest CI (95 per cent) (0.873–0.962, 0.859–0.993) whereas highest area under Bagging (the ROC) curve (AUC) value (0.914, 0.919), for both the training and validation datasets. After ensembling, the result shows that the result is a highly flood susceptible area located at the lower part of the study area. In this area, the very high flood susceptibility zone values lie between 4.46 and 6.00 in the ensemble result. The areas comprise the low height and belong to Murarai I, Murarai II, Suti I and Suti II C.D. block of West Bengal. The current study will help the policymakers and the researcher determine the flood conditioning problems for prospects.

Settlement Analysis in Potentially Liquifiable Soil at Kretek 2 Bridge Area

The Kretek 2 Bridge is one of the important infrastructures built on the southern coast road of Java. Referring to the Map of Indonesia Liquefaction Vulnerability Zone in 2019, Kretek 2 Bridge was identified as a high liquefaction susceptibility zone. The result of liquefaction potential analysis on the slab-on-pile section indicates that the soil layer has potential to liquefy if an earthquake occurs. Based on the Liquefaction Severity Index, the slab-on-pile section of the Kretek 2 Bridge has a high severity index at BH-10, moderate severity index at BH-11, and low severity index at BH-01 and BH-12. One of liquefaction effects is a ground settlement. Settlement due to liquefaction is a vertical soil deformation in the soil layer caused by compaction due to the earthquake. This study was conducted by calculating the settlement of sand deposits in slab-on-pile section using an equation proposed by Zhang et al. in 2002 and Yoshimine in 2006. The results of settlement analysis using Zhang’s method to predict settlement in the study area, BH-10 having the most significant settlement at 29.803 cm. Using the settlement analysis method by Yoshimine, the highest settlement found in BH-10, which is 30.401 cm.

A Multi-Criteria Decision Analysis (MCDA) Approach for Landslide Susceptibility Mapping of a Part of Darjeeling District in North-East Himalaya, India

Landslides are the nation’s hidden disaster, significantly increasing economic loss and social disruption. Unfortunately, limited information is available about the depth and extent of landslides. Therefore, in order to identify landslide-prone zones in advance, a well-planned landslide susceptibility mapping (LSM) approach is needed. The present study evaluates the efficacy of an MCDA-based model (analytical hierarchy process (AHP)) and determines the most accurate approach for detecting landslide-prone zones in one part of Darjeeling, India. LSM is prepared using remote sensing thematic layers such as slope, rainfall earthquake, lineament density, drainage density, geology, geomorphology, aspect, land use and land cover (LULC), and soil. The result obtained is classified into four classes, i.e., very high (11.68%), high (26.18%), moderate (48.87%), and low (13.27%) landslide susceptibility. It is observed that an entire 37.86% of the area is in a high to very high susceptibility zone. The efficiency of the LSM was validated with the help of the receiver operating characteristics (ROC) curve, which demonstrate an accuracy of 96.8%, and the success rate curve showed an accuracy of 81.3%, both of which are very satisfactory results. Thus, the proposed framework will help natural disaster experts to reduce land vulnerability, as well as aid in future development.

Flash flood and landslide susceptibility analysis for a mountainous roadway in Vietnam using spatial modeling

Flash floods and landslides are dangerous natural hazards in hilly areas. They often occur extensively and potentially cause widespread destruction to agriculture, infrastructure, roads, houses, and human beings. This research aimed to analyze the hazard susceptibility on a mountainous roadway using advanced Machine Learning (ML) models. We conducted field surveys to collect data on flash flood and landslide locations in 2017, 2018, and 2019 on a particular roadway in Vietnam, National Highway 6, consisting of 88 flash flood sites and 235 landslide sites. The state-of-art ML models were utilized for the predictive modeling, including AdaBoost-RBF, Bagging-RBF, MultiBoostAB-RBF, and Random Sub-spaceRBF, with Radial Basis Function (RBF) serving as the primary classifier. The AdaBoost-RBF model outperformed all others in predicting landslide and flash flood vulnerability. The resulting map showed that 44.89% or 14,183 ha is in very high susceptibility zones, 15.55% or 4914 ha is in high susceptibility zones, 10.37% or 3.275 ha is in moderate susceptibility zones, 13.69% or 4324 ha is in low susceptibility zones, and 15.50% or 4899 ha is in very low susceptibility zones. A detailed map of the areas where landslides and flash floods are most likely to occur on the roadway might provide local authorities with crucial information for disaster management.

Multi-Hazard Susceptibility Assessment Using the Analytical Hierarchy Process in Coastal Regions of South Aegean Volcanic Arc Islands

Coastal environments are highly recognized for their spectacular morphological features and economic activities, such as agriculture, maritime traffic, fishing, and tourism. In the context of climate change and the evolution of physical processes, the occurrence of intense natural phenomena adjacent to populated coastal areas may result in natural hazards, causing human and/or structural losses. As an outcome, scientific interest in researching and assessing multi-hazard susceptibility techniques has increased rapidly in an effort to better understand spatial patterns that are threatening coastal exposed elements, with or without temporal coincidence. The islands of Milos and Thira (Santorini Island) in Greece are prone to natural hazards due to their unique volcano-tectonic setting, the high number of tourist visits annually, and the unplanned expansion of urban fabric within the boundaries of the low-lying coastal zone. The main goal of this research is to analyze the onshore coastal terrain’s susceptibility to natural hazards, identifying regions that are vulnerable to soil erosion, torrential flooding, landslides and tsunamis. Therefore, the objective of this work is the development of a multi-hazard approach to the South Aegean Volcanic Arc (SAVA) islands, integrating them into a superimposed susceptibility map utilizing Multi-Criteria Decision-Making (MCDM) analysis. The illustrated geospatial workflow introduces a promising multi-hazard tool that can be implemented in low-lying coastal regions globally, regardless of their morphometric and manmade characteristics. Consequently, findings indicated that more than 30% of built-up areas, 20% of the transportation network, and 50% of seaports are within the high and very high susceptible zones, in terms of the Extended Low Elevation Coastal Zone (ELECZ). Coastal managers and decision-makers must develop a strategic plan in order to minimize potential economic and natural losses, private property damage, and tourism infrastructure degradation from potential inundation and erosion occurrences, which are likely to increase in the foreseeable future.

Geospatial approach for Landslide Susceptibility Mapping techniques: A Case Study of Ratnagiri District, Maharashtra, India

Natural and anthropogenic pressure leads to landslides which are catastrophic disaster. Due to climate and land use changes, increase in population landslides has been increasing, specifically, in the mountainous areas. It is threat to lives and their properties. It is necessary to understand geophysical setup of any region causing landslides. It is better to have scientific approach to identify landslide potential risk zones. There are several advanced techniques to study landslides. Here analytical hierarchical process and frequency ratio method incorporated with geospatial technology are utilized for identification of conditioning factors for landslides and landslide susceptibility mapping. Most of the landslide susceptible area are located in the eastern and some middle part of study region (Ratnagiri District). The landslide possibilities are very high along the riverside, high slope region and lineament surrounding. Very high susceptible zone is found to be located mainly in middle part of Chiplun, Devrukh, Rajapur and Dapoli, the southern part of Mandangarh, Khed and eastern part of Ratnagiri. The eastern part of Khed, Chiplun, Devrukh and Lanja falls under the high susceptible zone. Middle part of Guhagar, northern part of Ratnagiri and Rajapur fall under moderate susceptible zone. The southern part of Ratnagiri and western side of Rajapur fall under low susceptible zone. Proper planning and development in terms of disaster mitigation management are needed. No further construction near landslide hazards should be allowed. New laws/regulations should also restrict further construction around the zone of slope failures.

Landslide susceptibility zonation using the analytical hierarchy process (AHP) in the Bafoussam-Dschang region (West Cameroon)

The main goal of this study is to produce a landslide susceptibility map of the Bafoussam-Dschang region (BDR) which is often subjected to landsliding by using Analytical Hierarchical Process (AHP). To achieve this objective, landslide triggering factors such as the lithology, slope, soil, land use, flow density, curvature, elevation and aspect have been combined with field observations. These Parameters were plotted on thematic layers using Geographical Information System (GIS) according to the AHP principles. Factors of weights were attributed based on the frequency of mass movements. The impact of one to another was assessed, allowing weights to be assigned to the different parameters. Subsequently, field observations revealed 100 mass movements including 97 landslides, 01 mudflow, 01 rock fall and 01 collapse within - the BDR. The field data including remote sensing techniques have subdivided the landslides susceptibility map into “Low (3%)”, “Moderate (10%)”, “High (21%)”, and “Very High (66%)” susceptibility zones respectively. The majority of these mass movements fall into the high to very high susceptibility zones equivalent to ∼ 87% of landslides. These results permit to identify the landslide susceptibility zones linked to the steep landscaping West Cameroon Highland (Cameroon Volcanic Line).

Enhancing landslide management with hyper-tuned machine learning and deep learning models: Predicting susceptibility and analyzing sensitivity and uncertainty

IntroductionNatural hazards such as landslides and floods have caused significant damage to properties, natural resources, and human lives. The increased anthropogenic activities in weak geological areas have led to a rise in the frequency of landslides, making landslide management an urgent task to minimize the negative impact. This study aimed to use hyper-tuned machine learning and deep learning algorithms to predict landslide susceptibility model (LSM) and provide sensitivity and uncertainty analysis in Aqabat Al-Sulbat Asir region of Saudi Arabia.MethodsRandom forest (RF) was used as the machine learning model, while deep neural network (DNN) was used as the deep learning model. The models were hyper-tuned using the grid search technique, and the best hypertuned models were used for predicting LSM. The generated models were validated using receiver operating characteristics (ROC), F1 and F2 scores, gini value, and precision and recall curve. The DNN based sensitivity and uncertainty analysis was conducted to analyze the influence and uncertainty of the parameters to the landslide.ResultsResults showed that the RF and DNN models predicted 35.1–41.32 and 15.14–16.2 km2 areas as high and very high landslide susceptibility zones, respectively. The area under the curve (AUC) of ROC curve showed that the LSM by the DNN model achieved 0.96 of AUC, while the LSM by RF model achieved 0.93 of AUC. The sensitivity analysis results showed that rainfall had the highest sensitivity to the landslide, followed by Topographic Wetness Index (TWI), curvature, slope, soil texture, and lineament density.DiscussionRoad density and geology map had the highest uncertainty to the landslide prediction. This study may be helpful to the authorities and stakeholders in proposing management plans for landslides by considering potential areas for landslide and sensitive parameters.

Efficacy of Landslide Susceptibility Maps Prepared Using Different Bivariate Methods: Case Study from Mussoorie Township, Garhwal Himalaya

Abstract Four bivariate methods viz frequency ratio, weight of evidence, Yule’s coefficients and information value were utilized for the preparation of the landslide susceptibility map of the hilly township of Mussoorie. Two scenarios, one with partitioning landslide inventory prepared till 2019 with 70% landslides, and another with all the active landslides till 2019 were used for the preparation of landslide susceptibility maps. In order to understand the efficacy and the reliability of each of the bivariate approach used under both the scenarios, the maps thus obtained were overlaid with the landslides that occurred in the area during excessive rainfall of 2020. It has been noted that the landslide susceptibility maps prepared using four different bivariate methods exhibit more or less similar results, nevertheless of all the four methods used, information value method indicate that more than twice the area (~38%) fall in high and very high landslide susceptible zones in scenario-I. The scenario-II exhibits higher percentage of area falling in high and very high landslide susceptible zones for all the methods as compared to scenario-I, still the Information value indicate the highest percent of area ( ~31%) falling in the high and very high landslide susceptibility zones. The validation of the maps prepared using scenario-I exhibit 58-75% of the 2020 landslides occur in high and very high landslide susceptibility zones, whereas in scenario-II, 57-72% of the 2020 landslides falls in high and very high landslide susceptibility zones. Finally the weight of evidence method and information value method indicate the higher prediction accuracy under both the scenarios

Assessing landslide susceptibility using geospatial technology in Bonga town, southwestern Ethiopia

ABSTRACT This study aims to investigate the main factors that contribute to frequent landslides in Bonga town using GIS and remote sensing. To this end, a frequency ratio model in integration with geospatial technology was applied. The first input parameter (landslide inventory data) was collected and randomly categorized into training and validation. The second input parameters were causative landslide factors such as slope, aspect, curvature, elevation, land use, lithology, stream distance, and distance to the road. The contributing rates of landslide causative factors were calculated using the frequency ratio model based on causative factors and landslide training data. The study found that distance to the road, distance to the stream, elevation, and aspect were the most important factors for the occurrence of landslides in Bonga town. Land use and slope were the moderate contributing factors to landslide occurrence, while curvature and lithology were the least important contributing factors. The landslide susceptibility index map shows that the largest portion of the study area falls under the moderate (42.3%,) and high (34.3%) susceptibility zones. Hence, different intervention strategies for saving lives and assets should be developed swiftly based on the vulnerability level of each site.

Mapping of Flood-Prone Areas Utilizing GIS Techniques and Remote Sensing: A Case Study of Duhok, Kurdistan Region of Iraq

One of the most common types of natural disaster, floods can happen anywhere on Earth, except in the polar regions. The severity of the damage caused by flooding can be reduced by putting proper management and protocols into place. Using remote sensing and a geospatial methodology, this study attempts to identify flood-vulnerable areas of the central district of Duhok, Iraq. The analytical hierarchy process (AHP) technique was used to give relative weights to 12 contributing parameters, including elevation, slope, distance from the river, rainfall, land use land cover, soil, lithology, topographic roughness index, topographic wetness index, aspect, the sediment transport index, and the stream power index in order to calculate the Flood Hazard Index (FHI). The relative importance of each criterion was revealed by a sensitivity analysis of the parameter values. This research developed a final flood susceptibility map and identified high-susceptible zones. This was classified anywhere from very low to very high classifications for its potential flood hazard. The generated map indicates that 44.72 km2 of the total land area of the study area in Duhok city has a very high susceptibility to flooding, and that these areas require significant attention from government authorities in order to reduce flood vulnerability.

Framework for rainfall-triggered landslide-prone critical infrastructure zonation

Rainfall-induced landslides cause frequent disruptions to critical infrastructure in mountainous countries. Climate change is altering rainfall patterns and localizing extreme rainfall events, increasing the occurrence of landslides. For planning climate-resilient critical infrastructure in landslide-prone regions, it is urgent to understand the changing landslide susceptibility in relation to changing rainfall extremes and spatially overlay them with critical infrastructure to determine risk zones. As such, areas requiring financial reinforcements can be prioritized. In this paper, we develop a framework linking changing rainfall extremes to landslide susceptibility and intensity of critical infrastructure — exemplified on a national scale using Nepal as a case study. First, we define a set of 21 different unique rainfall indices that describe extreme and localized rainfall. Second, we prepare a new annual (2016–2020) inventory of 107,900 landslides in Nepal mapped on PlanetScope satellite imagery. Next, we prepare a landslide susceptibility map by training a random forest model using the collected extreme rainfall indices and landslide locations in combination with spatial data on topography. Fourth, we construct a gridded critical infrastructure spatial density map that quantifies the intensity of infrastructure (i.e., transportation, energy, telecommunication, waste, water, health, and education) at each grid location using OpenStreetMap. The landslide susceptibility map classified Nepal's topography into low (36 %), medium (33 %), and (32 %) high rainfall-triggered landslide susceptibility zones. The landslide susceptibility map had an average area under the receiver characteristic curve value of 0.94. Finally, we overlay the landslide susceptibility map with the critical infrastructure intensity to identify areas needing financial reinforcement. Our framework reasonably mapped critical infrastructure hotspots in Nepal prone to landslides on a 1 km grid. The hotspots are mainly concentrated along major national highways and in provinces 4, 3, and 1, highlighting the need for improved land management practices. These hotspots need spatial prioritization regarding climate-resilient critical infrastructure financing and slope conservation policies. The research data, output maps, and code are publicly released via an ArcGIS WebApp and GitHub repository. The framework is scalable and can be used for developing infrastructure financing strategies for landslide mountain regions and countries.