Susceptibility Zonation Research Articles

Investigating the dynamic nature of landslide susceptibility in the Indian Himalayan region.

Landslide susceptibility zonation (LSZ) mapping is used to delineate areas prone to landslides and is critical for effective landslide hazard management. The existing methodologies for generating such maps tend to neglect the influence of dynamic environmental variables on landslide occurrences, which may lead to obsolete and erroneous estimates of landslide susceptibility (LS) for a concerned area. Although recent studies have started to report the effects of Land Use/ Land Cover (LULC) variation on LSZ mapping, variations in other dynamic variables like rainfall, soil moisture, and evapotranspiration apart from LULC may also influence slope stability in mountainous regions. The present study investigates the impact of variations in these four variables on the LS distribution, of a selected Indian Himalayan region between 2017 and 2021. Random Forest (RF) susceptibility models are utilized for evaluating the LS for the selected years and geospatial technologies are employed for LS change detection. The results indicate up to 19% variations in the spatial extent for some of the zones of the generated LSZ maps. The research findings of this study are crucial since they reveal the impact of dynamic behavior on LS, which has not been previously documented in the literature.

Landslide susceptibility zonation using the analytical hierarchy process (AHP) in the Great Xi’an Region, China

This study aims to delineate landslide susceptibility maps using the Analytical Hierarchy Process (AHP) method for the Great Xi’an Region, China, which is a key planning project for urban construction in Shaanxi Province, China from 2021 to 2035. Multiple data as elevation, slope, aspect, curvature, river density, soil, lithology, and land use have been considered for delineating the landslide susceptibility maps. Spatially thematic layers and distributed maps of all the aforementioned parameters were created in a GIS environment. Determine the relative importance of these thematic layers in the occurrence of landslides in the study area concerning historical landslide data to assign appropriate weights. Landslide sensitivity maps were generated by a weighted combination in a GIS environment after being analyzed by the AHP method. The sensitivity maps were categorized as “very high (11.06%), high (19.41%), moderate (23.03%), low (28.70%), and very low (17.80%)”. Overlay analysis of the test data with the LSM showed that the moderate to very high landslide susceptibility zones were able to contain 82.58% of the historic landslides. The results of the study help determine the landslide-prone areas in the area and provide a reference for subsequent construction. In addition, the analysis of landslide susceptibility in the area contributes to the study of landslides in similar loess sites.

Landslide Susceptibility and Risk Mapping in the Tectonic Ensemble Comprising of Eastern Himalayan Zone, Northeast India and Bhutan using Logistic Regression and Random Forest Techniques

Abstract The mountainous terrain of the East-Northeast tectonic ensemble covers an approximate area of 3,11,420 km2 comprising the Eastern Himalayan zone, Northeast India and Bhutan is prone to mass movements. The increasing trend of landslide occurrences in the last few decades sets loud alarm bells for mapping the landslide hotspot zones in terms of Landslide Susceptibility and Risk. Initially, a landslide inventory of around 9751 landslides has been prepared, of which 6826 (70%) has been randomly picked as training dataset and the remaining 2925 (30%) as the testing dataset. Thereafter, Random Forest (RF) and binary Logistic Regression (LR) based Landslide Susceptibility Zonation (LSZ) have been prepared through twelve predictor layers viz. Slope Angle, Slope Aspect, Slope Curvature, Distance to Drainage, Distance to Lineament, Landform, Surface Geology, Distance to Road, Normalized Difference Vegetation Index (NDVI), Landuse/ Landcover (LULC), Rainfall and Epicentre Proximity. Both the Landslide Susceptibility Index (LSI) maps are divided into five classes, viz. ‘low’, ‘moderate’, ‘high’, ‘very high’ and ’severe’, which are then validated statistically by drawing a comparison with the 30% testing chronological landslide inventory database. The statistical index based accuracy assessment in terms of Area Under the Curve (AUC) exhibits an LR model AUC of 0.776 and RF model AUC of 0.820. However, it has been observed that the RF model strongly correlates with the testing inventory dataset exhibiting that around 46% of the landslide-prone terrain is classified in the ‘high to severe’ zones with 41% inventory landslides occurring in these zones. Integrating the Random Forest (RF) based LSI thematic layer with the socio-economic vulnerability layers like the Number of Households and Population Density have demarcated around 20.78% of the region under ‘very high to severe’ socio-economic risk as convoluted by landslide susceptibility in the terrain. The present findings are expected to be useful in urban development and town planning with appropriate slope management and land-use planning.

Sensitivity analysis of multipactor susceptibility zone to variations in secondary electron yield values

We present a sensitivity analysis of the multipactor susceptibility zones to variations in the secondary electron yield (SEY) of materials, specifically focusing on the first and second unity crossover points of SEYs. In conducting this research, we leveraged our semi-analytic approach, which allows for the rapid prediction of the full multipactor zones with enhanced accuracy. Using this approach, we unveil several unique features of multipactor susceptibility zones, including the infinite extension of different-order multipactor zones and the overlap between them. Building upon this prediction capability, our results complement previous findings on the same topic and reveal that the multipactor zones depend not only on the first crossover point but also on the second crossover point of SEY, which this latter predominantly impacts multipactor susceptibility zones for low SEY materials. To validate our predictions, we present two distinct sets of multipactor experiments, providing empirical support for our results.

Seismic Landslide Susceptibility Assessment Using Newmark Displacement Based on a Dual-Channel Convolutional Neural Network

Landslide susceptibility assessment (LSA) is an essential tool for landslide hazard warning. The selection of earthquake-related factors is pivotal for seismic LSA. In this study, Newmark displacement (Dn) is employed as the earthquake-related factor, providing a detailed representation of seismic characteristics. On the algorithmic side, a dual-channel convolutional neural network (CNN) model is built, and the last classification layer is replaced with two machine learning (ML) models to facilitate the extraction of deeper features related to landslide development. This research focuses on Beichuan County in Sichuan Province, China. Fifteen landslide predisposing factors, including hydrological, geomorphic, geological, vegetation cover, anthropogenic, and earthquake-related features, were extensively collected. The results demonstrate some specific issues. Dn outperforms conventional earthquake-related factors such as peak ground acceleration (PGA) and Arias intensity (Ia) in capturing seismic influence on landslide development. Under the same conditions, the OA improved by 5.55% and AUC improved by 0.055 compared to the PGA; the OA improved by 3.2% and AUC improved by 0.0327 compared to the Ia. The improved CNN outperforms ML models. Under the same conditions, the OA improved by 4.69% and AUC improved by 0.0467 compared to RF; the OA improved by 4.47% and AUC improved by 0.0447 compared to SVM. Additionally, historical landslides validate the reasonableness of the landslide susceptibility maps. The proposed method exhibits a high rate of overlap with the historical landslide inventory. The proportion of historical landslides in the very high and high susceptibility zones exceeds 87%. The method not only enhances accuracy but also produces a more fine-grained susceptibility map, providing a reliable basis for early warning of seismic landslides.

Geological Disaster Susceptibility Evaluation Using a Random Forest Empowerment Information Quantity Model

Geological hazard susceptibility assessment (GSCA) is a crucial tool widely utilized by scholars worldwide for predicting the likelihood of geological disasters. The traditional information quantity model in geological disaster susceptibility evaluation, which superimposes the information quantity of each evaluation factor without considering their weights, often negatively impacts susceptibility zoning results. This paper introduces a method employing random forest (RF) empowerment information quantity to address this issue. The method involves calculating objective weights based on a parameter-optimized random forest model, assigning these weights to each evaluation factor, and then conducting a weighted superimposition of the information. Utilizing the natural discontinuity method, the resulting comprehensive information volume map was segmented. The proposed method was applied in Kang County, Gansu Province, and its performance was compared with that of traditional methods in terms of geological disaster susceptibility zoning maps, zoning of statistical disaster point density, and receiver operating characteristic (ROC) curve accuracy. The experimental findings indicate the superior accuracy and reliability of the proposed method over the traditional approach.

Identifying susceptible groundwater contamination zones in western Odisha of India using hydro-geochemical and geospatial approaches

Nuapada is one of the most drought-affected and fluoride-contaminated districts in Odisha, India. The presence of various dissolved substances, evapotranspiration, and lowering water table during pre- and post-monsoon (PRM and POM) seasons are responsible for declining groundwater (GW) quality over the Nuapada region. To comprehend the contaminated GW zones over the Nuapada and Komna blocks of the northern Nuapada district during the seasons, integration of hydrogeochemistry and statistical approaches using GW sample data on a geospatial platform have been done. The analysis exhibits that the major source of groundwater contamination is mostly geogenic with little anthropogenic impact. The cumulative impact of fluoride (F−), iron (Fe2+), and nitrate (NO₃−) contents are noticed in great-depth zones of the water table in the north and south parts of Nuapada and Komna blocks, respectively. The dominant hydro facies, such as Na-Cl (41.77%) and Ca-Cl (25.31%) types exist over both blocks during PRM and POM seasons, respectively. Demarcation of contaminant and susceptible zones over the study area using geospatial analysis and groundwater quality indices (GWQI) were done. About 3% of the total area, in the north and middle parts of the Nuapada and Komna blocks, falls under contamination zones and is unfit for drinking purposes, and about 35% of the region is susceptible to future contamination. The outcome of the result analysis will enhance the scope for researchers, policymakers, and water managers to regulate emerging health, agricultural, and industrial issues in the stressed aquifer system in India and the world.

Exploring machine learning and statistical approach techniques for landslide susceptibility mapping in Siwalik Himalayan Region using geospatial technology.

Landslides are a natural threat that poses a severe risk to human life and the environment. In the Kumaon mountains region in Uttarakhand (India), Nainital is among the most vulnerable areas prone to landslides inflicting harm to livelihood and civilization due to frequent landslides. Developing a landslide susceptibility map (LSM) in this Nainital area will help alleviate the probability of landslide occurrence. GIS and statistical-based approaches like the certainty factor (CF), information value (IV), frequency ratio (FR) and logistic regression (LR) are used for the assessment of LSM. The landslide inventories were prepared using topography, satellite imagery, lithology, slope, aspect, curvature, soil, land use and land cover, geomorphology, drainage density and lineament density to construct the geodatabase of the elements affecting landslides. Furthermore, the receiver operating characteristic (ROC) curve was used to check the accuracy of the predicting model. The results for the area under the curves (AUCs) were 87.8% for logistic regression, 87.6% for certainty factor, 87.4% for information value and 84.8% for frequency ratio, which indicates satisfactory accuracy in landslide susceptibility mapping. The present study perfectly combines GIS and statistical approaches for mapping landslide susceptibility zonation. Regional land use planners and natural disaster management will benefit from the proposed framework for landslide susceptibility maps.

Influence of buffer distance on environmental geological hazard susceptibility assessment.

Previous researches seldom studied the selection of buffer distance between geological hazards (positive samples) and non-geological hazards (negative samples), and its reasonable selection plays a very important role in improving the accuracy of susceptibility zoning, protecting the environment and reducing the cost of hazard management. Based on GIS technology and random forest (RF) and frequency-ratio random forest (FR-RF) models, this study innovatively explored the influence of randomly selected non-geological hazard samples outside different buffer distances on the susceptibility evaluation results, with buffer distances of 100 m, 500 m, 1000 m and 2000 m in sequence. The results show that through the confusion matrix and ROC curve test, the accuracy of the model increases first and then decreases with the increase of buffer distance. Both RF and FR-RF models have the highest accuracy when the buffer distance is 1000 m, and the accuracy of the RF model is generally higher than that of the FR-RF model under the same buffer distance. Similar attribute values of positive samples and randomly selected negative samples or "extreme" attribute values of negative samples are the main reasons for the differences in evaluation results of different buffer distances. According to the weight analysis of causative factors, the distance from road, the distance from river and the normalized vegetation index (NDVI) are the main factors affecting the occurrence of hazards. The high and very high susceptibility areas in the study area are mainly distributed on both sides of roads and water systems, which are the key areas for hazard prevention and reduction. The HMC of RF-1000m decreased by 3.55% on average compared with other models. The results of this study improve the accuracy of geological hazard susceptibility assessment, maintain the safety of ecological environment, and provide a scientific basis for the selection of buffer distance index in local and surrounding areas in the future.

Flood susceptibility assessment of the Agartala Urban Watershed, India, using Machine Learning Algorithm.

Frequent floods are a severe threat to the well-being of people the world over. This is particularly severe in developing countries like India where tropical monsoon climate prevails. Recently, flood hazard susceptibility mapping has become a popular tool to mitigate the effects of this threat. Therefore, the present study utilized four distinctive Machine Learning algorithms i.e., K-Nearest Neighbor, Decision Tree, Naive Bayes, and Random Forest to estimate flood susceptibility zones in the Agartala Urban Watershed of Tripura, India. The latter experiences debilitating floods during the monsoon season. A multicollinearity test was conducted to examine the collinearity of the chosen flood conditioning factors, and it was seen that none of the factors were compromised by multicollinearity. Results showed that around three-fourths of the AUW area was classified as moderate to very high flood-prone zones, while over 20 percent was between low and very low flood-prone zones. The models applied performed well with ROC-AUC scores greater than 70 percent and MAE, MSE, and RMSE scores less than 30 percent. DT and RF algorithms were suggested for places with similar physical characteristics based on their outstanding performance and the training datasets. The study provides valuable insights to policymakers, administrative authorities, and local stakeholders to cope with floods and enhance flood prevention measures as a climate change adaptation strategy in the AUW.

Flood susceptibility assessment using machine learning approach in the Mohana-Khutiya River of Nepal

Nepal, known for its challenging topography and fragile geology is confronted with the constant threat of floods leading to substantial socio-economic losses annually. However, the country's efforts in planning and managing flood risks remain insufficient, especially in the vulnerable Mohana-Khutiya River. Therefore, this study focused on the Mohana-Khutiya River and utilizes the Maximum Entropy (MaxEnt) model to comprehensively map flood susceptibility and fill crucial gaps in flood risk assessments. This study employed a combination of 10 geospatial environmental layers and field-based past flood inventory to implement the MaxEnt machine learning model for flood susceptibility modeling. The available past flood data were divided into two sets, with 75% allocated for model construction and the remaining 25% for model validation. This study demonstrated that the proximity of the river had a significant impact (33.1%) on the occurrence of the flood. Surprisingly, the amount of annual precipitation throughout the year exhibited no detectable contribution to the flood event in the study site. About 4.9% area came under the high flood susceptible zone followed by 12.75 % in the moderate zone and 82.34% in the low-risk zone. The model exhibited excellent performance with an Area Under Curve (AUC) value of 0.935 and a low standard deviation of 0.018, indicating accurate predictions and consistent precision. These results highlight the model's reliability and its significance for developing disaster management policy by local government in the study site. Future research should refine the MaxEnt model by including more variables, validating against observed flood events, and exploring integration with other flood modeling approaches.

MAPPING AND PREDICTION OF LAND SUBSIDENCE

The exact location and timing of a land subsidence-related calamity cannot be foreseen with any degree of confidence. This is true for both progressive subsidence caused by fluid (groundwater, oil or gas) withdrawal and abrupt subsidence on surface caused by underground mine collapse. The best way to deal with these dangers is to mitigate them. In an ideal world, all regions prone to such risks would be well-known, and measures would be made to either prevent causing the problem if it is human-caused, or to avoid inhabiting such areas if they are prone to natural subsidence. Extensometers, levelling, hydrogeology modelling, and GPS are common methods for monitoring subsidence, although they require precise field data and are time-consuming. PSI (Persistent Scatterer Interferometry) is a strong radar-based remote sensing technique for measuring and monitoring surface displacements over time. The techniques of PSI using microwave sensors has made monitoring of earth deformation precisely more reliable and generates large number of Persistent Scatterer Candidates (PSC’s) or sampling points. Although, all the above techniques have their own merits and de-merits but mapping and prediction of susceptible subsidence prone zones is an issue. This paper deals with the application of the Exploratory Data Analysis (EDA), a powerful environment in Data Science provided a set of tools that made it easier to interpret PSI processed Big Data and also improved the capability of validation, management, visualization, and presentation of results with greater reliability.

Landslide susceptibility zonation using the analytical hierarchy process. A case study of Guantanamo Province

Landslide susceptibility zonation using the analytical hierarchy process. A case study of Guantanamo Province

Terrain Factors Induced Slope Instability in CNG 37 – Ghat section, Panthalur, The Nilgiris

— Landslides are short-lived phenomena but can cause extraordinary changes in the landscape, destroying life and property. Mitigation and management of natural disasters are important issues. Recognising this, the United Nations in the year 2004 made gout “Learning from today’s disaster for tomorrow’s hazards”(UNISDR website). The Nadugani Hill is situated in SE of Panthalur taluk, Nilgiris District. The ghat section is 6.5 km in length. The region has moderate climatic conditions. The average rainfall in this ghat section is 3500mm. In this ghat section, two major activities, like tea plantation and heavy truck movement, lead to slope instability during heavy monsoon season. The study mainly aimed to use terrain factors and identify land susceptibility zonation. In this context, a detailed investigation was carried out using geomorphology, parent rock weathering, structural mapping, runoff and anthropogenic activities. The ghat section is covered by a thick Reserve Forest, a tea plantation and a smaller area enclosed by settlement. Hornblende biotitic gneiss and patches of charnockite in some places cover the study area. The gneissic rocks are highly fissile in nature. NE - SW fault controls the drainage systems, contributing to landslides. The structural data delineated from IRS-RS2, L-IV data and the lineaments are oriented in NE - SW. Morphology is one of the prime factors that cause which is landslides. Satellite imageries identify structural and fracture valleys in this study area. The thematic maps were converted into a digital format using ARC Map (10.2v) software. GIS-based Decision Support System provides advanced models of a system to identify land susceptibility zonation. Based on the analysis, the study area has been categorized into low, moderate, high and very high classes.

Flash flood susceptibility mapping of north-east depression of Bangladesh using different GIS based bivariate statistical models

Flash flood causes severe damage to the environment and human life across the world, no exception is Bangladesh. Severe flash floods affect the northeastern portion of Bangladesh in the early monsoon and pose a serious threat to every aspect of socioeconomic development and environmental sustainability. To manage the threat and reduce flood loss, the map of flash flood susceptible zones plays a key role. Thus, the aim of this research is to map the flash flood-susceptible areas of the northeastern haor areas of Bangladesh utilizing GIS-based bivariate statistical models. The models utilized are frequency ratio (FR), weights of evidence (WoE), certainty factor (CF), Shanon’s entropy (SE) and information value (IV). Among the 250 identified flash flood locations, 80 % data was used for training purposes and 20 % data for testing purposes. Eleven selected conditioning factors of flash flood include elevation, slope, aspect, curvature, TWI, TRI, SPI, distance to stream, stream density, rainfall and physiography. The calculated weights are assigned to the conditioning factors using ArcGIS environment to prepare the final flash flood maps. Results of AUC of ROC indicate WoE (success rate = 0.833 and prediction rate = 0.925) is the best model for flash flood susceptibility mapping followed by FR (success rate = 0.828 and prediction rate = 0.928) and SE (success rate = 0.827 and prediction rate = 0.923). According to the models, topographic (flat area) and hydrologic factors significantly control flash flood occurrence in the study area. The prepared flash flood susceptibility maps will be helpful for disaster managers and haor master planners of the study area.

Performing of spatial hydro-geomorphology analysis to detect the potential flood susceptibility in kr. keureuto watershed using ahp-mcdm approach

Flood is one of the most destructive natural disasters that causes loss of lives, properties, and significant resources, repeatedly occurring throughout Aceh, particularly in North Aceh. Given the more frequent floods and their damaging effects, it appears urgent and crucial to enhance the identification and mapping of flood susceptibility. One method that has been developed is the flood susceptibility mapping method. However, this method cannot address the challenges of flood susceptibility zone delineation due to the uncertainty of evaluation processes and the complex nonlinear relationships between indicators and risk levels, given that flood mapping is a multi-criteria spatial decision problem requiring hydrologic and geographic information. The objectives of this research are to assess flood susceptibility using the AHP-MCDM and to integrate using GIS to produce flood susceptibility map in Kr. Keureuto Watershed. Seven criteria from hydrologic and geographic information of the watershed were collected and analyzed using ArcGIS, and the weight of criteria using AHP from 25 expert responders. The results shows that there are four distinct levels of flood susceptibility: very high, high, medium, and low vulnerable. Significantly, the downstream Kr. Keureuto watershed emerges as highly susceptible to flooding, compared to the upstream zone.

COMPARATIVE STUDY OF STAPHYLOCOCCUS AUREUS ISOLATES IN PUS AND NASAL SWABS AMONG LAHORE PATIENTS

Staphylococcus aureus predominately found in nasal and pus samples is one of the major causes of skin infections and invasive diseases like endocarditis, pneumonia, and osteomyelitis. The current study was performed over the 3 month period, from April 2022 to June 2022. A total of 368 participants’ nasal and pus swab specimens were included in this study and were collected from the different hospitals in Lahore. The objective of the study was to discern the relative prevalence of Staphylococcus aureus isolates in Pus and Nasal Swabs among Patients in Lahore. All the collected samples were cultured in sterile conditions on Blood agar, Chocolate agar, MacConkey agar, and Mannitol salt agar according to the standard laboratory guidelines. S. aureus culture plates were identified based on the colonial morphology after 48hrs. Furthermore, isolated bacteria were characterized based on Gram stain, and differential biochemical analyses like the Coagulase test, Oxidase test, Catalase test, Triple sugar iron, Urease test, Indole test, Motility agar test that was performed to reconfirm S. aureus presence in the given pus and nasal specimens. The modified Kirby Bauer disc diffusion method was employed for antimicrobial susceptibility testing and Zone sizes were interpreted according to the Clinical and Laboratory Standard Institute guidelines (2020). From a total of 368 bacterial samples, 283 were pus swabs and 85 were nasal swabs, whereas the most frequently isolated organism was gram-positive Staphylococcus aureus which accounts for 90% (330) of the total samples. Our results showed a 90 % MRSA prevalence rate in Lahore and in our study oxacillin represents high resistance against MRSA in nasal samples, whereas, clindamycin represents high resistance in pus samples respectively. The reason for this increased prevalence of MRSA is due to the irrational use of antibiotics without prior cultural reports, the absence of antimicrobial situation programs in hospitals, and the failure to look at the infection management practices which may be some of the highlighted reasons for this drawback in the hospital and clinical setup.

Landslide susceptibility assessment along the National Highway-244 from Batote to Doda, J & K, India: A study based on the Frequency Ratio Method

The National Highway-244 is highly susceptible to landslide occurrences, frequently resulting in road blockades and causing significant hardships for the local population. These landslides pose a threat to human lives, property and the environment, leading to substantial losses. In this study, an attempt has been made to carry out landslide susceptibility assessment through frequency ratio method along the National Highway-244 utilizing GIS and statistical computations. It considers eight parameters, which include topographical (slope, slope aspect, slope curvature, hill shade, and relief), anthropogenic (distance to road and distance to river) and geological parameters that mostly influence the occurrence of landslides in the area under investigation. The present study focuses only along National Highway-244 and data has been gathered from field visits and secondary sources. The results of the study inferred that the area under investigation falls into different susceptibility zones, namely very high, high, moderate, low, and very low, covering approximately 15%, 31%, 27%, 19%, and 8%, respectively of the total area. This study reveals that a considerable proportion, around 73%, of the study area falls within very high to moderate susceptibility zones. The conclusions drawn from this study hold significant implications for stakeholders and also provide valuable insights for future planning and infrastructure development, enabling them to make informed decisions. By considering the susceptibility zones identified in this study, stakeholders can implement appropriate measures to mitigate the impact of landslides, ensuring the safety and stability of the region.

Local-scale flash flood susceptibility assessment in northeastern Bangladesh using machine learning algorithms

The recurring flash floods in northeastern Bangladesh have been causing significant damage to the lives and livelihoods of local people every year, underscoring the necessity of effective flood management and mitigation efforts. Identifying flood hazard areas is the very first step in achieving such ends. While machine learning algorithms have been widely used in flood susceptibility studies, there is a significant dearth of research addressing their application for local-scale assessment of flood susceptibility in Bangladesh. Besides, in most of the flood susceptibility studies, only statistical metrics are used to evaluate the prediction capability of machine learning algorithms, overlooking their ability to account for spatial consistency. Therefore, this study attempts to evaluate the performance of Random Forest (RF) and Support Vector Machine (SVM) algorithms in flash flood susceptibility assessment at local scale using statistical and spatial metrics. RF model performed better than SVM with area under the curve (0.964), accuracy (92.1 %) and kappa (0.894). Five susceptibility zones were identified using a natural breaks method: very high, high, moderate, low and very low. The overall spatial agreement between the susceptibility maps of RF and SVM was 73.3 %. For very low susceptible classes, the spatial agreement was highest for RF-SVM (97.15 %) and SVM-RF (81.54 %). The findings of this study can be useful for a more accurate assessment of flash flood susceptibility in similar local settings. Moreover, the prepared susceptibility map can be helpful for taking sustainable measures to mitigate the devastating effects of flood hazard in northeastern Bangladesh.

Development of landslide susceptibility maps of Tripura, India using GIS and analytical hierarchy process (AHP).

Landslides are one of the most extensive and destructive geological hazards on the globe. Tripura, a northeastern hilly state of India experiences landslides almost every year during monsoon season causing casualties and huge economic losses. Hence, it is required to assess the landslide susceptibility of the area that would support short- and long-term planning and mitigation. The analytic hierarchy process (AHP) integrated with geospatial technology has been adopted for landslide susceptibility mapping in the state. Eight influencing factors such as slope, lithology, drainage density, rainfall, land use land cover, distance from rivers and roads, and soil type were selected to map the landslide susceptibility. Landslide susceptibility index (LSI) was found to vary from 6.205 during monsoon to 1.427 during post-monsoon season. The LSI values were classified into very high, high, moderate, low, and very low susceptibility. Landslide susceptibility maps for three different seasons, namely, pre-monsoon, monsoon, and post-monsoon, were prepared. The study showed that most of the areas of the state come under very low to moderate landslide susceptibility zones. Around 73.2% area of the state is found to be under low landslide-susceptible zones during the pre-monsoon season, around 62% area is prone to landslides with moderate susceptibility during the monsoon season, and 68.5% area comes under landslides with low susceptibility zones during the post-monsoon season. The resultsof this study may be referred to the engineers and planners for the assessment, control, and mitigation of landslides and the development of basic infrastructure in the state.