Natural Hazards Research最新文献

{"title":"Drought monitoring using the Climate Hazards InfraRed Precipitation with Stations (CHIRPS) in Ethiopia","authors":"Birhan Getachew Tikuye ,&nbsp;Ram L. Ray ,&nbsp;Busnur Manjunatha ,&nbsp;Gebrekidan Worku Tefera ,&nbsp;Sanjita Gurau","doi":"10.1016/j.nhres.2024.12.002","DOIUrl":"10.1016/j.nhres.2024.12.002","url":null,"abstract":"<div><div>Ethiopia has experienced numerous natural disasters, with 110 documented events that include floods, disease outbreaks, droughts, landslides, pest infestations, volcanic eruptions, earthquakes, mass movements, and wildfires over the past 58 years. Among these, drought occurrences are characterized by various factors such as duration, inter-arrival time, peak intensity, frequency, and severity. This study aims to monitor drought patterns in Ethiopia using the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS-v2) satellite rainfall product. A 3 and 12-month timescale Standardized Precipitation Index (SPI) based on a gamma distribution was calculated to evaluate inter-arrival time, peak intensity, frequency, severity, and trends from 1981 to 2021. The performance of CHIRPS data was evaluated compared to gauging stations using the coefficient of determination (R²), root means square error (RMSE), and mean absolute error (MAE) and showed a good agreement was reached. Results reveal an increasing trend in drought across all seasons, including winter, spring, summer, and autumn. However, statistical analysis via the Mann-Kendall trend test indicates that these upward trends are not statistically significant, with computed p-values (0.335, 0.419, 0.384, and 0.207) exceeding the significance level of α ​= ​0.05. Temporal variations in drought indices reveal that certain years, such as 1984, 2010, and 2016, were among the driest periods in Ethiopia, both in terms of annual and seasonal drought severity. In contrast, 1998, 2007, and 2020 were identified as the wettest years and seasons in the country. The SPI-12 monthly index drought characterization shows an average of 4 drought events per year, with a maximum drought duration of 45 months, a maximum magnitude of 80, a frequency of 18%, and a severity of 2.6. The findings highlight the significance of advanced satellite data in accurately characterizing drought conditions, vital for strengthening the country's capacity to adapt to climate variability. By incorporating these insights into national planning and resource management frameworks, Ethiopia can enhance its ability to protect ecosystems, ensure food security, and maintain overall socio-economic stability in the face of the growing threat of drought.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 2","pages":"Pages 348-362"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Climate change-induced shifts in landslide susceptibility in São Sebastião (southeastern Brazil)","authors":"Enner Alcântara ,&nbsp;Cheila Flávia Baião ,&nbsp;Yasmim Carvalho Guimarães ,&nbsp;José Antonio Marengo ,&nbsp;José Roberto Mantovani","doi":"10.1016/j.nhres.2024.11.005","DOIUrl":"10.1016/j.nhres.2024.11.005","url":null,"abstract":"<div><div>Landslides are a pressing natural hazard, particularly in regions prone to extreme weather events, and their frequency is expected to rise due to climate change. This paper investigates landslide susceptibility in São Sebastião, a coastal region in southeastern Brazil, under various climate change scenarios. The study fills a critical gap in understanding how future precipitation changes driven by climate models could affect the area's susceptibility to landslides. Current assessments often overlook the combined effects of environmental variables and land-use dynamics under future climate conditions. To bridge this gap, this research integrates environmental variables, including Soil Moisture Index (SMI), slope degree, saturation, relief dissection, geomorphology, geology, and topographic position index (TPI), with land use and land cover (LULC) data. Scenarios from the Intergovernmental Panel on Climate Change (IPCC) for RCP2.6, RCP4.5, RCP6.0, and RCP8.5 CMIP5 (Climate Models Intercomparison Programme Version 5) models were applied to model the impact of changing precipitation patterns on landslide susceptibility. Using geospatial data and a weighted sum model, susceptibility maps were developed for each climate scenario and validated with a landslide inventory and receiver operating characteristic (ROC) analysis. The findings indicate a notable shift in landslide risk, with scenarios RCP6.0 and RCP8.5 showing significant increases in moderately susceptible areas due to higher precipitation intensities. Frequency Ratio (FR) analysis revealed varying levels of landslide susceptibility across scenarios, with RCP2.6 showing lower probabilities for moderate landslides (FR: 0.007946) compared to higher ratings for RCP4.5, RCP6.0, and RCP8.5 (FR: 1.663156 for high landslides). Slope and TPI emerged as the most influential variables, while land-use types, particularly urban areas and deforestation zones, showed heightened vulnerability in future scenarios.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 2","pages":"Pages 321-334"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and validation of a novel tool: The family resilience assessment scale for flood-affected families (FRAS-FAF)","authors":"Haleema Sadia ,&nbsp;Boonjai Srisatidnarakul ,&nbsp;Jen-Jiuan Liaw","doi":"10.1016/j.nhres.2024.10.002","DOIUrl":"10.1016/j.nhres.2024.10.002","url":null,"abstract":"<div><div>Family resilience is critical in coping with disasters such as floods, yet there is a lack of tools specifically designed to assess family resilience in flood-prone regions. This study aimed to develop and validate the Family Resilience Assessment Scale for Flood-Affected Families (FRAS-FAF), a culturally sensitive tool tailored to families impacted by recurrent flooding in Pakistan. A mixed-methods study design was employed in two phases. In Phase I, qualitative data were collected through in-depth interviews with heads of flood-affected families in the Nowshera district of Pakistan, revealing key themes related to family resilience. These insights informed the creation of 54 initial items for the FRAS-FAF. In Phase II, the psychometric properties of the tool were assessed using factor analysis with 400 participants, allocated for Exploratory Factor Analysis (EFA) 200 and the remaining 200 for Confirmatory Factor Analysis (CFA). The final version of the scale retained 52 items across five factors: Family Culture, Family Structure, Family Spirituality, Family Resources, and Environment. EFA and CFA confirmed a strong five-factor structure, with model fit indices demonstrating adequate construct validity (χ2 ​= ​1551/df ​= ​936, CFI ​= ​.91, TLI ​= ​.89, SRMR ​= ​.075, RMSEA ​= ​.057). Internal consistency was high for all factors, with Cronbach's alpha values ranging from .85 to .96. FRAS-FAF is a reliable and valid tool for assessing family resilience in flood-affected families. It provides a framework for practitioners and policymakers to identify areas of strength and vulnerability, informing the development of targeted interventions aimed at enhancing family resilience in flood-prone regions. The tool's culturally sensitive design ensures its relevance for flood-affected families in Pakistan and offers the potential for adaptation in other disaster-prone areas.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 2","pages":"Pages 229-246"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Specific impacts of climate change on the hydrological patterns and land use dynamics in the Arghandab River Basin, Kandahar, Afghanistan","authors":"Bashir Ahmad Karimi ,&nbsp;Mohammad Aslam Haziq ,&nbsp;Athiqullah Hayat","doi":"10.1016/j.nhres.2024.12.007","DOIUrl":"10.1016/j.nhres.2024.12.007","url":null,"abstract":"<div><div>Changes in rainfall patterns and warming temperatures brought on by greenhouse gas emissions have significant impact on the global hydrological cycle. The deteriorating physical properties of river basins, melting glaciers, drought conditions, food shortages, extreme weather events, and alterations in groundwater recharge are all consequences of these changes. Climate change and global warming have placed a greater strain on the climatic factors affecting the Arghandab River Basin. Sustainable development in the basin's depends on assessment of the long-term outcomes of climate change on several characteristics of the basin. The primary goal is to understand the average general hydrology of the basin. Furthermore, based on that, assess the impacts of climate change on temperature and precipitation Next, evaluate the work done to simulate and model surface runoff and sediment transport in order to understand the impacts of climate change on these processes, as well as the temporal variations and alterations in land use and land cover due to climate change. Data were collected from regional and international data sets. The soil and water assessment tool (Swat, 2012) model was applied to assess the average general hydrology and sediment transport of the basin, and statistics were used to estimate the amount of change. The research found that this basin is highly sensitive to climate change. The results indicate that from 1981 to 2021, the historic temperature increased by 1.41 ​°C, while average yearly precipitation and runoff decreased by 7.72%. Conversely, evapotranspiration rose by 7.54%. Additionally, shrubland expanded by 2.28%, grassland increased by 12.9%, and barren areas decreased by 15.23%. Such alterations directly affect water availability for agriculture and other essential uses, exacerbating vulnerability to natural hazards. Future research should focus on developing targeted adaptation strategies for the Arghandab River Basin, emphasizing the need for integrated water management practices and innovative groundwater recharge techniques to mitigate the impacts of climate change.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 2","pages":"Pages 380-390"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Natural hazard induced coastal vulnerability in Indian Sundarbans: A village-level study by using geospatial and statistical techniques","authors":"Md Hasnine,&nbsp;Dewaram Abhiman Nagdeve","doi":"10.1016/j.nhres.2024.10.004","DOIUrl":"10.1016/j.nhres.2024.10.004","url":null,"abstract":"<div><div>The Sundarbans, an area with a history dating back to the dawn of civilization, has faced numerous environmental hazards that have remarkably affected the lives and livelihoods of its natives. Strom surge and Tropical cyclones are the most substantial natural hazards, causing severe damage to local communities by affecting food security, the economy, shelter and health. By defining vulnerability as a function of exposure, sensitivity, and resilience capacity, we calculated a composite vulnerability index (CVI) using equal weight method (EWM) to assess the vulnerability of mouzas (small administrative units) to natural hazards in the Sundarbans, India. The vulnerability map has been drawn based on composite value of CVI which shows 30.50% of villages falling into the high vulnerability category and 12.06% in the very high vulnerability category. The mouza-level analysis also indicates that 22.62% of Sundarbans's villages are highly exposed to natural hazards and 19.70 % of villages are classified as being at Very high sensitive. Only 7.07% villages have very high adopted capacity against these natural hazards. Villages in the southern parts and along the coast were found to be more vulnerable to storm surges. Conversely, those situated at higher elevations in the central area exhibited lower vulnerability. In the northern part of the region, several villages faced high to very high vulnerability due to low-lying, waterlogged wetlands. This study offers vital insights for decision-makers, government planners, and disaster management professionals, assisting in the identification of high-risk populations and areas that require immediate preservation efforts.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 2","pages":"Pages 262-275"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lightweight CNN model for automatic detection and depth estimation of subsurface voids using GPR B-scan data","authors":"Abdelaziz Mojahid ,&nbsp;Driss EL Ouai ,&nbsp;Khalid EL Amraoui ,&nbsp;Khalil EL-Hami ,&nbsp;Hamou Aitbenamer ,&nbsp;Jochem Verrelst ,&nbsp;Pier Matteo Barone","doi":"10.1016/j.nhres.2025.02.001","DOIUrl":"10.1016/j.nhres.2025.02.001","url":null,"abstract":"<div><div>Subsurface cavities pose significant risks, including structural instability, safety hazards, and environmental damage. Early detection of these cavities is crucial to prevent material losses and protect human lives. Investigation and manual processing of these structures using traditional methods can be difficult and time-consuming. Therefore, automated approaches using machine learning algorithms for identifying subsurface anomalies have recently emerged, providing promising pathways for real-time cavity detection. Consequently, this study proposes a Convolutional Neural Network (CNN)-based framework for the automated detection and depth estimation of subsurface cavities from Ground Penetrating Radar (GPR) B-scan images. The model was trained on 1408 augmented B-scans collected with 200 and 400 ​MHz antennas across various subsurface materials, ensuring exposure to a wide range of material types with different electromagnetic properties. Testing experiments were performed using eight profiles where cavity detection was confirmed by borehole data. The results demonstrate an impressive 100% success rate for cavity detection and over 95% accuracy in depth estimation. Comparing this model to other deep learning-based methods, our results show great remarkable performance tested in various subsurface environments. Furthermore, the model's lightweight design can be deployed on normal portable computing machines, enabling real-time cavity detection and depth estimation during the acquisition. The proposed approach in this study provides practical solutions that can have a significant impact in civil engineering applications, providing an efficient and reliable tool for subsurface challenging problems.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 2","pages":"Pages 432-446"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning approaches for mapping and predicting landslide-prone areas in São Sebastião (Southeast Brazil)","authors":"Enner Alcântara ,&nbsp;Cheila Flávia Baião ,&nbsp;Yasmim Carvalho Guimarães ,&nbsp;José Roberto Mantovani ,&nbsp;José Antonio Marengo","doi":"10.1016/j.nhres.2024.10.003","DOIUrl":"10.1016/j.nhres.2024.10.003","url":null,"abstract":"<div><div>This study employs machine learning techniques to map and predict landslide-prone areas in São Sebastião, Brazil, a region susceptible to landslides due to its steep terrain and intense rainfall. We compared five algorithms: Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Network, and k-Nearest Neighbors, using various environmental factors as inputs. The Gradient Boosting model performed best, achieving an AUC-ROC of 0.963 and an accuracy of 99.6%. Slope degree, soil moisture index, and relief dissection emerged as the most influential factors in predicting landslide susceptibility. Analysis of land use and land cover changes between 1985 and 2021 revealed significant increases in forest cover and urban areas, with implications for landslide risk distribution. The resulting susceptibility map shows predominantly low-risk areas with scattered high-risk zones, providing crucial information for targeted risk management. This research demonstrates the effectiveness of machine learning in landslide susceptibility mapping and offers valuable insights for disaster risk reduction and urban planning in coastal mountainous regions.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 2","pages":"Pages 247-261"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A massive landslide damaged NHPC's Teesta V hydropower station on 20th August 2024 ​at Balutar, Sikkim Himalayas","authors":"Biswajit Bera","doi":"10.1016/j.nhres.2024.12.005","DOIUrl":"10.1016/j.nhres.2024.12.005","url":null,"abstract":"<div><div>A gigantic landslide occurred at Dipu-Dara near Singtam of Himalayan State Sikkim above the powerhouse of the NHPC's (National Hydroelectric Power Corporation) Teesta stage V dam power station on August 20, 2024. The Gas Insulated Switchgear building of the powerhouse was partly smashed and this landslide also affected the six residential buildings. The large cracks developed along the Singtam-Dikchu road which is the significant lifeline of Gangtok Town as well as North Sikkim. This study attempts to identify the principal causes and probable effects at the proximity region. Here, the geotechnical investigation has been done for slope stability using the limit equilibrium method (LEM). A total of three slopes (rock-debris) have been considered and the physical properties of the slopes have been systematically measured (slope material, angle, orientation, height, etc.) during a field survey in September 2024. SAR (C-band) imageries (Synthetic aperture radar, Sentine-1A) have been used here for InSAR coherence analysis before (09-08-2024) and after the event (21-08-2024). Results showed that most of the slopes (above 45°) along the riverside of Teesta are characterized by unconsolidated loose soil-forming materials of Phyllitic rock. At the time of GLOF, 2023, the slopes near the powerhouse were affected by the devastating flood through toe erosion. Here, this rock type experiences alternating dry and wet cycles which weaken its mechanical strength, develop cracks, and trigger slope failure. This 17 ​km NHPC headrace tunnel runs through fragile phyllite, schist, slate and quartzite rocks and it reduces the rock strength. Numerous past earthquake epicenters (the highest 5.45 magnitude, 2013) are also located between Dikchu and Signtam. The result of the LEM showed that the safety factor value of the landslide slope was 1.069, representing a little stable slope. This study will help policymakers for long-term sustainable hillslope as well as landslide management, particularly for the Himalayan tourism industry and border security.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 2","pages":"Pages 372-379"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of gully erosion susceptibility using four data-driven models AHP, FR, RF and XGBoosting machine learning algorithms","authors":"Md Hasanuzzaman ,&nbsp;Pravat Shit","doi":"10.1016/j.nhres.2024.05.001","DOIUrl":"10.1016/j.nhres.2024.05.001","url":null,"abstract":"<div><div>Gully erosion is a significant global threat to socioeconomic and environmental sustainability, making it a widespread natural hazard. Developing spatial models for gully erosion is crucial for local governance to effectively implement mitigation measures and promote regional development. This study applied two machine learning (ML) models, RF and XGB, alongside an AHP-based multi-criteria decision method and FR bivariate statistics, to assess gully erosion susceptibility (GES) in the Kangsabati River basin in eastern India's Chotonagpur plateau fringe. A GIS database was created, incorporating recorded gully erosion incidents and 20 conditioning variables, which were evaluated for multicollinearity. These variables served as predictive factors for assessing gully erosion presence in the study area. The models' performance was evaluated using metrics such as RMSE, MAE, specificity, sensitivity, and accuracy. The XGB model outperformed the others, achieving a predictive accuracy of 90.22%. The study found that approximately 6.56% of the Kangsabati catchment is highly susceptible to gully erosion, with 12.39% moderately susceptible and 81.05% not susceptible. The XGB model had the highest ROC value of 85.5 during testing, indicating its superiority over the FR (ROC ​= ​81.7), AHP (ROC ​= ​79.8), and RF (ROC ​= ​83.8) models. These findings highlight the XGB model's efficacy and potential for large-scale GES mapping.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 1","pages":"Pages 36-47"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geospatial intelligence for landslide susceptibility and risk analysis: Insights from NH31A and east Sikkim Himalaya settlements","authors":"Sk Asraful Alam ,&nbsp;Sujit Mandal ,&nbsp;Ramkrishna Maiti","doi":"10.1016/j.nhres.2024.10.001","DOIUrl":"10.1016/j.nhres.2024.10.001","url":null,"abstract":"<div><div>Slope instability is a serious concern in the Sikkim Himalayas. The town and numerous road segments along National Highway 31A were ravaged by multiple landslides that occurred in the nearby region. A bivariate statistical method known as frequency ratio (FR), information value (IV), and certainty factor (CF) analysis was employed in this work to examine landslide risk assessment (LRA) and landslide susceptibility zonation (LSZ) maps in the Rorachu watershed. This study represents the first comprehensive analysis of landslide risk in the populated areas of East Sikkim and along NH31A, offering a deeper understanding of the risks involved and contributing to the enhancement of local resilience against landslide hazards. A total of 153 different landslide locations were mapped using Google Earth and GIS software; 30% (46) of these locations were used to validate the models, and 70% of these (107) served as training data for the FR, IV, and CF models. The thirteen landslide causative factors (geology, soil, elevations, slope, curvature, drainage density (DD), road density (RD), rainfall, normalized difference vegetation index (NDVI), land use land cover (LULC), topographic position index (TPI), stream power index (SPI), and topographic wetness index (TWI)) were extracted from a spatial database for LSZ mapping. Landslides were most prevalent on slopes (35°–50°), heights (2500–4100 ​m), and rainfall (2000–2500 ​mm and 3000–3300 ​mm). The area under the curves (AUC) for the FR, IV, and CF models are 0.925 (92.50%), 0.846 (84.60%), and 0.868 (86.20%), respectively. The prediction rates are shown by the AUCs for the FR, IV, and CF models, which are 0.828 (82.8%), 0.750 (%), and 0.836 (83.60%), respectively. According to the landslide risk assessment (LRA), the FR (20.75%), IV (40.91%) and CF (18.78%) models showed high risk on Highway 31A, while the FR (9.05%), IV (38.59%) and CF (20.90%) models showed high risk in densely populated areas. These landslide risk and vulnerability maps can be used to develop land use planning strategies that can save lives and are useful for planners and mitigation measures. Special attention should be paid to urbanization, highway construction, and deforestation.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 1","pages":"Pages 187-208"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}