Susceptibility Model Research Articles

Landslide susceptibility assessment using different rainfall event-based landslide inventories: advantages and limitations

AbstractThe present work aims to evaluate potential sources of uncertainty associated with rainfall-triggered event-based landslide inventories within the framework of landslide susceptibility assessment. Therefore, this study addresses the following questions: (i) How representative is an event-based landslide inventory map of the total landslide activity and distribution in a study area?; (ii) How reliable is an event-based landslide susceptibility map?; (iii) How appropriate is an event-based landslide inventory map for independently validating a landslide susceptibility map? To address these questions, two independent and contrasting rainfall event-based landslide inventories were used, together with a historical landslide inventory, to assess landslide susceptibility for different types of landslides in a study area located north of Lisbon, Portugal. The results revealed the following findings: (i) contrasting rainfall critical conditions for failure can trigger similar landslide types, although they may vary in size and be spatially constrained by different predisposing conditions, particularly lithology and soil type; (ii) landslide susceptibility models using event-based landslide inventories are not reliable in the study area, regardless of the landslide inventory map used for training and validation; and (iii) complementary sources of uncertainty results from using incomplete historical landslide inventories to assess landslide susceptibility and non-totally independent landslide inventories for modeling validation. The present study enhances the understanding of regional landslide susceptibility patterns based on contrasting rainfall-trigger conditions, providing valuable information to minimize exposure; to design regional landslide early warning systems for specific rainfall-trigger landslide events; and to improve the response and preparedness of civil protection services.

Effects of Negative Emotionality on Parenting and Problem Behaviors During Early Childhood: Moderating Effects of Mother and Fathers’ Effortful Control and Parenting Behavior

Objectives: This study aimed to examine how mothers’ and fathers’ effortful control moderates the relationship between young children’s negative emotionality and mothers and fathers’ parenting behavior, and how parenting behavior moderates the relationship between young children’s negative emotionality and their problematic behavior.Methods: A survey was conducted among 143 fathers and 205 mothers of children aged 3-5 years through self-report assessments of their young children’s negative emotionality, problem behavior, and their own effortful control, and parenting behavior. Data were analyzed using correlation analysis, frequency analysis, and hierarchical regression analyses with SPSS 22.0.Results: Mothers’ effortful control significantly moderated the association between children’s negative emotionality and the mothers’ rejective parenting behaviors. Mothers’ reactive parenting behavior moderated the association between children’s negative emotionality and problematic behavior. Fathers’ rejective parenting behavior significantly moderated the effect of children’s negative emotionality on problematic behavior.Conclusion: This study is meaningful as it confirms a two-way relationship between children and their mothers or fathers according to “a process model of determinants of parenting”. Partially supported by “Differential susceptibility model”, the parenting behavior moderated the effect of children’s negative emotionality on their problematic behavior. In addition, mothers and fathers’ effortful control affected their positive parenting behavior; however only mothers’ internal factors moderated the effect of children’s negative emotionality on the parenting behavior. Finally, verifying whether mothers and fathers’ parenting behavior moderates the effect of children’s negative emotions on problem behavior differently is important.

Landslide susceptibility mapping core-base factors and models’ performance variability: a systematic review

Landslide susceptibility mapping core-base factors and models’ performance variability: a systematic review

Machine learning approaches to debris flow susceptibility analyses in the Yunnan section of the Nujiang River Basin.

The Yunnan section of the Nujiang River (YNR) Basin in the alpine-valley area is one of the most critical areas of debris flow in China. We analyzed the applicability of three machine learning algorithms to model of susceptibility to debris flow-Random Forest (RF), the linear kernel support vector machine (Linear SVM), and the radial basis function support vector machine (RBFSVM)-and compared 20 factors to determine the dominant controlling in debris flow occurrence in the region. We found that (1) RF outperformed RBFSVM and Linear SVM in terms of accuracy, (2) topographic conditions were prerequisites, and geology, precipitation, vegetation, and anthropogenic influence were critical to forming debris flows. Also, the relative elevation difference was the most prominent evaluation factor of debris flow susceptibility, and (3) susceptibility maps based on RF's debris flow susceptibility (DFS) showed that zones with very high susceptibility were distributed along the mainstream of the Nujiang River. These findings provide methodological guidance and reference for improvement of DFS assessment. It enriches the content of DFS studies in the alpine-valley areas.

Landslide susceptibility assessment through multi-model stacking and meta-learning in Poyang County, China

This study aims to evaluate the effectiveness of various individual machine learning and their ensemble techniques such as Stacking, Voting and Meta-learning in landslide susceptibility assessment taking Poyang, Jiangxi, China as an example. Multi-source geo-environmental data including field surveys, Sentinel-2A/B satellite images, Digital Elevation Models (DEM), and geological and hydrological data were utilized to construct and validate landslide susceptibility models. Results show that the Stacking Classifier outperformed other models, achieving the highest F1 Score of 0.846 and AUC (Area Under ROC Curve) of 0.923, demonstrating its strong predictivity, followed by the Voting Classifier with the F1 Score of 0.829 and AUC of 0.922. Among the individual models, the Multi-Layer Perceptron (MLP) performed best with the F1 Score of 0.828 and AUC of 0.904. Furthermore, the explainable Artificial Intelligence (XAI) technique was applied to better understand the mechanism of classifiers in predicting landslide susceptibility and it suggests a significant correlation between land use, distance to fault, and landslide occurrences. In conclusion, Stacking and Voting hybrid learning models show clear advantages over the individual ones for landslide risk zoning. The results of study may provide technical support for disaster mitigation efforts and future urban planning in areas prone to landslides in Poyang.

A Study on the Frequency-Domain Black-Box Modeling Method for the Nonlinear Behavioral Level Conduction Immunity of Integrated Circuits Based on X-Parameter Theory.

During circuit conduction immunity simulation assessments, the existing black-box modeling methods for chips generally involve the use of time-domain-based modeling methods or ICIM-CI binary decision models, which can provide approximate immunity assessments but require a high number of tests to be performed when carrying out broadband immunity assessments, as well as having a long modeling time and demonstrating poor reproducibility and insufficient accuracy in capturing the complex electromagnetic response in the frequency domain. To address these issues, in this paper, we propose a novel frequency-domain broadband model (Sensi-Freq-Model) of IC conduction susceptibility that accurately quantifies the conduction immunity of components in the frequency domain and builds a model of the IC based on the quantized data. The method provides high fitting accuracy in the frequency domain, which significantly improves the accuracy of circuit broadband design. The generated model retains as much information within the frequency-domain broadband as possible and reduces the need to rebuild the model under changing electromagnetic environments, thereby enhancing the portability and repeatability of the model. The ability to reduce the modeling time of the chip greatly improves modeling efficiency and circuit design. The results of this study show that the "Sensi-Freq-Model" reduces the broadband modeling time by about 90% compared to the traditional ICIM-CI method and improves the normalized mean square error (NMSE) by 18.5 dB.

Fast physically-based probabilistic modelling of rainfall-induced shallow landslide susceptibility at the regional scale considering geotechnical uncertainties and different hydrological conditions

The inherent uncertainty in hydro-geotechnical parameters presents a significant challenge for accurately predicting rainfall-triggered shallow landslides in mountainous regions. In this study, a novel probabilistic framework was developed and implemented in the “Py.GIS-FSLAM-FORM” software, designed to address the complexities associated with parameter uncertainty, correlation, and distribution. By combining the Fast Shallow Landslide Assessment Model (FSLAM) with the First-Order Reliability Method (FORM), we have enhanced the traditional probabilistic approach to create more accurate landslide susceptibility maps. This study emphasizes the uncertainly of geotechnical parameters and the critical influence of hydrological conditions on landslide susceptibility, especially focusing on the interaction between antecedent recharge (qa) and event rainfall (Pe). In our study area (Val d’Aran, Spain), the probabilistically based results revealed that areas of very high susceptibility are significantly affected by event rainfall, particularly on slopes of 30–40 degrees and aspects between 100 and 250 degrees. The variability in geotechnical parameters, especially the coefficient of variation (COV) in cohesion and friction angle, plays a crucial role in landslide susceptibility assessment, with increased COVs leading to greater landslide uncertainty. Additionally, cross-negative correlations and non-normal distributions of geotechnical parameters substantially influence the spatial distribution of landslides, notably when combining antecedent recharge with event rainfall. These results highlight the importance of incorporating parameter variability and hydrological conditions in susceptibility models to improve the precision of regional landslide forecasts. While the study was performed in Val d'Aran, its methodologies and conclusions are relevant to mountainous areas worldwide, offering insights for refining landslide prediction models and susceptibility assessments, contributing to global efforts in landslide disaster prevention.

Repetitive Infection Spreading and Directed Evolution in the Susceptible–Infected–Recovered–Susceptible Model

Repetitive Infection Spreading and Directed Evolution in the Susceptible–Infected–Recovered–Susceptible Model

Landslide susceptibility modeling in the Kulfo river catchment, rift valley, Ethiopia: An integrated geospatial and statistical analysis

Landslides occur when debris, rocks, or soil particles move downward. Examining the susceptibility of landslides is essential for safeguarding human well-being and assessing the consequences of landslides on the natural surroundings and ecosystems. This study utilized the frequency ratio technique to evaluate the probability of landslides happening in the Kulfo River watershed, situated in the Rift Valley area of Ethiopia. In order to ensure a comprehensive analysis, many data sources were employed, including satellite images, geological data, and historical records of landslides. This study developed a systematic approach to assess the probability of landslides by considering ten (10) influential factors: land use/land cover, slope, aspect, elevation, curvature, lithology, proximity to lineament, and normalized difference vegetation index (NDVI). The ten influential elements were prioritised based on literature review, expert knowledge, and preliminary study area analysis. The aforementioned causal elements are integrated with a comprehensive landslide inventory map. The sensitivity of the study's area to landslides was mapped using a frequency ratio (FR) model. Subsequently, it was classified as representing various degrees of vulnerability, spanning from extremely minimal to quite significant. The effectiveness of the suggested model was measured by evaluating the accuracy of the generated map of landslide susceptibility by the area under the curve (AUC) technique. Based on the most recent study results, the success rate curve has an area under the curve (AUC) of 79.6%, which indicates a highly satisfactory level of performance. Policymakers may utilize the findings of this study to make educated decisions on how to mitigate the risks of landslides in relation to land use and preparedness for disasters.

Modeling and analyzing competitive epidemic diseases with partial and waning virus-specific and cross-immunity

In this paper, we consider a novel mathematical modeling framework for the spread of two competitive diseases in a well-mixed population. The proposed framework, which we term a bivirus SIRIS model, encapsulates key real-world features of natural immunity, accounting for different levels of (partial and waning) virus-specific and cross protection acquired after recovery. Formally, the proposed framework consists of a system of coupled nonlinear ordinary differential equations that builds on a classical bivirus susceptible–infected–susceptible model by means of the addition of further states to account for (temporarily) protected individuals. Through the analysis of the proposed framework and of two specializations, we offer analytical insight into how natural immunity can shape a wide range of complex emergent behaviors, including eradication of both diseases, survival of the fittest one, or even steady-state co-existence of the two diseases.

Variance of the Infection Number of Heterogeneous Malware Spread in Network

The Susceptible–Infected–Susceptible (SIS) model in complex networks is one of the critical models employed in the modeling of virus spread. The study of the heterogeneous SIS model with a non-homogeneous nodal infection rate in finite-size networks has attracted little attention. Investigating the statistical properties of heterogeneous SIS epidemic dynamics in finite networks is thus intriguing. In this paper, we focus on the measure of variability in the number of infected nodes for the heterogeneous SIS epidemic dynamics in finite-size bipartite graphs and star graphs. Specifically, we investigate the metastable-state variance of the number of infected nodes for the SIS epidemic process in finite-size bipartite graphs and star graphs with heterogeneous nodal infection rates. We employ an extended individual-based mean-field approximation to analyze the heterogeneous SIS epidemic process in finite-size bipartite networks and star graphs. We derive the approximation solutions of the variance of the infected number. We verify the proposed theory by simulations. The proposed theory has the potential to help us better understand the fluctuations of SIS models like epidemic dynamics with a non-homogeneous infection rate.

Flood susceptibility modeling by integrating tree‐based regression with metaheuristic algorithm, BWO

AbstractFloods are becoming more widely acknowledged as a common occurrence of nature's dangers on a global scale. Although forecasting models primarily focus on timely warnings, models aimed at evaluating dangerous zones can play a vital role in shaping policies for adaptation, mitigation, and reducing the risk of disasters. Using machine learning techniques including hybrid black widow optimization (BWO) with XGBoost, LGBoost, and AdaBoost. We generate a flood susceptibility map for considered region of lower mahanadi basin (LMB). This study examines the effectiveness of these machine learning models in assessing and mapping flood susceptibility, while also providing suggestions for future research in this area. Flood susceptibility model was developed using 13 variables: Altitude, Aspect, Curvature, Distance from river, Drainage Density, Stream Power Index (SPI), Sediment Transport Index (STI), Rainfall intensity, Land Use Land Cover (LULC), Topographic Wetness Index (TWI), Terrain Roughness Index (TRI), Normalized Difference Vegetation Index (NDVI), and slope. Additionally, flood inventory data were incorporated into the model. Dataset was divided into a 70% portion for training model and a 30% portion for validating model. To assess the performance of the model, several evaluation metrics were employed, including receiver operating characteristic (ROC) curve and other performance indices. Evaluation of flood susceptibility mapping, using ROC curve method in combination with flood density yielded strong and reliable results for various models. BWO‐XGBoost achieved a score of 0.889, BWO‐LGBoost achieved a score of 0.937, and BWO‐ADABoost achieved a score of 0.904. These scores indicate effectiveness of these models in accurately predicting flood susceptibility in the study area. A comparison was made with commonly used methods in flood susceptibility assessment to evaluate the efficiency of proposed models. It was found that having a first‐class and enlightening database is crucial for accurately classifying flood types in flood susceptibility mapping. This aspect greatly contributes to improving the overall performance of the model. Among the evaluated methods, the hybrid model BWO‐LGBoost demonstrated better performance compared with others, indicating its effectiveness in accurately predicting flood susceptibility.

New method for landslide susceptibility evaluation in alpine valley regions that considers the suitability of InSAR monitoring and introduces deformation rate grading

ABSTRACT Incorporating Interferometric Synthetic Aperture Radar (InSAR) data sources into the landslide susceptibility evaluation process has yielded favorable outcomes in some studies. However, there are fewer analyses of the applicability of InSAR data monitoring, and a framework of accurate and effective grading criteria needs to be developed. To overcome these limitations, this study presents a novel and precise approach for landslide susceptibility assessment in alpine valley regions. This method incorporates the suitability of InSAR monitoring and introduces a grading system based on deformation rates, enhancing accuracy and efficiency. Taking the Dongchuan district, the most typical high mountain valley area in southwest China, as the research object, the SAR is quantitatively simulated and analyzed in this study using the improved R-index shadow layover method. Then, the optimal Synthetic Aperture Radar (SAR) monitoring scheme is derived, calculate deformation rates using LiCSBA (small baseline subset with the automated sentinel-1 InSAR processor) technology, and accurately establish the extent of landslide hazards (including potential landslides) by incorporating high-resolution images. The criteria for grading susceptibility within the landslide hazard range are based on deformation rates. Evaluation factors for corresponding grid cells are obtained. The best evaluation factors are selected using covariance diagnosis and gray correlation analysis. The landslide susceptibility model is developed utilizing the Particle Swarm Optimization-Back Propagation (PSO-BP) algorithm. It includes evaluation techniques for regions without deformation rates. The study findings demonstrate that: (i) Analyzing SAR suitability in alpine and canyon areas is crucial. Complementary monitoring with SAR lift tracks may only sometimes resolve geometric distortion issues in all these regions. (ii) The InSAR deformation rate can be an essential evaluation factor for landslide susceptibility evaluation. (iii) The proposed method effectively addresses low coherence challenges in certain alpine valley regions, where grading based on deformation rate is complex. The landslide susceptibility evaluation model is validated using performance evaluation indexes (Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-Square (R2)), confirming its reliability and effectiveness. (iv) The proposed method improves the grading accuracy by 37.84 ~ 60.91%. Overall, our proposed new landslide susceptibility method brings a new way of evaluating landslide susceptibility in alpine valley regions.

Rural community response to accidental toxic gas release: An individual emergency response model during self-organized evacuations

The emergency response ability in rural areas is weaker than in urban areas. Once a major accident occurs in a rural area, it is more likely to cause serious consequences. Thus, we propose a individual emergency response model during self-organized evacuations after accidental toxic gas releases in rural areas. The proposed model can reproduce emergency information dissemination and individual emergency decision-making in self-organized evacuations. We propose an improved susceptible infected recovered model for emergency information dissemination process. At the same time, we propose a emergency decision-making mechanism to simulate individual emergency actions. In this model, individual personality characteristics will affect the accessing and disseminating individual emergency information. Then, we conduct a simulation experiment based on methanol leakage in a rural community. Through comparative experiments, we found that the traditional model may be too pessimistic in estimating emergency information dissemination results, underestimating shadow evacuation. In noncompliance evacuation areas, loudspeakers can inhibit individuals from unnecessarily evacuating who are close to it, and may cause individuals far away from it to evacuate unnecessarily. When formulating emergency response strategies, managers should focus on groups with weak access to information and consider the impact of loudspeaker locations on individual emergency response actions.

Advanced risk assessment framework for land subsidence impacts on transmission towers in salt lake region

To ensure the continuous transmission of clean energy for electricity, transmission towers often traverse geologically challenging terrains. This is particularly evident in the Salt Lake region, where the convergence of unique geological attributes and environmental stressors leads to recurrent land subsidence incidents, jeopardizing the integrity of power lines. In light of the prohibitive costs associated with indiscriminate remedial actions, precision in risk assessment and management is imperative to streamline these interventions. The objective of this study is to propose an accurate and effective framework for assessing the risk of land subsidence for power transmission towers. To achieve this, by utilizing the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology and cross-validation from field investigations, representative sample disaster datasets of surface deformation points of transmission towers are obtained in the first. Two optimization approaches for susceptibility modeling were employed to further enhance the susceptibility model for land subsidence and obtain high-precision susceptibility results along the transmission towers. Then, combined on-site damage investigation with SBAS-InSAR technology to conduct subsidence vulnerability analysis of transmission towers. Finally, the ultimate outcome of this process was the development of a risk assessment matrix based on spatial probability (susceptibility) and damage scales (vulnerability), aimed at more accurately assessing land subsidence risk. This framework could identify the spatial distribution of subsidence susceptibility in transmission towers exposed to potential damage, which could be followed by a more detailed transmission tower-based vulnerability and risk assessment. The method has been successfully applied in the Salt Lake area, and the susceptibility results based on the Grey Wolf Optimization-Random Forest (GW0-RF) model have shown the best modeling effect, and the area under of receiver operating characteristic curve (AUC) = 0.95. The vulnerability results show that 105 towers have been most severely damaged by land subsidence The risk distribution map shows that the central part of the Salt Lake area is the core zone for future land subsidence risk management. A total of 101 towers are in the high and very high risk level. The insights garnered from this investigation furnish a comprehensive understanding of the risk landscape for transmission towers, underpinning future-focused design, planning, and management strategies.

Generation of a lethal mouse model expressing human ACE2 and TMPRSS2 for SARS-CoV-2 infection and pathogenesis

Mouse models expressing human ACE2 for coronavirus disease 2019 have been frequently used to understand its pathogenesis and develop therapeutic strategies against SARS-CoV-2. Given that human TMPRSS2 supports viral entry, replication, and pathogenesis, we established a double-transgenic mouse model expressing both human ACE2 and TMPRSS2 for SARS-CoV-2 infection. Co-overexpression of both genes increased viral infectivity in vitro and in vivo. Double-transgenic mice showed significant body weight loss, clinical disease symptoms, acute lung injury, lung inflammation, and lethality in response to viral infection, indicating that they were highly susceptible to SARS-CoV-2. Pretreatment with the TMPRSS2 inhibitor, nafamostat, effectively reduced virus-induced weight loss, viral replication, and mortality in the double-transgenic mice. Moreover, the susceptibility and differential pathogenesis of SARS-CoV-2 variants were demonstrated in this animal model. Together, our results demonstrate that double-transgenic mice could provide a highly susceptible mouse model for viral infection to understand SARS-CoV-2 pathogenesis and evaluate antiviral therapeutics against coronavirus disease 2019.

Landslide Susceptibility Mapping Using Machine Learning: A Case Study of Oregon

Landslide susceptibility assessment, a crucial component of disaster prevention and mitigation, has traditionally relied on geological datasets and commonly used evaluation metrics. However, these metrics, often applied to 2D susceptibility maps, may not capture the full complexity of the issue. In this study, we incorporated spatial distribution data, including a historical landslide inventory, and integrated morphological, geological, hydrological, and land-use features. This approach enabled the construction of predictive models using eight distinct algorithms. The evaluation of classifier performance scores unveiled the superior performance of gradient tree boosting. In contrast, logistic regression struggled to handle the nonlinear aspects of landslide susceptibility analysis. A finding in our research was the consistent disparity in predictive susceptibility maps, even when similar performance scores were achieved across different algorithms. This underscores the necessity of combining evaluation metrics with spatial mapping in landslide susceptibility model assessment. We also highlighted the critical role of digital elevation models in constructing effective models when feature data was limited. Conversely, when abundant information was available, the integration of multi-source data significantly enhanced the precision of landslide susceptibility mapping. Overall, this study provides valuable insights for spatial prioritization in landslide field investigations and enhances our understanding of landslide risk assessment.

Why are social media users susceptible to health misinformation? A perspective from complexity theory

PurposeHealth misinformation on social media threatens public health. A critical question that sheds light on the propagation of health misinformation across social media platforms revolves around identifying the specific types of social media users susceptible to this issue. This study provides an initial insight into this matter by examining the underlying psychological mechanism that renders users susceptible to health misinformation.Design/methodology/approachIn this study, we developed an integrated model of susceptibility to health misinformation, drawing on the motivation-opportunity-ability theory and the elaboration likelihood model. We collected the data from a sample of 342 social media users in China. Furthermore, the fuzzy-set qualitative comparative analysis was adopted to examine the proposed model and uncover the causal recipes associated with susceptibility to health misinformation.FindingsThe results indicated that there are three configural types of users that are susceptible to health misinformation: the health-consciousness core-driven type, the popularity-driven core type and the dual-driven type characterized by both high health consciousness and information popularity. Among these, high health-consciousness and the reliance on information popularity-based pathways emerge as pivotal factors influencing susceptibility to health misinformation.Originality/valueThis study contributes to the social media literature by identifying various psychological traits that lead to social media users’ susceptibility to health misinformation. Additionally, the study provides comprehensive guidance on how to mitigate the spread of health misinformation.

Application of a Soil Erosion Susceptibility Model Using Unmanned Aerial Vehicle Photogrammetry in a Timber Harvesting Area, South Korea

Application of a Soil Erosion Susceptibility Model Using Unmanned Aerial Vehicle Photogrammetry in a Timber Harvesting Area, South Korea

Pixel-Based Spatio-Statistical Analysis of Landslide Probability in Humid and Seismically Active Areas of Himalaya and Hindukush

The Hindukush and Himalaya regions of Pakistan are chronically prone to several geological hazards such as landslides. Studying landslides in these regions is crucial for risk assessment and disaster management, as well as for determining the effects of adverse climatic conditions, infrastructure management, and increasing anthropogenic activities. High-relief mountains in these regions face severe challenges because of frequently occurring landslides and other natural hazards, especially during intensive rainfall seasons and seismic activity, which destroy infrastructure and cause injuries and deaths. Landslides in the Alpuri Valley (Hindukush) and the Neelum Valley (Himalaya) have been activated through high magnitude earthquakes, intensive rainfalls, snowfall, floods, and man-made activities. Landslide susceptibility mapping in these areas is essential for sustainable development as it enables proactive risk management, up-to-date decision-making, and effective responses to landslide hazards, ultimately safeguarding human lives, property, and the environment. In this study, the relative effect method was applied for landslide susceptibility modeling in both study areas to determine the capability to reduce the effects of landslides, and to improve the prediction accuracy of the method. The relative effect is a statistical model that has only been used for very limited time for landslide susceptibility with effective results. A total of 368 (Neelum Valley) and 89 (Alpuri Valley) landslide locations were identified, which were utilized to prepare the reliable landslide inventory using GIS. In order to evaluate the areas at risk for future landslides activities and determine their spatial relationship with landslide occurrences, the landslide inventory was developed with 17 landslide causative factors. These factors include slope gradient, slope aspect, geology, plan curvature, general curvature, profile curvature, elevation, stream power index, drainage density, terrain roughness index, distance from the roads, distance from the streams, distance from fault lines, normalized difference wetness index, land-use/land-cover, rainfall, and normalized difference vegetation index. Finally, the performance of the relative effect method was validated using the success and prediction curve rate. The AUC-validated result of the success rate curve in the Alpuri Valley is 74.75%, and 82.15% in the Neelum Valley, whereas, the AUC-validated result of the prediction rate curve of the model is 87.87% in the Alpuri Valley and 82.73% in the Neelum Valley. These results indicate the reliability of the model to produce a landslide susceptibility map, and apply it to other landslide areas. The model demonstrated a more effective result in the Alpuri Valley, having a smaller area. However, the results are also desirable and favorable in Neelum Valley, with it being a large area. It will assist in general landslide hazard management and mitigation, and further research studies related to future landslide susceptibility assessments in other parts of the region.