Mapping Model Research Articles

Urban flood susceptibility mapping using remote sensing, social sensing and an ensemble machine learning model

Flood susceptibility mapping is crucial for urban disaster management. However, the heterogeneity of urban land use and the complexity of terrain pose challenges to the accuracy and generalizability of flood models. In this paper, we propose a framework named the EMF model for urban flood mapping. Specifically, the social sensing and remote sensing were used for flooding information collection. The XGBoost, Support Vector Classifier (SVC), Multilayer Perceptron (MLP), and Multimodal Deep Learning (MDL), were used as the predictive models, and their results were ensembled using a Random Forest model to produce the final outcome. Results show that the EMF model outperforms the standalone models, with an accuracy of 0.942 on the training set and 0.940 on the testing set. The accuracy of the five models is ranked as EMF > MDL > XGBoost > SVC > MLP. The flood map indicates that flooding has a greater impact on the outskirts and suburbs of the city compared to its central urban areas. Farmland is the most affected land type, making up 54.8 % of the flooded area. Overall, the proposed framework enables rapid and accurate identification of flood-prone areas, providing technical support for managers in formulating effective flood prevention strategies.

Learning spectral-indices-fused deep models for time-series land use and land cover mapping in cloud-prone areas: The case of Pearl River Delta

Mapping of highly dynamic changes in land use and land cover (LULC) can be hindered by various cloudy conditions with optical satellite images. These conditions result in discontinuities in high-temporal-density LULC mapping. In this paper, we developed an integrated time series mapping method to enhance the LULC mapping accuracy and frequency in cloud-prone areas by incorporating spectral-indices-fused deep models and time series reconstruction techniques. The proposed method first reconstructed cloud-contaminated pixels through time series filtering, during which the cloud masks initialized by a deep model were refined and updated during the reconstruction process. Then, the reconstructed time series images were fed into a spectral-indices-fused deep model trained on samples collected worldwide for classification. Finally, post-classification processing, including spatio-temporal majority filtering and time series refinement considering land–water interactions, was conducted to enhance the LULC mapping accuracy and consistency. We applied the proposed method to the cloud- and rain-prone Pearl River Delta (i.e., Guangdong–Hong Kong–Macao Greater Bay Area, GBA) and used time series Sentinel-2 images as the experimental data. The proposed method enabled seamless LULC mapping at a temporal frequency of 2–5 days, and the production of 10 m resolution annual LULC products in the GBA. The assessment yielded a mean overall accuracy of 87.01% for annual mapping in the four consecutive years of 2019–2022 and outperformed existing mainstream LULC products, including ESA WorldCover (83.98%), Esri Land Cover (85.26%), and Google Dynamic World (85.06%). Our assessment also reveals significant variations in LULC mapping accuracies with different cloud masks, thus underscoring their critical role in time series LULC mapping. The proposed method has the potential to generate seamless and near real-time maps for other regions in the world by using deep models trained on datasets collected globally. This method can provide high-quality LULC data sets at different time intervals for various land and water dynamics in cloud- and rain-prone regions. Notwithstanding the difficulties of obtaining high-quality LULC maps in cloud-prone areas, this paper provides a novel approach for the mapping of LULC dynamics and the provision of reliable annual LULC products.

Prediction of hourly wind speed time series at unsampled locations using machine learning

Various models for wind speed mapping have been developed, with increasing attention on models focusing on mapping wind speed distribution. This study extends these models to predict hourly wind speed time series at unsampled locations. A model based on the quantile mapping (QM) procedure was compared to a traditional and machine-learning model to interpolate wind speed spatially. These proposed models were also used with inputs from the ERA5 reanalysis dataset, enabling them to consider local variation in orography and large-scale wind fields. A widely used procedure for mean bias correction of reanalysis based on the Global Wind Atlas (GWA) was implemented and compared to the proposed models. It was found that the QM and machine learning model, both using input from ERA5, significantly outperformed GWA bias correction in terms of time series correlation and probability distribution. Despite being more computationally intensive than GWA bias correction, both models are recommended due to their significantly (in a statistical sense) superior performance.

A Multinational, Multicenter Study Mapping Models of Kidney Supportive Care Practice

IntroductionKidney supportive care (KSC) integrates kidney and palliative care to improve quality of life for people with chronic kidney disease. Despite increasing interest and global advocacy to integrate KSC into kidney care, evidence to guide optimal care delivery is limited. MethodsThis observational cross-sectional study used an online survey to describe current KSC models in Australia, Aotearoa-New Zealand and the United Kingdom (UK). ResultsBetween April and December 2022, 114 units responded (response rate 67%), with 66% having a dedicated KSC service (UK 74%, Australia 58%, New Zealand 67%). Many different healthcare professionals worked in KSC services with diversity in clinical resources and activities between units and across countries. Overall, funding for KSC services was low, with a median full time equivalent per unit (standardised per 100 people receiving haemodialysis) of 0.51 (interquartile range 0.17-1.05) and 4 units provided a service without allocated funding. The scope of KSC service was wide-ranging and prioritised activities included symptom management, psychological support, complex future treatment planning and discussion, and care coordination. There were no significant differences between countries in terms of location of care provision, frequency of review, referral patterns or discharge rates, but there was variation described within countries. ConclusionModels of KSC vary markedly across kidney units and between countries. Despite this variation there was consistency in terms of clinical priorities which were person-centred and focused on physical and psychosocial wellbeing. Further research is required to evaluate the effectiveness of KSC provision, alongside improved funding methods to ensure sustainable and equitable KSC delivery.

PQSDC: a parallel lossless compressor for quality scores data via sequences partition and run-length prediction mapping.

The quality scores data (QSD) account for 70% in compressed FastQ files obtained from the short and long reads sequencing technologies. Designing effective compressors for QSD that counterbalance compression ratio, time cost, and memory consumption is essential in scenarios such as large-scale genomics data sharing and long-term data backup. This study presents a novel parallel lossless QSD-dedicated compression algorithm named PQSDC, which fulfills the above requirements well. PQSDC is based on two core components: a parallel sequences-partition model designed to reduce peak memory consumption and time cost during compression and decompression processes, as well as a parallel four-level run-length prediction mapping model to enhance compression ratio. Besides, the PQSDC algorithm is also designed to be highly concurrent using multicore CPU clusters. We evaluate PQSDC and four state-of-the-art compression algorithms on 27 real-world datasets, including 61.857 billion QSD characters and 632.908 million QSD sequences. (1) For short reads, compared to baselines, the maximum improvement of PQSDC reaches 7.06% in average compression ratio, and 8.01% in weighted average compression ratio. During compression and decompression, the maximum total time savings of PQSDC are 79.96% and 84.56%, respectively; the maximum average memory savings are 68.34% and 77.63%, respectively. (2) For long reads, the maximum improvement of PQSDC reaches 12.51% and 13.42% in average and weighted average compression ratio, respectively. The maximum total time savings during compression and decompression are 53.51% and 72.53%, respectively; the maximum average memory savings are 19.44% and 17.42%, respectively. (3) Furthermore, PQSDC ranks second in compression robustness among the tested algorithms, indicating that it is less affected by the probability distribution of the QSD collections. Overall, our work provides a promising solution for QSD parallel compression, which balances storage cost, time consumption, and memory occupation primely. The proposed PQSDC compressor can be downloaded from https://github.com/fahaihi/PQSDC.

‘Conga lines’ of Ediacaran fronds: insights into the reproductive biology of early metazoans

Late Ediacaran strata from Newfoundland, Canada (~574–560 Ma) document near-census palaeocommunities of some of the earliest metazoans. Such preservation enables reproductive strategies to be inferred from the spatial distribution of populations of fossilized benthic organisms, previously revealing the existence of both propagule and stoloniferous reproductive modes among Ediacaran frondose taxa. Here, we describe ‘conga lines’: linear arrangements of more than three closely spaced fossil specimens. We calculate probabilistic models of point maps of 13 fossil-bearing bedding surfaces and show that four surfaces contain conga lines that are not the result of chance alignments. We then test whether these features could result from passive pelagic propagules settling in the lee of an existing frond, using computational fluid dynamics and discrete phase modelling. Under Ediacaran palaeoenvironmental conditions, preferential leeside settlement at the spatial scale of the conga lines is unlikely. We therefore conclude that these features are novel and do not reflect previously described reproductive strategies employed by Ediacaran organisms, suggesting the use of mixed reproductive strategies in the earliest animals. Such strategies enabled Ediacaran frondose taxa to act as reproductive generalists and may be an important facet of early metazoan evolution.

Quantifying aboveground biomass, soil organic carbon and erosion with a detailed crop map and PESERA model in the Yangtze River Basin

AbstractSoil erosion represents a primary threat to soil systems with adverse implications for ecosystem services, crop production, potable water and carbon storage. While numerous studies have quantified the spatial distribution of aboveground Biomass (AGB), soil erosion and soil organic carbon (SOC) in the Yangtze River Basin (YRB), limited attention has been given to assessing the contributions of different land use types and especially crop types to AGB, soil erosion and SOC. In most studies, cropland is taken as a land use class, while detailed crop types and rotation patterns, and their effect on soil erosion and SOC, vary significantly. In this study, we used the Metronamica model to generate a detailed crop rotation and distribution map across the YRB and subsequently employed the Pan‐European Soil Erosion Risk Assessment (PESERA) model to simulate the spatial distribution of AGB, soil erosion and SOC on a monthly basis. PESERA model simulations indicate an average soil erosion rate across the entire YRB of 7.7 ton/ha/yr, with erosion hotspots concentrated in the Sichuan Basin and the central‐southern regions. The southwestern region and western Sichuan show elevated levels of AGB and SOC, while the eastern plains display lower levels. Erosion rates are lowest in areas designated as artificial land, pasture and grassland, whereas croplands and fruit tree plantations experience the highest erosion rates. In terms of crop types, the highest erosion rates and lowest AGB are observed under fallow and potato cultivation, while the lowest erosion rates and highest AGB are found in rice‐wheat rotation fields. To the best of our knowledge, this is the first study taking detailed crop types and patterns into account while evaluating their effect at a relatively large scale (i.e., YRB). These findings can help to develop sustainable soil management and (cropping) conservation strategies.

Multi-objective model transformation chain exploration with MOMoT

Context:The increasing complexity of modern systems leads to an increasing amount of artifacts that are used along the model-based software and systems development lifecycle. This also includes model transformations, which serve for mapping models between representations, e.g., for verification and validation purposes. Objectives:Model repositories manage this variety of artifacts and promote reusability, but should also enable the bundling of compatible artifacts. Therefore, model transformations should be reused and arranged into transformation chains to support more complex transformation scenarios. The resulting transformation should correspond to the user’s interest in terms of quality criteria such as model coverage, transformation coverage, and number of transformation steps, thus assembling such chains becomes a multi-objective problem. Methods:A novel multi-objective approach for exploring possible transformation chains residing in model repositories is presented. MOMoT, a model-driven optimization framework, is leveraged to explore the transformation space spanned by the repository. For demonstration, three differently populated repositories are considered. Results:We have extended MOMoT with an exhaustive, multi-objective search that explores the entire model transformation space defined by graph transformation rules, allowing all possible transformation chains to be considered as solution. Accordingly, the optimal solutions were identified in the demonstration cases with negligible computation time. Conclusion:The approach assists modelers when there are multiple chains for transforming an input model to a specified output model to consider. Our evaluation shows that the approach elicits all legitimate transformation chains, thus enabling the modelers to consider trade-offs in view of multiple criteria selection.

Evidence for an Outer Component in the Continuum Reverberation Mapping of Active Galactic Nuclei

Continuum reverberation mapping is widely used in studying the accretion disks of active galactic nuclei (AGNs). Some indirect evidence and simulations have indicated that the diffuse continuum, especially the strong Balmer continuum from the broad-line region, may contribute to the continuum in the u/U band. Here, we present direct evidence for this contribution. In this work, we apply the ICCF-Cut method to continuum reverberation mapping to extract the possible diffuse continuum light curves of six AGNs, using high-cadence, high-quality, and multiband observations. We find the existence of an outer component out of the accretion disk for each of the six AGNs in the Swift U band. Meanwhile, similar results can be derived with the JAVELIN Photometric Reverberation Mapping Model for four of them. The lags of the outer components are consistent with the predicted Balmer continuum lags, which are about half of the Hβ lag values. Our result directly reinforces the understanding that an outer component, especially the Balmer continuum in the rest-frame u/U band, can contribute significantly to the continuum reverberation lags of AGNs.

Intelligent competency mapping for improving knowledge management in consulting firms

AbstractThis study explores the application of competency mapping models, incorporating in knowledge management for consulting firms. It evaluates 15 different models, focusing on their suitability for consulting contexts based on data collection, advantages, risks, and limitations. The findings indicate that AI and ML-enhanced competency mapping models are particularly more effective in consulting firms. Finally, the article proposes three key applications of these models for improving knowledge management in consulting firms via empowering communities and collaboration.

Research on speculation method for compressor map of PG9351FA gas turbine

A compressor map is usually represented by a limited number of feature points to speculate the entire operating range. Also, accurate compressor map models can be obtained quickly by using the appropriate methods. In this paper, 9351FA gas turbine is used as the research object, and a set of targeted compressor map speculation scheme is proposed. At 15 data points, high-precision compressor maps are obtained based on BP neural network, and this method is suitable for a large number of data points. At 6 data points, compressor maps are obtained based on the parameter estimation method, and this method is suitable for a small number of data points. The mean square deviation of the compressor map obtained by the neural network is about 0.002, while the minimum mean square deviation of the results of the parameter estimation method is 0.026 and the maximum mean square deviation is 0.088. Since the corrected speed line of 106.4 is almost vertical, the maximum error mean squared deviation and the maximum standard deviation occur on this line. Both methods are suitable for different sample sizes, and the speculated compressor maps are more reliable. The combination of the two methods can provide a set of reference methods for compressor map speculation.

Vein pattern visualization for CSAM investigation using deep learning

ABSTRACT Child sexual abuse material (CSAM) has become one of the fastest-growing criminal industries linked to other illegal activities, such as human trafficking, underage prostitution, and the sextortion of minors. Although common biometric traits such as fingerprint, face, and palmprint are widely used in traditional identification systems, they are ineffective in investigating CSAM cases, as perpetrators often conceal their faces, and only partial non-facial skin may be visible. Vein pattern visualization has been introduced as a new tool in forensic investigations to overcome the limitations of current identification methods. Our early research has shown promising results in uncovering vein patterns from normal digital images using computer vision and deep learning techniques. For this research, we use a dataset of forearms and palm images collected from 301 participants in New Zealand. Utilizing a deep learning framework, we develop a collection of mapping models to reveal unique vein patterns from regular digital images. The results demonstrate our method’s efficiency in visualizing vein patterns, indicating nearly 95% similarity in the contrast values between reference NIR and generated images and 79% in the vein length of the generated and target images.

Data extension-based analysis and application selection of process-composition-properties of die casting aluminum alloy

This research aims to provide a solution to the scarcity and fragmentation of industrial data on die casting aluminum alloys. Quantifying the coupling between die casting process-composition-properties of aluminum alloys through small datasets, is a critical step in predicting part properties and optimizing process selection. To visualize the connections and discuss the effect of the interaction between different parameters on the property, data is fed into a self-organizing mapping model. Whereafter, an innovative data extension method is proposed to predict both yield and tensile strengths with more than 96% accuracy using a small data set. Moreover, two novel methods of multi-parameter combined range selection, the multi-objective optimization based on the agent model and the superimposition of the contour map, are guided by being informed in which region the mechanical properties fall. Finally, the feasibility of application range selection method is verified experimentally. Mapping based on data-driven process-composition-properties relationships and the free combination of application ranges are reliable theoretical solutions which are valuable for practical applications.

High-performance steering tracking control of open circuit variable-speed pump-controlled steering system for heavy-duty vehicles based on flow nonlinearity compensation

Heavy-duty vehicles with long bodies, a large number of axles and large loads are subject to increasingly high requirements for precise steering technology due to the increasing trend toward energy conservation and intelligent assisted driving as well as variable driving conditions. In this paper, an energy-efficient open circuit variable-speed pump-controlled steering system (OPCEHSSS) adapted for heavy loads is used, but its strong flow output nonlinearity and system nonlinear dynamic behavior greatly impede the steering performance. Therefore, in order to reduce the influence of the flow leakage of the fixed-displacement pump on the system and to ensure that the flow output of the system matches the control model, a mapping model based on the fitting of a two-layer neural network algorithm with a dynamic real-time compensation strategy (FNC) is proposed. In addition, considering the strong robustness of the system even under parameter uncertainty and unknown disturbance, a complex nonlinear mathematical model is established based on OPCEHSSS physical characteristics, and a dual-objective control strategy of steering angle and pressure based on sliding mode control (SMC) is proposed. However, in order to reduce the influence of high-order switching discontinuity on the steering and ensure the fast convergence of the control system, a fast super twisting algorithm (STA) based on double saturation function of the boundary layer is proposed. The experimental results show that the three different controllers can effectively reduce the steering angle error after the introduction of FNC. And in the case of a single axle loaded with 6 tons, the improved new FNC+STA integrated dual-objective control strategy improves the accuracy by 53.16% compared with PID and 40.67% compared with SMC. The steady-state error is maintained within 0.9°, realizing the high-performance steering tracking control of OPCEHSSS for heavy vehicles.

A novel hexagonal grid map model and regenerated heuristic factor based strategy for intelligent manufacturing system’s AGV path planning problem solving

In modern industry, flexible manufacturing systems play an indispensable role, and the precision and connectivity of map modeling are crucial for improving the efficiency and flexibility of AGV (Automated Guided Vehicle) transportation systems. Facing challenges such as spatial constraints, equipment failures, and unexpected task changes in real manufacturing environments, this paper proposes an innovative AGV path planning method based on hexagonal grid map modeling. This method significantly enhances the connectivity, sampling frequency, and safety of AGV path planning by replacing traditional square grids with hexagonal ones. Additionally, this study employs an improved ant colony algorithm combined with the hexagonal grid map for AGV path planning. The algorithm incorporates heuristic factors to effectively avoid local optima. Furthermore, by dividing the ant colony into odd and even groups and implementing a bidirectional search strategy, the comprehensive exploration capability of the algorithm is further enhanced. Experimental results show that compared to traditional square grids, the hexagonal grid reduces the optimal path length by 11.1%, and the application of heuristic factors further shortens the path length by 18.6%. The bidirectional search strategy also significantly reduces the number of iterations required by the algorithm, with a maximum reduction of 46.46%. In summary, this paper provides a new, efficient, stable, and energy-saving solution for AGV path planning systems, with thorough optimizations and improvements made in every aspect from map modeling to path planning algorithms.

Structural deformation and clamping force monitoring of reconfigurable tooling motivated by strain data in aircraft assembly

The quality of aircraft assembly is mainly guaranteed by toolings which are vital to the geometrical accuracy and service performance of aviation products. In this research, a real-time monitoring system that determines the structural deformation and clamping force of reconfigurable toolings using strain data is developed to perceive the service state of the toolings. By laying fiber Bragg gratings on positioning beam and baseplate of the reconfigurable tooling, strain data of the tooling structures are gauged and transferred to curvatures. The beam and baseplate are modeled as one and two-dimensional objects respectively and shape reconstruction algorithms are established to obtain their deflection curve and surface using curvature information. Distribution of fibers is optimized to minimize the conversion error from strain to curvature. An estimation that reveals the mathematical relationship between the shape reconstruction error and measurement interval is implemented, and a mapping model from strains to clamping force of the beam is established. These algorithms are integrated into the self-developed monitoring software and undergo simulating and experimental tests. The maximum relative errors of deformation and force are 4.53% and 4.12% respectively in simulation, and 9.21% and 7.29% individually in experiment, which validates the efficiencies of the method. Tests of the monitoring system suggest that it can provide a timely and accurate sensing of the deformation and force of the tooling.

A non-degenerate n-dimensional integer domain chaotic map model with application to PRNG

To address the limitations of existing chaotic maps, we proposed a non-degenerate n-dimensional (n ≥ 2) integer domain chaotic map (nD-IDCM) model that can construct any non-degenerate n-dimensional integer domain chaotic maps. Moreover, we analyzed its chaotic behavior through Lyapunov exponent, and found that the nD-IDCM generates chaotic sequences in the integer domain, which effectively resolves the issue of finite precision effect when implementing existing chaotic maps on computers or digital devices. To verify the effectiveness of nD-IDCM, we presented two instances to demonstrate how the positive Lyapunov exponents can be regulated by manipulating the parameter matrix. Subsequently, we have scrutinized their dynamical behavior using Kolmogorov entropy, sample entropy, correlation dimension and randomness testing via TestU01. Finally, to assess the feasibility of nD-IDCM, we devised a keyed pseudo random number generator (PRNG) based on a 3D-IDCM that can ensure superior randomness and unpredictability. Experimental results indicated that integer domain chaotic maps constructed using nD-IDCM have desirable Lyapunov exponents and exhibit ergodicity within a sufficient larger chaotic range.

VTT-LLM: Advancing Vulnerability-to-Tactic-and-Technique Mapping through Fine-Tuning of Large Language Model

Vulnerabilities are often accompanied by cyberattacks. CVE is the largest repository of open vulnerabilities, which keeps expanding. ATT&CK models known multi-step attacks both tactically and technically and remains up to date. It is valuable to correlate the vulnerability in CVE with the corresponding tactic and technique of ATT&CK which exploit the vulnerability, for active defense. Mappings manually is not only time-consuming but also difficult to keep up-to-date. Existing language-based automated mapping methods do not utilize the information associated with attack behaviors outside of CVE and ATT&CK and are therefore ineffective. In this paper, we propose a novel framework named VTT-LLM for mapping Vulnerabilities to Tactics and Techniques based on Large Language Models, which consists of a generation model and a mapping model. In order to generate fine-tuning instructions for LLM, we create a template to extract knowledge of CWE (a standardized list of common weaknesses) and CAPEC (a standardized list of common attack patterns). We train the generation model of VTT-LLM by fine-tuning the LLM according to the above instructions. The generation model correlates vulnerability and attack through their descriptions. The mapping model transforms the descriptions of ATT&CK tactics and techniques into vectors through text embedding and further associates them with attacks through semantic matching. By leveraging the knowledge of CWE and CAPEC, VTT-LLM can eventually automate the process of linking vulnerabilities in CVE to the attack techniques and tactics of ATT&CK. Experiments on the latest public dataset, ChatGPT-VDMEval, show the effectiveness of VTT-LLM with an accuracy of 85.18%, which is 13.69% and 54.42% higher than the existing CVET and ChatGPT-based methods, respectively. In addition, compared to fine-tuning without outside knowledge, the accuracy of VTT-LLM with chain fine-tuning is 9.24% higher on average across different LLMs.

Characterizing land use/land cover change dynamics by an enhanced random forest machine learning model: a Google Earth Engine implementation

Land use and land cover (LULC) analysis is crucial for understanding societal development and assessing changes during the Anthropocene era. Conventional LULC mapping faces challenges in capturing changes under cloud cover and limited ground truth data. To enhance the accuracy and comprehensiveness of the descriptions of LULC changes, this investigation employed a combination of advanced techniques. Specifically, multitemporal 30 m resolution Landsat-8 satellite imagery was utilized, in addition to the cloud computing capabilities of the Google Earth Engine (GEE) platform. Additionally, the study incorporated the random forest (RF) algorithm. This study aimed to generate continuous LULC maps for 2014 and 2020 for the Shrirampur area of Maharashtra, India. A novel multiple composite RF approach based on LULC classification was utilized to generate the final LULC classification maps utilizing the RF-50 and RF-100 tree models. Both RF models utilized seven input bands (B1 to B7) as the dataset for LULC classification. By incorporating these bands, the models were able to influence the spectral information captured by each band to classify the LULC categories accurately. The inclusion of multiple bands enhanced the discrimination capabilities of the classifiers, increasing the comprehensiveness of the assessment of the LULC classes. The analysis indicated that RF-100 exhibited higher training and validation/testing accuracy for 2014 and 2020 (0.99 and 0.79/0.80, respectively). The study further revealed that agricultural land, built-up land, and water bodies have changed adequately and have undergone substantial variation among the LULC classes in the study area. Overall, this research provides novel insights into the application of machine learning (ML) models for LULC mapping and emphasizes the importance of selecting the optimal tree combination for enhancing the accuracy and reliability of LULC maps based on the GEE and different RF tree models. The present investigation further enabled the interpretation of pixel-level LULC interactions while improving image classification accuracy and suggested the best models for the classification of LULC maps through the identification of changes in LULC classes.

Utilization of Frequency Ratio and Logistic Regression Model for Landslide Susceptibility Mapping in Bogor Area

Java Island holds the highest record of landslide events in Indonesia. In 2021, the Bogor area, consisting of the city and regency of Bogor, recorded the highest number of landslides. These events further impact the fatalities, damage, and loss to society. Landslide mitigation should be considered to reduce the risk caused by landslide hazards. In this regard, a landslide susceptibility analysis is one of the fundamental steps in mitigation measures that can support policymakers in response to landslide disaster risk reduction. The location of landslide possibilities can be identified by mapping landslide susceptibility. Therefore, this study aims to produce a landslide susceptibility map (LSM) using a statistical frequency ratio method and logistic regression. The number of landslide inventories used in the model is about 822 events. To apply the model, the present study evaluates 13 influencing factors consisting of elevation, slope angle, slope aspect, slope curvature, Topographic Wetness Index (TWI), distance to river, lithological, distance to fault, soil type, annual rainfall, Normalized Difference Vegetation Index (NDVI), land use land cover (LULC), and road distance. The model performance is further evaluated using Area Under the ROC Curve (AUC). The frequency ratio (FR) and logistic regression (LR) models produce satisfactory results and have high predictions of future landslide occurrences with a score of 0.8317 and 0.8817, respectively.