Multi-source Data Integration Research Articles

Unraveling the Core Components and Critical Targets of Houttuynia cordata Thunb. in Treating Non-small Cell Lung Cancer through Network Pharmacology and Multi-omics Analysis.

This study aimed to preliminary explore the molecular mechanisms of Houttuynia cordata Thunb. (H. cordata; Saururaceae) in treating non-small cell lung cancer (NSCLC), with the goal of screening drug potential targets for clinical drug development. This study employed a multi-omics and multi-source data integration approach to identify potential therapeutic targets of H. cordata against NSCLC from the TCMSP database, GEO database, BioGPS database, Metascape database, and others. Meanwhile, target localization was performed, and its possible mechanisms of action were predicted. Furthermore, dynamics simulations and molecular docking were used for verification. Multiomics analysis was used to confirm the selected key genes' efficacy in treating NSCLC. A total of 31 potential therapeutic targets, 8 key genes, and 5 core components of H. cordata against NSCLC were screened out. These potential therapeutic targets played a therapeutic role mainly by regulating lipid and atherosclerosis, the TNF signaling pathway, the IL-17 signaling pathway, and others. Molecular docking indicated a stable combination between MMP9 and quercetin. Finally, through multi-omics analysis, it was found that the expression of some key genes was closely related not only to the progression and prognosis of NSCLC but also to the level of immune infiltration. Through comprehensive network pharmacology and multi-omics analysis, this study predicts that the core components of H. cordata play a role in treating NSCLC by regulating lipid and atherosclerosis, as well as the TNF signaling pathway. Among them, the anti-NSCLC activity of isoramanone is reported for the first time.

Innovative Data Visualization for Business Intelligence: Enabling Ad-hoc Querying and Predictive Analysis

This comprehensive analysis explores the transformative impact of innovative Business Intelligence (BI) solutions that combine ad-hoc querying capabilities with advanced predictive analytics. The article examines the limitations of traditional BI tools and highlights the critical need for more flexible and powerful solutions in today's fast-paced business environment. It delves into the key features of modern BI solutions, including interactive visualizations, natural language processing, automated data preparation, multi-source data integration, and collaborative features. The study also investigates the far-reaching effects of these advanced BI tools on various aspects of business operations and strategy, such as operational efficiency, strategic planning, customer-centric approaches, risk mitigation, and innovation. Supported by extensive industry data and research, this analysis demonstrates how these cutting-edge BI solutions are reshaping the way organizations interact with data, make decisions, and compete in an increasingly data-driven marketplace.

Short-Term Traffic Speed Prediction Based on AGC-LSTM with Multi-Source Data Integration

Short-Term Traffic Speed Prediction Based on AGC-LSTM with Multi-Source Data Integration

Modeling transient mixed flows in sewer systems with data fusion via physics-informed machine learning

Transitions between free-surface and pressurized flows, known as transient mixed flows, have posed significant challenges in urban drainage systems (UDS), e.g., pipe bursts, road collapses, and geysers. However, traditional mechanistic modeling for mixed flows is challenged by the difficult integration of multi-source data, complex equation forms for the discovery of dynamic processes, and high computational demands. In response, we proposed a data-driven model, TMF-PINN, which utilizes a Physics-Informed Neural Network (PINN) to simulate and invert Transient Mixed Flow (TMF) in sewer networks. This model integrates experimental data, simulation results and Partial Differential Equations (PDEs) into its loss function, leveraging the extensive data available in smart urban water systems. A status factor (α) has been introduced to seamlessly link open channel and pressurized flow dynamics, facilitating rapid adjustments in wave speed. On this basis, Fourier feature extraction and quadratic neural networks have been employed to capture complex dynamic processes featuring high-frequency. Validation through three classical cases using the Storm Water Management Model (SWMM) and comparisons with finite volume Harten-Lax-van Leer (HLL) solver reveal that the proposed model circumvents the constraints of spatiotemporal resolution, yielding accurate flow field predictions.

Enhancing Heart Disease Prediction with Federated Learning and Blockchain Integration

Federated learning offers a framework for developing local models across institutions while safeguarding sensitive data. This paper introduces a novel approach for heart disease prediction using the TabNet model, which combines the strengths of tree-based models and deep neural networks. Our study utilizes the Comprehensive Heart Disease and UCI Heart Disease datasets, leveraging TabNet’s architecture to enhance data handling in federated environments. Horizontal federated learning was implemented using the federated averaging algorithm to securely aggregate model updates across participants. Blockchain technology was integrated to enhance transparency and accountability, with smart contracts automating governance. The experimental results demonstrate that TabNet achieved the highest balanced metrics score of 1.594 after 50 epochs, with an accuracy of 0.822 and an epsilon value of 6.855, effectively balancing privacy and performance. The model also demonstrated strong accuracy with only 10 iterations on aggregated data, highlighting the benefits of multi-source data integration. This work presents a scalable, privacy-preserving solution for heart disease prediction, combining TabNet and blockchain to address key healthcare challenges while ensuring data integrity.

Evaluation Method of Teaching Effect of Environmental Art and Design in Universities Based on Multi-Source Data Fusion Technology

The evaluation of teaching reform satisfaction is crucial for improving teaching quality. In the evaluation of satisfaction with teaching reforms in environmental art and design, issues such as the low reliability of evaluation indicator data can lead to unsatisfactory results. To address this, this paper proposes a new method for evaluating satisfaction with teaching reforms in environmental art and design based on multi-source data integration. By establishing principles for constructing a multi-source indicator system for evaluating satisfaction with teaching reforms in environmental art and design, a four-level multi-source indicator system was determined. This system mainly includes one first-level indicator, four second-level satisfaction evaluation indicators, and several third- and fourth-level evaluation indicators. The construction of the multi-source indicator system for evaluating satisfaction with teaching reforms in environmental art and design was completed. Using a four-part graph model and entropy weight calculation, the importance of the evaluation indicators for satisfaction with teaching reforms in environmental art and design was classified. The expert scoring method was introduced to correct the multi-source indicator data for evaluating satisfaction with teaching reforms in environmental art and design. Based on standardized indicator data, a penalty rule was added to the loss function of the multiple linear regression model to remove data noise, achieving pre-processing of the multi-source indicators for evaluating satisfaction with teaching reforms in environmental art and design. After completing the above process, first collect multi-source data from questionnaire surveys, student feedback, etc., and preprocess and map it to a unified ontology class. Subsequently, data features are extracted through non-negative matrix factorization, and the similarity between object types is calculated. Finally, based on the importance, redundancy, and interaction of data sources, an integrated quality model is designed to effectively integrate multiple sources of data, construct a satisfaction evaluation model, and output the score of satisfaction evaluation. The experimental results show that the proposed method has the highest satisfaction index, reaching about 0.99, indicating a high degree of recognition for the reform; when the amount of integrated data changes, the proposed method maintains the best integratison reliability. Even with an increase in data volume, its reliability only slightly decreases and stabilizes at a high level; meanwhile, the average absolute error of the proposed method is the lowest, consistently below 2.5%, indicating a high level of stability and accuracy in its evaluation results. The above results confirm that the proposed method can effectively evaluate the satisfaction of environmental art design teaching reform and improve teaching quality.

Implementing data governance in financial systems: Strategies for ensuring compliance and security in multi-source data integration projects

This study explores the complexities of implementing data governance within financial systems, focusing on strategies to ensure compliance and security in multi-source data integration projects. The primary aim was to identify challenges and establish a comprehensive framework to enhance data integrity, confidentiality, and regulatory adherence in the financial sector. Using a mixed-methods approach, the research included an extensive literature review, case studies, and expert interviews to gain insights into current practices, challenges, and emerging trends in data governance. Key findings reveal that while multi-source data integration offers significant opportunities for enhanced decision-making, it also presents substantial risks, including data breaches and regulatory non-compliance. The study underscores the pivotal role of data governance frameworks in mitigating these risks through the establishment of clear data policies, the implementation of robust cybersecurity measures, and the promotion of a compliance-focused organizational culture. Additionally, the analysis highlights the necessity for harmonized global data privacy laws to address the diverse regulatory environments that financial institutions operate within. The research concludes that effective data governance is crucial for financial institutions to safeguard their data assets, ensure regulatory compliance, and maintain stakeholder trust. Recommendations include the adoption of advanced technologies like artificial intelligence and blockchain to enhance data governance capabilities, the provision of ongoing employee training to foster a culture of compliance, and the implementation of dynamic compliance monitoring systems to keep pace with evolving regulatory demands. By embracing these strategies, financial institutions can develop a resilient data governance framework that not only ensures compliance and security but also drives operational excellence, competitive advantage, and sustained trust in the increasingly digital financial landscape. Keywords: Data Governance, Financial Systems, Multi-Source Data Integration, Regulatory Compliance, Cybersecurity, Data Privacy.

Budget impact analysis of high-priced orphan medicinal products intended for the treatment of rare diseases in China: evidence from a densely populated metropolis of Chengdu

BackgroundAn increasing number of orphan medicinal products (OMPs) are being included in social health insurance schemes, significantly improving access to medicines for patients with rare diseases. However, high-priced OMPs are still not covered, primarily due to health equity controversies and inadequate data systems required for economic evaluation. The aim of this study was to estimate the burden of drug expenditures and the size of the reimbursement budget required for high-priced OMPs from the perspectives of society and healthcare payers.MethodsThe study performed a budget impact analysis using data from multiple sources to estimate the reimbursement budget for high-priced OMPs in Chengdu, a densely populated metropolis in China. The budget analysis consisted of three main elements: the number of patients, the price of drugs, and the simulated policy scenario. By adjusting the combinations of these elements, the budget fluctuations for payers were estimated. Furthermore, the study predicted the budget trend for the next three years to validate its sustainability.ResultsThe analysis indicated that 98 rare disease patients in Chengdu required high-priced OMPs in 2019. This suggested a projected budget of CNY 179 million for these patients without reimbursement policies, from a societal perspective. Under six assumed policy scenarios, this budget ranged from CNY 32 million to CNY 156 million. Over the next three years, the annual budget was estimated to range from CNY 200 million to CNY 1.303 billion.ConclusionIntegration of multi-source data helps to obtain more scientifically reliable results on budget impacts. The study found that the budgetary impacts of high-priced OMPs on society and payers are relatively limited. Health policymakers can choose appropriate reimbursement strategies based on financial affordability among a diverse mix of elements. The results of related studies provided insights for optimizing the allocation of health resources and improving patient access to medications.

Wetland mapping in the Liaohe River Estuary using multi-source remote sensing image feature selection

ABSTRACT As a crucial node in the East Asia–Australasia migratory bird network, the Liaohe River Estuary Wetland (LREW) contributes significantly to coastal ecological balance and biodiversity. Accurate and timely mapping of the LREW is essential for monitoring ecological changes and understanding shifts in the wetland’s ecosystem structure and functions. In this study, an innovative approach that integrates radar (Sentinel-1), optical (Sentinel-2), and DEM data on Google Earth Engine (GEE) is proposed to achieve a more optimized and accurate wetland mapping. Recursive Feature Elimination (RFE) is employed to improve feature collection, facilitating the construction of an optimal feature set for Random Forest (RF) classification to extract detailed LREW information. Mapping conducted from 2017 to 2023 using the proposed approach demonstrates its effectiveness and stability in mapping the LREW, achieving overall accuracies ranging from 89.84% to 93.73% and Kappa from 0.85 to 0.91. Compared to single-source methods, the integration of multi-source data substantially enhances mapping accuracy, while RF-RFE effectively eliminates redundant features, further refining classification precision. Texture features (Sum Average, SAVG) and the Water Index 2019 (WI2019) were found to significantly influence wetland mapping. The importance of different feature types is ranked as follows: spectral features > vegetation/water body index > PCA features > SAR texture features > red-edge index > other spectral index > spectral texture features > backscatter coefficient > H-Alpha. During the study period, the wetland area increased from 849.60 km2 to 979.49 km2. This expansion was caused primarily by wetland protection policies that led to the dismantling of coastal aquaculture ponds, resulting in a reduction of 90.28 km2 in waterbodies over 7 years, with many areas transitioning to tidal flats and Suaeda salsa habitats. In conclusion, the proposed method is effective in mapping the LRE wetlands and provides reliable data support for wetland ecological protection and restoration.

Interdisciplinary perspective on architectural programming: current status and future directions

PurposeArchitectural programming, as a critical phase in construction projects, has been widely recognized for its importance and advantages throughout the construction process. With the rapid development of the socioeconomic landscape, architectural programming has garnered increasing attention from various other disciplines, becoming a key trend in interdisciplinary collaboration. This study aims to provide a comprehensive understanding of the current status and future directions of architectural programming from an interdisciplinary perspective through scientometric analysis and systematic review.Design/methodology/approachThis study first collected English journal articles on architectural programming published between 1975 and 2024 from the Web of Science and Scopus databases. After an initial screening of titles and abstracts, 515 articles were selected for scientometric analysis to reveal the current state and advantages of architectural programming research in multidisciplinary fields. Subsequently, a second screening of full-text articles identified 75 journal articles for systematic review, focusing on research topics and challenges in interdisciplinary applications.FindingsThe study reveals an exponential increase in the number of papers related to architectural programming between 1975 and 2024, particularly in the last decade. Six key research topics of architectural programming in multidisciplinary fields were identified: (1) performance optimization and evaluation, (2) digitalization and automation development, (3) project management and decision support, (4) improvement of human and social welfare, (5) sustainable resources and environment and (6) educational practices of architectural programming. Additionally, the study identified the main challenges in the interdisciplinary application of architectural programming, including (1) incompatibility among disciplines, (2) limitations of data and methodologies and (3) insufficient social engagement. To address these challenges, three potential future directions were proposed: (1) establishing interdisciplinary teams and platforms, (2) enhancing multi-source data integration and digital transformation and (3) improving governance mechanisms and educational training.Originality/valueBy combining quantitative and qualitative methods, this study provides a comprehensive review of architectural programming research and applications in multidisciplinary fields, offering theoretical foundations and practical references for the future development of architectural programming. This review not only aids in understanding the overall status of current architectural programming research but also offers valuable insights and recommendations for future research directions and practical applications.

Toward Ensuring Data Quality in Multi-Site Cancer Imaging Repositories

Cancer remains a major global health challenge, affecting diverse populations across various demographics. Integrating Artificial Intelligence (AI) into clinical settings to enhance disease outcome prediction presents notable challenges. This study addresses the limitations of AI-driven cancer care due to low-quality datasets by proposing a comprehensive three-step methodology to ensure high data quality in large-scale cancer-imaging repositories. Our methodology encompasses (i) developing a Data Quality Conceptual Model with specific metrics for assessment, (ii) creating a detailed data-collection protocol and a rule set to ensure data homogeneity and proper integration of multi-source data, and (iii) implementing a Data Integration Quality Check Tool (DIQCT) to verify adherence to quality requirements and suggest corrective actions. These steps are designed to mitigate biases, enhance data integrity, and ensure that integrated data meets high-quality standards. We applied this methodology within the INCISIVE project, an EU-funded initiative aimed at a pan-European cancer-imaging repository. The use-case demonstrated the effectiveness of our approach in defining quality rules and assessing compliance, resulting in improved data integration and higher data quality. The proposed methodology can assist the deployment of big data centralized or distributed repositories with data from diverse data sources, thus facilitating the development of AI tools.

Improved 3D characterization of in-situ soil desiccation cracking by multi-source data integration

Desiccation cracking is a common and natural phenomenon under a drought climate. The geometric and morphologic characteristics of the crack pattern are critical to understanding the response of soil mechanical and hydraulic properties to drought climate. It is always a big challenge to obtain the refined geometric structure of the in-situ soil desiccation crack network. This study proposes an integrated method named ERT+ for determining the three-dimensional geometry of in-situ soil desiccation crack networks by integrating multi-source data from electrical resistivity tomography (ERT), surface image analysis, and depth investigation. The proposed method was applied to three in-situ expansive soil sites with different crack geometries and soil properties. The results showed that the joint application of ERT with other investigations reduced the ambiguity of interpreting each technique independently. The crack network model characterized the three-dimensional geometric structure of the desiccation crack accurately and quantitatively, verifying the effectiveness and feasibility of the ERT+ method. Field and laboratory experiments showed that heterogeneity in soil properties resulted in different cracking morphologies (width, depth, and width-depth ratio). The crack geometric data suggested that the width-depth ratio of the crack network was related to the cracking modes and the soil properties, indicating the non-homology of different crack networks. In addition, the correction gradients calculated from the ERT+ method also varied with the cracking modes and soil properties, further suggesting the reliability and prospect of the proposed method.

Mapping the Time-Series of Essential Urban Land Use Categories in China: A Multi-Source Data Integration Approach

Accurate, detailed, and long-term urban land use mapping is crucial for urban planning, environmental assessment, and health evaluation. Despite previous efforts, mapping essential urban land use categories (EULUCs) across multiple periods remains challenging, primarily due to the scarcity of enduring consistent socio-geographical data, such as the widely used Point of Interest (POI) data. Addressing this issue, this study presents an experimental method for mapping the time-series of EULUCs in Dalian city, China, utilizing Local Climate Zone (LCZ) data as a substitute for POI data. Leveraging multi-source geospatial big data and the random forest classifier, we delineate urban land use distributions at the parcel level for the years 2000, 2005, 2010, 2015, 2018, and 2020. The results demonstrate that the generated EULUC maps achieve promising classification performance, with an overall accuracy of 78% for Level 1 and 71% for Level 2 categories. Features derived from nighttime light data, LCZ, Sentinel-2 satellite imagery, and topographic data play leading roles in our land use classification process. The importance of LCZ data is second only to nighttime light data, achieving comparable classification accuracy to that when using POI data. Our subsequent correlation analysis reveals a significant correlation between POI and LCZ data (p = 0.4), which validates the rationale of the proposed framework. These findings offer valuable insights for long-term urban land use mapping, which can facilitate effective urban planning and resource management in the near future.

Multi-Source Data Integration for Navigation in GPS-Denied Autonomous Driving Environments

Multi-Source Data Integration for Navigation in GPS-Denied Autonomous Driving Environments

TEMDI: A Temporal Enhanced Multisource Data Integration model for accurate PM2.5 concentration forecasting

Accurate forecasting of PM2.5 concentration is crucial for implementing effective protective measures and mitigating the adverse health impacts of air pollution. To address the complex spatial propagation dynamics and temporal variations of PM2.5, we developed the Temporal Enhanced Multisource Data Integration (TEMDI) model. This innovative approach combines spatial modeling by a Graph Neural Network (GNN) to capture the intricate spatial propagation patterns based on multi-source data fusion, and a novel Time Series Enhancement (TSE) module that includes Ensemble Empirical Mode Decomposition (EEMD), Gated Recurrent Units (GRUs), and a self-attention mechanism to adequately manage the time series’ short-term and long-term trends. Our results demonstrate TEMDI’s superior performance, achieving exceptionally high Probability of Detection (POD) rates of 96.15%, 80.28%, and 71.86% for forecast horizons of 3, 36, and 72 h, respectively. Furthermore, our feature importance analysis reveals that multi-scale features extracted by the EEMD component become increasingly crucial as the prediction horizon extends. The TEMDI model’s ability to provide accurate, reliable PM2.5 forecasts and its enhanced interpretability position it as a valuable tool for guiding environmental policy and management decisions to safeguard public health.

Landslide Hazard Prediction Based on Small Baseline Subset–Interferometric Synthetic-Aperture Radar Technology Combined with Land-Use Dynamic Change and Hydrological Conditions (Sichuan, China)

Le’an Town, located in the southwest of Qingchuan County, Guangyuan City, Sichuan Province, boasts a unique geographical position. The town’s terrain is complex, and its geological environment is fragile. Multiple phases of tectonic movements have resulted in numerous cracks and faults, making the area prone to landslides, debris flows, and other disasters. Additionally, heavy rainfall and fluctuating groundwater levels further exacerbate the instability of the mountains. Human activities, such as overdevelopment and deforestation, have significantly increased the risk of geological disasters. Currently, the methods for landslide prediction in Le’an Town are limited; traditional techniques cannot provide precise forecasts, and the study area is largely covered by tall vegetation. Therefore, this paper proposes a method that combines SBAS-InSAR technology with dynamic changes in land use and hydrological conditions. SBAS-InSAR technology is used to obtain surface deformation information, while land-use changes and hydrological condition data are incorporated to analyze the dynamic characteristics and potential influencing factors of landslide areas. The innovation of this method lies in its high-precision surface deformation monitoring capability and the integration of multi-source data, which can more comprehensively reveal the geological environmental characteristics of the study area, thereby achieving accurate predictions of landslide development. The study results indicate that the annual subsidence rate in most deformation areas of Le’an Town ranges from −10 to 0 mm, indicating slow subsidence. In some areas, the subsidence rate exceeds −50 mm per year, showing significant slope aspect differences, reflecting the combined effects of geological structures, climatic conditions, and human activities. It is evident that land-use changes and hydrological conditions have a significant impact on the occurrence and development of landslides. Therefore, by utilizing SBAS-InSAR technology and cross-verifying it with other techniques, the consistency of identified landslide deformation areas can be enhanced, thereby improving results. This method provides a scientific basis for the monitoring and early warning of landslide disasters and has important practical application value.

Bibliometric Analysis on the Research of Geoscience Knowledge Graph (GeoKG) from 2012 to 2023

The geoscience knowledge graph (GeoKG) has gained worldwide attention due to its ability in the formal representation of spatiotemporal features and relationships of geoscience knowledge. Currently, a quantitative review of the state and trends in GeoKG is still scarce. Thus, a bibliometric analysis was performed in this study to fill the gap. Specifically, based on 294 research articles published from 2012 to 2023, we conducted analyses in terms of the (1) trends in publications and citations; (2) identification of the major papers, sources, researchers, institutions, and countries; (3) scientific collaboration analysis; and (4) detection of major research topics and tendencies. The results revealed that the interest in GeoKG research has rapidly increased after 2019 and is continually expanding. China is the most productive country in this field. Co-authorship analysis shows that inter-national and inter-institutional collaboration should be reinforced. Keyword analysis indicated that geoscience knowledge representation, information extraction, GeoKG construction, and GeoKG-based multi-source data integration were current hotspots. In addition, several important but currently neglected issues, such as the integration of Large Language Models, are highlighted. The findings of this review provide a systematic overview of the development of GeoKG and provide a valuable reference for future research.

Investigating urban mobility through multi-source public transportation data: A multiplex network perspective

The integration of multi-source and diverse spatio-temporal travel data provides a comprehensive insight into urban mobility. Using data from Shenzhen's public transportation system, this study presents an analytical framework based on multiplex networks to examine variations in multi-mode public transportation usage (metro, bus, taxi, and shared bike) and their correlation with the built environment. This framework encompasses the analysis of network topological characteristics, centrality, and communities. The examination of network topological characteristics reveals that the multiplex transportation network exhibits high global accessibility and local connectivity. Network centrality analysis, focusing on weighted outdegree centrality, captures the patterns of public transportation ridership. Centrality modeling, employing the light gradient boosting machine, demonstrates a nonlinear relationship between ridership and the built environment. Factors including population density, residential land use percentage, entertainment service density, restaurant density, and metro station density consistently exhibit positive correlations with ridership across different times of the day. The community structure analysis, using consensus community detection, indicates that distinct urban areas exhibit clustering behavior based on public transportation demand patterns, forming distinct communities that closely align with the functional zoning of urban planning. These findings could provide valuable insights for the strategic planning of transportation services and the built environment.

Research on Big Data Intelligent System for Fault Diagnosis of Computer Aided Ship Power System

This paper aims to build a new fault diagnosis system based on big data and artificial intelligence technology. This study first expounds the application value and prospect of big data technology and intelligent algorithm in the fault diagnosis of ship power system, and realizes the effective integration and mining of massive, multi-source and heterogeneous ship power system operation data through in-depth analysis of the characteristics of ship power system operation data. Secondly, in view of the nonlinear and time-varying characteristics of ship power system faults, this study designs and implements a fault diagnosis model that integrates intelligent algorithms such as machine learning and deep learning. The model can automatically extract key fault features, realize early warning and accurate identification of potential faults, and improve the accuracy and response speed of fault diagnosis through continuous learning and optimization. Finally, this study applies the big data intelligent system for fault diagnosis of computer-aided ship power system to a practical case to verify its effectiveness and practicability in improving fault diagnosis efficiency, reducing maintenance costs, and ensuring navigation safety. The research results have important theoretical significance and practical value for improving the level of ship operation and maintenance management and ensuring the stable operation of ship power system.

Application of Integrated Multi-source Data in Landslide Vulnerability Assessment

This paper employs GIS technology and multi-source data integration methods to assess landslide vulnerability in a city in the southwestern region. The study combines the Analytic Hierarchy Process (AHP) and machine learning techniques, utilizing field survey data and remote sensing information to perform a systematic quantitative analysis of the hazard and vulnerability of landslides. A comprehensive evaluation model was established, assessing the risk levels for populations, buildings, and infrastructure, thereby providing effective decision support for mitigating landslide risks.