Data Integration Framework Research Articles

Real-Time Risk Assessment in Insurance: A Deep Learning Approach to Predictive Modeling

The insurance industry is experiencing a paradigm shift driven by the integration of big data analytics and machine learning technologies. This article presents a comprehensive framework for implementing advanced analytics in insurance operations, focusing on risk assessment optimization and predictive modeling applications. Building upon established actuarial methods, this article demonstrates how modern machine learning algorithms and real-time data processing enhance underwriting accuracy and claim processing efficiency. This article examines the integration of multiple data sources, including IoT sensors, telematics, and external databases, highlighting their collective impact on risk assessment precision. This analysis indicates significant improvements in underwriting accuracy and claims processing speed through the implementation of automated analytics workflows. This article also addresses critical challenges in data quality, regulatory compliance, and integration complexity, providing strategic recommendations for insurers pursuing digital transformation. This article contributes to the growing body of knowledge in insurance analytics by establishing a scalable framework for big data integration while maintaining regulatory compliance and operational efficiency

MOHGCN: A trustworthy multi-omics data integration framework based on specificity-aware heterogeneous graph convolutional neural networks for disease diagnosis

With the advancement of cutting-edge sequencing methodologies, the integration of multi-omics data provides invaluable opportunities for researchers to study complex diseases from a molecular perspective while at the same time being challenged by the deployment of safety-critical applications such as computer-aided diagnostics. However, existing methods in multi-omics data integration primarily explore interactions between omics or samples, neglecting high-order interaction information among biomolecules specific to certain diseases. In this work, we propose MOHGCN, a trustworthy multi-omics data integration framework based on specificity-aware heterogeneous graph convolutional neural networks for disease diagnosis, aiming to maximize the utilization of biomolecular interactions in patients with specific diseases for precise diagnosis to enhance the model’s credibility. In the approach, we constructed a heterogeneous graph of samples and genes and devised the HGCN graph convolution model specifically tailored to the sample–gene heterogeneous graph. Concurrently, techniques such as trustworthy attention weights and self-attention mechanisms were incorporated to unveil relationships between different omics, facilitating the efficient integration of multi-omics data. Through comprehensive experimentation on four publicly available multi-omics medical datasets, our proposed framework consistently demonstrates superior performance across various classification tasks. Simultaneously, the experimental results substantiate the model’s effectiveness in extracting features from multi-omics data and unveiling latent associations among different omics.

Ontology-based Data Integration Framework for Smart Pig Farms

Semantic web technology is known to enhance data for the connection of information using a network that can be easily read by machines. This work applies an ontology to integrate data involved in swine farm management towards the development of smart farm for swine. To realize environmental data, IoT devices including sensors, camera, and RFID readers were deployed in a pig farm. Despite differences in data format from different sources, an ontology was crafted to provide semantic annotations that could unify the data and enhance them with tangible relationships based on the domain knowledge. The core concepts of this ontology were sensing data with temporal information and identifiable data to represent relationship between real-time environmental features and the pigs within the pen to improve pig management. The semantically enhanced data can thus be applied with semantic-based queries and inference reasoner. Smart services including monitoring, decision support, alerting, and automatic executing based on the deployed IoT devices are thus enabled to support farm managements towards smart farm for swine. From the results, we found that the integrated sensing information from sensors and knowledge given in the developed ontology with inference rules assists the task of interpretation of monitored information and decision-making to improve swine farm management. The data gathered could be extended by combining two or more factors such as temperature and humidity into heat index using knowledge inference to precisely understand environmental information and activate the relevant machine automatically.

Performance of an AI-powered visualization software platform for precision surgery in breast cancer patients

Surgery remains the primary treatment modality in the management of early-stage invasive breast cancer. Artificial intelligence (AI)-powered visualization platforms offer the compelling potential to aid surgeons in evaluating the tumor’s location and morphology within the breast and accordingly optimize their surgical approach. We sought to validate an AI platform that employs dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to render three-dimensional (3D) representations of the tumor and 5 additional chest tissues, offering clear visualizations as well as functionalities for quantifying tumor morphology, tumor-to-landmark structure distances, excision volumes, and approximate surgical margins. This retrospective study assessed the visualization platform’s performance on 100 cases with ground-truth labels vetted by 2 breast-specialized radiologists. We assessed features including automatic AI-generated clinical metrics (e.g., tumor dimensions) as well as visualization tools including convex hulls at desired margins around the tumor to help visualize lumpectomy volume. The statistical performance of the platform’s automated features was robust and within the range of inter-radiologist variability. These detailed 3D tumor and surrounding multi-tissue depictions offer both qualitative and quantitative comprehension of cancer topology and may aid in formulating an optimal surgical approach for breast cancer treatment. We further establish the framework for broader data integration into the platform to enhance precision cancer care.

Enhancing Compliance and Governance through Data Consistency and Rationalization for Effective Risk Mitigation in Health Care

In the era of big data, you absolutely must have consistency across your different data systems to make effective decision-making, be compliance, enhanced governance and security and also mitigation of risks,, and even have system integrity. The data consistency and how rationalization techniques help overcome challenges when working with heterogeneous data sources, is what this paper explores. Rationalization allows businesses to keep their data accurate and consistent by standardizing data formats, reducing duplications, measuring the data growth, categories the data sources, farmats, and aligning conflicting information. We compare the different methods of normalization, data deduplication, Master Data Management (MDM) and data integration framework. We also delve into the part that automation, artificial intelligence and machine learning play in optimizing these processes and provide scalable solutions that can potentially simplify the complexity of modern-day data environments. Practical implications from rationalization effort case studies are shown from sectors like, healthcare, finance, and e-commerce. Our specific findings show that long-term data consistency requires a systematic approach that employs technology and strong governance. In the end, this paper gives a blueprint for how to exploit rationalization methods to exploit the capabilities of data-driven insights fully.

Integrated Analysis of Utilization Efficiency of Intelligent Sports Venues Based on Data Mining Algorithms

With the rapid development of information technology and the increasing prosperity of the sports industry, the construction and management of intelligent sports venues have become an important way to improve the efficiency of sports resource utilization and meet diverse fitness needs. This paper focuses on the integrated analysis of the utilization efficiency of intelligent sports venues based on data mining algorithms. Aiming to reveal the inherent laws and potential value in the operation of sports venues through advanced data processing and analysis techniques, and provide a scientific basis for optimizing resource allocation and improving service quality. Intelligent sports venues utilize modern information technologies such as the Internet of Things, big data, and cloud computing to achieve comprehensive and intelligent monitoring and management of venue facilities, personnel activities, environmental conditions, and more. This new management model not only improves the operational efficiency of the venue but also enhances the user experience and meets personalized and diverse fitness needs. This paper focuses on the current situation of the opening of university sports venues to the outside world, aiming to reveal the pain points and bottlenecks in their operation process through detailed investigation and analysis. This study introduces advanced concepts of middleware architecture and constructs a multi-level data integration framework that includes local models, global models, user models, and mapping rules. This framework can effectively integrate distributed heterogeneous data sources, achieving seamless integration and efficient interaction of data. At the same time, with the help of distributed programming technology in cloud computing, it is possible to conduct in-depth mining and analysis of integrated massive data, discover potential value, and provide a scientific basis for decision support.

Three-dimensional intelligent monitoring and early warning technology for tailings ponds based on spatiotemporal fusion of multisource big data.

To solve the difficult problems of tailings dam instability and environmental pollution, multisource information perception, prediction and early warning technology for tailings dams are investigated. Taking a tailings pond in China as an example, a three-dimensional visualization intelligent management platform based on the spatiotemporal fusion of multisource big data is established to realize intelligent real-time monitoring, prediction and early warning of tailings dams. A monitoring system for air-space-ground integration was developed via high-resolution optical image recording, unmanned aerial vehicles (UAVs), radar, video surveillance and displacement sensors. This approach can realize pollution monitoring and efficient identification of high-risk areas. In addition, a machine learning algorithm is used to mine spatiotemporal data in detail. The Alibaba cloud platform was adopted to develop a data integration framework. Multisensor spatiotemporal data from tailings dams and multisource monitoring data from the environment were integrated. The integrated management of tailings dam environmental monitoring, pollution and stability assessment is realized. A stability prediction model for tailings dams based on multisource data is proposed. The temporal and spatial information of various forms of data is analysed from multiple levels and perspectives. Rapid prediction of the disaster situation and stability of tailings ponds is realized. Moreover, a tailings pollution assessment model based on a neural network is integrated with multisource information to establish a multifactor environmental pollution neural network assessment model and a multilevel warning platform. This work can provide technical support for intelligent monitoring and early warning systems for tailings ponds.

Enabling data linkages for rare diseases in a resilient environment with the SERDIF framework

Environmental factors amplified by climate change contribute significantly to the global burden of disease, disproportionately impacting vulnerable populations, such as individuals with rare diseases. Researchers require innovative, dynamic data linkage methods to enable the development of risk prediction models, particularly for diseases like vasculitis with unknown aetiology but potential environmental triggers. In response, we present the Semantic Environmental and Rare Disease Data Integration Framework (SERDIF). SERDIF was evaluated with researchers studying climate-related health hazards of vasculitis disease activity across European countries (NP1 = 10, NP2 = 17, NP3 = 23). Usability metrics consistently improved, indicating SERDIF’s effectiveness in linking complex environmental and health datasets. Furthermore, SERDIF-enabled epidemiologists to study environmental factors in a pregnancy cohort in Lombardy, showcasing its versatility beyond rare diseases. This framework offers for the first time a user-friendly, FAIR-compliant design for environment-health data linkage with export capabilities enabling data analysis to mitigate health risks posed by climate change.

How fund manager speaks values: evidence from China

ABSTRACT Confidence, especially in the form of overconfidence, is a crucial psychological factor that influences fund managers’ investment behaviours. However, how to reliably quantify it remains an open question. The self-marketing endeavours via video-sharing platforms of mutual fund companies in China have unexpectedly provided persuasive avenue to gauge confidence through video appearances. This paper proposes a novel data integration framework, combining with multimodal content understanding techniques, and offers calculations of nonverbal personal traits from videos circulated in social media. In particular, we focus on managers’ vocal cues and construct a confidence measure from vocal pitch. Our findings reveal a substantial relation between vocal confidence and fund managers’ investment decisions. Our work also underscores the potential of multimodal data from social media in disclosing nuanced findings in the interdisciplinary studies of finance and psychology.

A graph self-supervised residual learning framework for domain identification and data integration of spatial transcriptomics

Spatial transcriptomics (ST) technologies allow for comprehensive characterization of gene expression patterns in the context of tissue microenvironment. However, accurately identifying domains with spatial coherence in both gene expression and histology in situ and effectively integrating data from multi-sample remains challenging. Here, we propose ResST, a graph self-supervised residual learning model based on graph neural network and Margin Disparity Discrepancy (MDD) theory. ResST aggregates gene expression, biological effects, spatial location, and morphological information to capture nonlinear relationships between a cell and surrounding cells for spatial domain identification. Also, ResST integrates multiple ST datasets and aligns latent embeddings based on MDD theory for correcting batch effects. Results show that ResST identifies continuous spatial domains at a finer scale in ten ST datasets acquired with different technologies. Moreover, ResST efficiently integrated data from multiple tissue sections vertically or horizontally while correcting batch effects. Overall, ResST demonstrates exceptional performance in analyzing ST datasets.

Digital Microbe: a genome-informed data integration framework for team science on emerging model organisms

The remarkable pace of genomic data generation is rapidly transforming our understanding of life at the micron scale. Yet this data stream also creates challenges for team science. A single microbe can have multiple versions of genome architecture, functional gene annotations, and gene identifiers; additionally, the lack of mechanisms for collating and preserving advances in this knowledge raises barriers to community coalescence around shared datasets. “Digital Microbes” are frameworks for interoperable and reproducible collaborative science through open source, community-curated data packages built on a (pan)genomic foundation. Housed within an integrative software environment, Digital Microbes ensure real-time alignment of research efforts for collaborative teams and facilitate novel scientific insights as new layers of data are added. Here we describe two Digital Microbes: 1) the heterotrophic marine bacterium Ruegeria pomeroyi DSS-3 with > 100 transcriptomic datasets from lab and field studies, and 2) the pangenome of the cosmopolitan marine heterotroph Alteromonas containing 339 genomes. Examples demonstrate how an integrated framework collating public (pan)genome-informed data can generate novel and reproducible findings.

A Quantitative Computational Framework for Allopolyploid Single-Cell Data Integration and Core Gene Ranking in Development.

Polyploidization drives regulatory and phenotypic innovation. How the merger of different genomes contributes to polyploid development is a fundamental issue in evolutionary developmental biology and breeding research. Clarifying this issue is challenging because of genome complexity and the difficulty in tracking stochastic subgenome divergence during development. Recent single-cell sequencing techniques enabled probing subgenome-divergent regulation in the context of cellular differentiation. However, analyzing single-cell data suffers from high error rates due to high dimensionality, noise, and sparsity, and the errors stack up in polyploid analysis due to the increased dimensionality of comparisons between subgenomes of each cell, hindering deeper mechanistic understandings. In this study, we develop a quantitative computational framework, called "pseudo-genome divergence quantification" (pgDQ), for quantifying and tracking subgenome divergence directly at the cellular level. Further comparing with cellular differentiation trajectories derived from single-cell RNA sequencing data allows for an examination of the relationship between subgenome divergence and the progression of development. pgDQ produces robust results and is insensitive to data dropout and noise, avoiding high error rates due to multiple comparisons of genes, cells, and subgenomes. A statistical diagnostic approach is proposed to identify genes that are central to subgenome divergence during development, which facilitates the integration of different data modalities, enabling the identification of factors and pathways that mediate subgenome-divergent activity during development. Case studies have demonstrated that applying pgDQ to single-cell and bulk tissue transcriptomic data promotes a systematic and deeper understanding of how dynamic subgenome divergence contributes to developmental trajectories in polyploid evolution.

Big Data Analytics for Financial Decision Making in Malaysia

Purpose: To aim of the study was to analyze the big data analytics for financial decision making in Malaysia. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Big Data Analytics (BDA) significantly enhances financial decision-making in Malaysia by extracting valuable insights from large datasets. It helps improve risk management, customer segmentation, and operational efficiency for financial institutions. BDA enables predictive analysis of market behavior, supports personalized financial services, and enhances fraud detection capabilities. Despite its benefits, challenges like data privacy issues and the need for skilled data professionals persist. Unique Contribution to Theory, Practice and Policy: Diffusion of innovations theory, technology acceptance model (TAM) & network externalities theory may be used to anchor future studies on big data analytics for financial decision making in Malaysia. Prioritize investments in scalable and secure data infrastructure to support the deployment and integration of BDA technologies across financial institutions. This includes upgrading data storage systems, enhancing data processing capabilities, and implementing robust data integration frameworks. Develop and enforce clear regulatory frameworks that govern the ethical use of BDA in financial decision-making. Regulatory guidelines should address data privacy, security standards, transparency in algorithmic processes, and consumer rights protection.

Integration of Inventory Data from Cohort and Registry Studies into an Existing Research Network: National Pandemic Cohort Network (NAPKON)

In the early phase of the COVID-19 pandemic, many local collections of clinical data on patients infected with SARS-CoV-2 were initiated in Germany. As part of the National Pandemic Cohort Network (NAPKON) of the University Medicine Network, the "Integration Core" was established to design the legal, technical and organisational requirements for the integration of inventory data into ongoing prospective data collections and to test the feasibility of the newly developed solutions using use cases (UCs). Detailed study documents of the data collections were obtained. After structured document analysis, a review board evaluated the integrability of the data in NAPKON according to defined criteria. Of 30 university hospitals contacted, 20 responded to the request. Patient information and consent showed a heterogeneous picture with regard to the pseudonymised transfer of data to third parties and re-contact. The majority of the data collections (n=13) met the criteria for integration into NAPKON; four studies would require adjustments to the regulatory documents. Three cohorts were not suitable for inclusion in NAPKON. The legal framework for retrospective data integration and consent-free data use via research clauses (§27 BDSG) was elaborated by a legal opinion by TMF - Technology, Methods and Infrastructure for Networked Medical Research, Berlin. Two UCs selected by the NAPKON steering committee (CORKUM, LMU Munich; Pa-COVID-19, Charité- Universitätsmedizin Berlin) were used to demonstrate the feasibility of data integration in NAPKON by the end of 2021. Quality assurance and performance-based reimbursement of the cases were carried out according to the specifications. Based on the results, recommendations can be formulated for various contexts in order to create technical-operational prerequisites such as interoperability, interfaces and data models for data integration and to fulfil regulatory requirements on ethics, data protection, medical confidentiality and data access when integrating existing cohort data. The possible integration of data into research networks and their secondary use should be taken into account as early as the planning phase of a study - particularly with regard to informed consent - in order to maximise the benefits of the data collected.

Reconstruction of gene regulatory networks for Caenorhabditis elegans using tree-shaped gene expression data.

Constructing gene regulatory networks is a widely adopted approach for investigating gene regulation, offering diverse applications in biology and medicine. A great deal of research focuses on using time series data or single-cell RNA-sequencing data to infer gene regulatory networks. However, such gene expression data lack either cellular or temporal information. Fortunately, the advent of time-lapse confocal laser microscopy enables biologists to obtain tree-shaped gene expression data of Caenorhabditis elegans, achieving both cellular and temporal resolution. Although such tree-shaped data provide abundant knowledge, they pose challenges like non-pairwise time series, laying the inaccuracy of downstream analysis. To address this issue, a comprehensive framework for data integration and a novel Bayesian approach based on Boolean network with time delay are proposed. The pre-screening process and Markov Chain Monte Carlo algorithm are applied to obtain the parameter estimates. Simulation studies show that our method outperforms existing Boolean network inference algorithms. Leveraging the proposed approach, gene regulatory networks for five subtrees are reconstructed based on the real tree-shaped datatsets of Caenorhabditis elegans, where some gene regulatory relationships confirmed in previous genetic studies are recovered. Also, heterogeneity of regulatory relationships in different cell lineage subtrees is detected. Furthermore, the exploration of potential gene regulatory relationships that bear importance in human diseases is undertaken. All source code is available at the GitHub repository https://github.com/edawu11/BBTD.git.

The Chilean exposome-based system for ecosystems (CHiESS): a framework for national data integration and analytics platform.

The double burden of diseases and scarce resources in developing countries highlight the need to change the conceptualization of health problems and translational research. Contrary to the traditional paradigm focused on genetics, the exposome paradigm proposed in 2005 that complements the genome is an innovative theory. It involves a holistic approach to understanding the complexity of the interactions between the human being’s environment throughout their life and health. This paper outlines a scalable framework for exposome research, integrating diverse data sources for comprehensive public health surveillance and policy support. The Chilean exposome-based system for ecosystems (CHiESS) project proposes a conceptual model based on the ecological and One Health approaches, and the development of a technological dynamic platform for exposome research, which leverages available administrative data routinely collected by national agencies, in clinical records, and by biobanks. CHiESS considers a multilevel exposure for exposome operationalization, including the ecosystem, community, population, and individual levels. CHiESS will include four consecutive stages for development into an informatic platform: (1) environmental data integration and harmonization system, (2) clinical and omics data integration, (3) advanced analytical algorithm development, and (4) visualization interface development and targeted population-based cohort recruitment. The CHiESS platform aims to integrate and harmonize available secondary administrative data and provide a complete geospatial mapping of the external exposome. Additionally, it aims to analyze complex interactions between environmental stressors of the ecosystem and molecular processes of the human being and their effect on human health. Moreover, by identifying exposome-based hotspots, CHiESS allows the targeted and efficient recruitment of population-based cohorts for translational research and impact evaluation. Utilizing advanced technologies such as Artificial Intelligence (AI), Internet of Things (IoT), and blockchain, this framework enhances data security, real-time monitoring, and predictive analytics. The CHiESS model is adaptable for international use, promoting global health collaboration and supporting sustainable development goals.

Integration analysis of pituitary proteome and transcriptome reveals fertility-related biomarkers in FecB mutant Small Tail Han sheep.

The Booroola fecundity mutation (FecB) in Small Tail Han sheep has been shown to enhance ovulation rates and litter sizes by affecting the hypothalamic-pituitary-gonadal (HPG) axis. Despite the pituitary's role in reproductive regulation, its involvement in FecB-induced ovulation remains understudied. Our study aimed to fill this gap by analyzing pituitary tissues from FecB homozygous (BB) and wild-type (WW) ewes during luteal and follicular phases using tandem mass tag-based protein quantification and the DIABLO framework for proteomic and transcriptomic data integration. Significant differences in 277 proteins were observed across estrus periods, with network analysis highlighting the voltage-dependent calcium channel L-type alpha-1C as a key convergence point in oxytocin signaling and GnRH secretion pathways. The DIABLO method revealed a strong correlation (0.98) between proteomic and transcriptomic datasets, indicating a coordinated response in FecB ewes. Notably, higher expression levels of Follicle Stimulating Hormone Subunit Beta (FSHB) and Luteinizing Hormone Subunit Beta (LHB) were found in BB ewes during the follicular phase, potentially due to elevated E2 concentrations. Furthermore, our analysis identified genes related to the Gamma-aminobutyric acid type A receptor family (GABRA2, GABRG1, GABRB1) in the pituitary, with GABRB1 showing higher expression in BB ewes. This suggests a role for GABA in modulating GnRH and gonadotropin feedback loops, potentially contributing to the FecB mutation's effect on ovulation. This study provides novel insights into the pituitary's role in fertility among FecB sheep, identifying GABA as a potential regulatory factor within the HPG axis. The findings also open avenues for discovering new biomarkers in pituitary endocrinology for sheep breeding purposes.

Augmented reality navigation method based on image segmentation and sensor tracking registration technology

With the rapid development of modern science and technology, navigation technology provides great convenience for people's life, but the problem of inaccurate localization in complex environments has always been a challenge that navigation technology needs to be solved urgently. To address this challenge, this paper proposes an augmented reality navigation method that combines image segmentation and multi-sensor fusion tracking registration. The method optimizes the image processing process through the GA-OTSU-Canny algorithm and combines high-precision multi-sensor information in order to achieve accurate tracking of positioning and guidance in complex environments. Experimental results show that the GA-OTSU-Canny algorithm has a faster image edge segmentation rate, and the fastest start speed is only 1.8 s, and the fastest intersection selection time is 1.2 s. The navigation system combining the image segmentation and sensor tracking and registration techniques has a highly efficient performance in real-world navigation, and its building recognition rates are all above 99%. The augmented reality navigation system not only improves the navigation accuracy in high-rise and urban canyon environments, but also significantly outperforms traditional navigation solutions in terms of navigation startup time and target building recognition accuracy. In summary, this research not only provides a new framework for the theoretical integration of image processing and multi-sensor data, but also brings innovative technical solutions for the development and application of practical navigation systems.

Automated single-cell omics end-to-end framework with data-driven batch inference.

To facilitate single-cell multi-omics analysis and improve reproducibility, we present SPEEDI (Single-cell Pipeline for End to End Data Integration), a fully automated end-to-end framework for batch inference, data integration, and cell type labeling. SPEEDI introduces data-driven batch inference and transforms the often heterogeneous data matrices obtained from different samples into a uniformly annotated and integrated dataset. Without requiring user input, it automatically selects parameters and executes pre-processing, sample integration, and cell type mapping. It can also perform downstream analyses of differential signals between treatment conditions and gene functional modules. SPEEDI's data-driven batch inference method works with widely used integration and cell-typing tools. By developing data-driven batch inference, providing full end-to-end automation, and eliminating parameter selection, SPEEDI improves reproducibility and lowers the barrier to obtaining biological insight from these valuable single-cell datasets. The SPEEDI interactive web application can be accessed at https://speedi.princeton.edu/.

Remote sensing and machine learning approach for zoning of wastewater drainage system

This study aimed to optimize drainage in Tiruchirappalli city using Geographic Information Systems (GIS), machine learning, and remote sensing. The integration of these methods involved multiple steps: remote sensing data was used to gather up-to-date information on land use and land cover (LULC), GIS provided a spatial framework for data integration and visualization, and machine learning, specifically a Random Forest (RF) model, was utilized for detailed LULC classification. Hydrological analysis using a Digital Elevation Model (DEM) helped delineate the watershed and drainage network, determining suitable zones for wastewater systems. Key factors such as ground slope, land use, and proximity to water bodies were considered. Buffer analysis was employed to transform and overlay these data layers. For the site suitability analysis, the Analytic Hierarchy Process (AHP) method was employed within the GIS to integrate various weighted factors, enabling a comprehensive assessment of potential locations for wastewater treatment facilities. Wastewater drainage zones were determined to be appropriate for zones 1, 8, 9, 11, 15, and 20, which correspond to areas of 40.40, 1.11, 1.00, 2.04, 1.69, and 2.46 Sq.km, respectively. This comprehensive approach enabled the identification of optimal wastewater drainage zones and suitable locations for treatment facilities, enhancing urban wastewater management.