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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: April 2026
Volume 11 | Issue 4
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 119
Article Submitted : 23479
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Total Authors : 24029
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| Paper Title: | AI-Driven Network Serviceability Mapping Using eGIS and Streaming Data Pipeline |
| Authors Name: | Vikas Gupta |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_210220
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| Published Paper Id: | IJRTI2603121 |
| Published In: | Volume 11 Issue 3, March-2026 |
| DOI: | https://doi.org/10.56975/ijrti.v11i3.210220 |
| Abstract: | In the face of growing urban complexity and critical infrastructure demands, ensuring resilient and real-time network serviceability is more vital than ever. This review explores how the convergence of Artificial Intelligence (AI), electronic Geographic Information Systems (eGIS), and streaming data pipelines can revolutionize network monitoring and fault prediction. The paper systematically analyzes various AI models—such as Random Forests, LSTM, and Graph Neural Networks (GNNs)—applied within geospatial frameworks and assesses their effectiveness using real-world datasets and simulations. GNNs emerged as the most capable models in capturing the spatial topology of urban networks. Furthermore, the review examines how platforms like Apache Kafka and ArcGIS enable scalable, near-real-time solutions for service degradation detection. Key challenges such as data heterogeneity, model explainability, and privacy concerns are identified, with future directions pointing toward Edge AI, federated learning, and autonomous multi-agent systems. The findings suggest that AI-eGIS architectures are not only effective but essential in shaping the next generation of smart and resilient infrastructures. |
| Keywords: | AI-driven infrastructure, eGIS, real-time data pipeline, network serviceability, Graph Neural Networks, streaming analytics, explainable AI, smart cities, predictive maintenance, fault detection. |
| Cite Article: | "AI-Driven Network Serviceability Mapping Using eGIS and Streaming Data Pipeline ", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 3, page no.b147-b157, March-2026, Available :http://www.ijrti.org/papers/IJRTI2603121.pdf |
| Downloads: | 000127 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2603121
Registration ID:210220
Published In: Volume 11 Issue 3, March-2026
DOI (Digital Object Identifier): https://doi.org/10.56975/ijrti.v11i3.210220
Page No: b147-b157 Country: -, -, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2603121 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2603121 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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