Online Registration

ISO 9001:2015

INSPIRA-JOURNAL OF COMMERCE,ECONOMICS & COMPUTER SCIENCE(JCECS) [ Vol. 12 | No. 3 | July - September, 2026 ]

Integrating Artificial Intelligence, IoT, and GIS for Smart Disaster Risk Management: A Systematic Review

Chirag Girishchandra Patel

Disasters triggered by hydro-meteorological, geophysical, and climatic hazards are increasing in frequency and severity, straining the capacity of conventional disaster risk management (DRM) systems that rely on manual data collection and delayed decision-making [1], [5]. Recent advances in Artificial Intelligence (AI), the Internet of Things (IoT), and Geographic Information Systems (GIS) have opened new possibilities for building smart, data-driven, and anticipatory disaster management systems [3], [14]. This paper presents a systematic review of the literature published mainly between 2020 and 2026 that examines how AI, IoT, and GIS are being integrated to support hazard prediction, real-time monitoring, spatial risk assessment, early warning, and post-disaster response. Fifty-seven peer-reviewed and indexed sources were synthesised following a structured review protocol. The review identifies five major integration themes: (i) sensor-driven early warning networks, (ii) geospatial machine-learning hazard susceptibility mapping, (iii) UAV and remote-sensing based damage assessment, (iv) social-media and big-data situational awareness, and (v) digital-twin-enabled smart-city resilience platforms. The paper further develops a conceptual AI-IoT-GIS integration architecture, presents mathematical formulations commonly used for spatial risk indexing, sensor network reliability, and machine-learning performance evaluation, and illustrates these formulations with worked numerical examples, tables, and charts. The review concludes that while integration of these three technologies significantly improves prediction accuracy and response times, challenges remain around data interoperability, energy-constrained sensor networks, algorithmic bias, and the digital divide affecting adoption in low-resource regions [2], [15].

  1. International Journal of Advanced Computer Science and Applications. (2025). Artificial Intelligence in Disaster Risk Management: A Bibliometric Analysis (2003–2025). 16(6). https://thesai.org/Downloads/Volume16No6/Paper_101-Artificial_Intelligence_in_Disaster_Risk_Management.pdf
  2. PMC. (2025). Leveraging artificial intelligence in disaster management: A comprehensive bibliometric review. https://pmc.ncbi.nlm.nih.gov/articles/PMC12067534/
  3. Journal of Digital Earth. (2025). Artificial intelligence and machine learning-powered GIS for proactive disaster resilience in a changing climate.

https://www.tandfonline.com/doi/full/10.1080/19475683.2025.2473596

  1. Gupta, T., & Roy, S. (2024). Applications of Artificial Intelligence in Disaster Management. ACM Digital Library. https://dl.acm.org/doi/fullHtml/10.1145/3669754.3669802
  2. Natural Hazards (Springer). (2025). Artificial intelligence in disaster management: achievements, challenges, and prospects. https://link.springer.com/article/10.1007/s11069-025-07667-5
  3. arXiv. (2025). Human-AI Use Patterns for Decision-Making in Disaster Scenarios: A Systematic Review. arXiv:2509.12034. https://arxiv.org/pdf/2509.12034
  4. Ashwini, A., Sriram, & Sangeetha, S. (2024). IoT-Based Smart Sensors: The Key to Early Warning Systems and Rapid Response in Natural Disasters. In Predicting Natural Disasters With AI and Machine Learning (pp. 202–223). IGI Global. https://doi.org/10.4018/979-8-3693-2280-2.ch010
  5. Revolutionized. (2025). How IoT Disaster Management Improves Emergency Outcomes. https://revolutionized.com/iot-disaster-management/
  6. IoT For All. (2025). How IoT Is Revolutionizing Disaster Prevention and Response. https://www.iotforall.com/how-iot-is-revolutionizing-disaster-prevention-and-response
  7. MDPI IoT Journal. (2025). Internet of Things for Enhancing Public Safety, Disaster Response, and Emergency Management. 92(1), 61. https://www.mdpi.com/2673-4591/92/1/61
  8. Pahuriray, A. V., & Cerna, P. D. (2025). IoT-Enabled Flood Monitoring and Early Warning Systems. International Journal of Computer Science and Mobile Computing, 14(4), 50–67. https://www.ijcsmc.com/docs/papers/April2025/V14I4202515.pdf
  9. IEEE Xplore. (2025). Leveraging the Internet of Things (IoT) for Disaster Management: Enhancing Resilience, Early Warning System in a Globally Connected World. https://ieeexplore.ieee.org/document/10407362/
  10. GAO Tek. (2024). Disaster Prediction and Early Warning Systems – Public Safety and Emergency Response. https://gaotek.com/disaster-prediction-and-early-warning-systems-public-safety-and-emergency-response/
  11. ScienceDirect. (2025). Urban Resilience through IoT-Based Disaster Preparedness and Infrastructure Monitoring: A Systematic Literature Review.

https://www.sciencedirect.com/science/article/pii/S2666592125000812

  1. ScienceDirect. (2025). Flood susceptibility assessment and mapping using GIS-based analytical hierarchy process and frequency ratio models.

https://www.sciencedirect.com/science/article/pii/S0921818125001407

  1. Geomatics, Natural Hazards and Risk. (2024). Flood susceptibility mapping leveraging open-source remote-sensing data and machine learning approaches in Nam Ngum River Basin (NNRB), Lao PDR. https://www.tandfonline.com/doi/full/10.1080/19475705.2024.2357650
  2. PMC. (2024). Integrating machine learning and geospatial data analysis for comprehensive flood hazard assessment. https://pmc.ncbi.nlm.nih.gov/articles/PMC11297827/
  3. ScienceDirect. (2024). Flood susceptibility mapping: Integrating machine learning and GIS for enhanced risk assessment.

https://www.sciencedirect.com/science/article/pii/S2590197424000302

  1. ScienceDirect. (2025). Next generation data-driven flood susceptibility modelling with spatial machine learning. https://www.sciencedirect.com/science/article/pii/S2468227625005514
  2. Environmental Science and Pollution Research (Springer). (2024). Integrating machine learning and geospatial data analysis for comprehensive flood hazard assessment. https://link.springer.com/article/10.1007/s11356-024-34286-7
  3. Scientific Reports (Nature). (2026). Integrating geospatial intelligence and machine learning for flood susceptibility mapping. https://www.nature.com/articles/s41598-026-41014-3
  4. DOAJ. (2024). Flood susceptibility mapping: Integrating machine learning and GIS for enhanced risk assessment. https://doaj.org/article/b7272de8852e4f5d97f5bda848046620
  5. AmericasPG. (2026). Artificial Intelligence and Optimization Techniques in Earthquake Engineering: A Systematic Review. https://americaspg.com/journal/41/article/4207
  6. Catani, F. (2025). Deep learning unlocks global prediction of earthquake-triggered landslides. National Science Review, 12(8), nwaf282. https://doi.org/10.1093/nsr/nwaf282
  7. Natural Hazards and Earth System Sciences. (2026). Review article: Deep learning for potential landslide identification: data, models, applications, challenges, and opportunities. https://nhess.copernicus.org/articles/26/487/2026/
  8. MDPI Applied Sciences. (2025). Landslide Susceptibility Prediction Based on a CNN–LSTM–SAM–Attention Hybrid Model. 15(13), 7245. https://www.mdpi.com/2076-3417/15/13/7245
  9. MDPI GeoHazards. (2026). Prediction of Coseismic Landslides by Explainable Machine Learning Methods. 7(1). https://doi.org/10.3390/geohazards7010007
  10. ResearchGate. (2024). A Systematic review of machine learning based landslide susceptibility mapping. https://www.researchgate.net/publication/381522162
  11. PMC. (2025). Deep learning can predict global earthquake-triggered landslides (Fan et al., summary commentary). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12365752/
  12. arXiv. (2026). From Landslide Conditioning Factors to Satellite Embeddings: Evaluating the Utilisation of Google AlphaEarth for Landslide Susceptibility Mapping using Deep Learning. arXiv:2601.07268.
  13. OUCI. (2024). Building Resilient Smart Cities: The Role of Digital Twins and Generative AI in Disaster Management Strategy. https://ouci.dntb.gov.ua/en/works/7qEmmXe5/
  14. PMC. (2024). The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities: A comprehensive systematic review.

https://pmc.ncbi.nlm.nih.gov/articles/PMC11145432/

  1. arXiv. (2024). The Role and Applications of Airport Digital Twin in Cyberattack Protection during the Generative AI Era. arXiv:2408.05248.
  2. MDPI Smart Cities. (2025). IoT, AI, and Digital Twins in Smart Cities: A Systematic Review for a Thematic Mapping and Research Agenda. 8(5), 175. https://doi.org/10.3390/smartcities8050175
  3. Urban Planning (Cogitatio Press). (2025). Data-Driven Urban Digital Twins and Critical Infrastructure. https://www.cogitatiopress.com/urbanplanning/article/download/10109/4602
  4. Discover Cities (Springer). (2026). Linking digital twin paradigm for urban heat monitoring and policy integration to building smart city climate resilience.

https://link.springer.com/article/10.1007/s44327-025-00179-8

  1. ScienceDirect. (2025). Digital twin technology in smart cities: A step toward intelligent urban management. https://www.sciencedirect.com/science/article/pii/S2352484725007127
  2. arXiv. (2025). Hazard-Responsive Digital Twin for Climate-Driven Urban Resilience and Equity. arXiv:2510.22941.
  3. MDPI Applied Sciences. (2024). AI-Powered Digital Twins and Internet of Things for Smart Cities and Sustainable Building Environment. 14(24), 12056. https://www.mdpi.com/2076-3417/14/24/12056
  4. MDPI Forests. (2025). Real-Time Detection of Smoke and Fire in the Wild Using Unmanned Aerial Vehicle Remote Sensing Imagery. 16(2), 201. https://www.mdpi.com/1999-4907/16/2/201
  5. Semantic Scholar. (2025). A Deep Learning Based Forest Fire Detection Approach Using UAV and YOLOv3 (and related YOLOv11 fire-smoke detection studies).

https://www.semanticscholar.org/paper/9c68f1d2df0fc45d37f298890ca3f8d0863cacb3

  1. Frontiers in Forests and Global Change. (2025). Semi-supervised segmentation of forest fires from UAV remote sensing images via panoramic feature fusion and pixel contrastive learning. https://www.frontiersin.org/journals/forests-and-global-change/articles/10.3389/ffgc.2025.1669707/full
  2. Scientific Data (Nature). (2025). Boreal Forest Fire: UAV-collected Wildfire Detection and Smoke Segmentation Dataset. https://www.nature.com/articles/s41597-025-05634-0
  3. PMC. (2024). Development of a deep learning-based surveillance system for forest fire detection and monitoring using UAV. https://pmc.ncbi.nlm.nih.gov/articles/PMC10931456/
  4. ScienceDirect. (2024). Applying deep learning to real-time UAV-based forest monitoring: Leveraging multi-sensor imagery for improved results.

https://www.sciencedirect.com/science/article/abs/pii/S0957417423036114

  1. arXiv. (2025). Wildfire spread forecasting with Deep Learning. arXiv:2505.17556.
  2. arXiv. (2025). Two-Stage Framework for Efficient UAV-Based Wildfire Video Analysis with Adaptive Compression and Fire Source Detection. arXiv:2508.16739.
  3. ScienceDirect. (2025). A systematic review of social media-based sentiment analysis in disaster risk management. https://www.sciencedirect.com/science/article/pii/S2212420925003115
  4. International Journal of Disaster Risk Reduction. (2024). Social media data for disaster risk management and research. 114, 104980.

https://www.sciencedirect.com/science/article/pii/S2212420924007428

  1. kkant.net. Social Media Driven Big Data Analysis for Disaster Situation Awareness: A Tutorial. https://www.kkant.net/papers/Social_Media_Driven_Big_Data_Analysis_for_Disaster_Situation_Awareness__A_Tutorial_.pdf
  2. Springer. (2025). A Tutorial on Social Media Data Analytics for Disaster Management. https://link.springer.com/chapter/10.1007/978-3-031-97207-2_37
  3. arXiv. (2025). FRIDA to the Rescue! Analyzing Synthetic Data Effectiveness in Object-Based Common Sense Reasoning for Disaster Response. arXiv:2502.18452.
  4. ResearchGate. Social Media Analytics for Disaster Management.

https://www.researchgate.net/publication/371864990

  1. Natural Hazards Review (ASCE). (2024). Surveying the Use of Social Media Data and Natural Language Processing Techniques to Investigate Natural Disasters. 25(4).

https://ascelibrary.org/doi/10.1061/NHREFO.NHENG-2047

  1. arXiv. Social Media Information Sharing for Natural Disaster Response. arXiv:2005.07019.
  2. Patel, C. (2026). Enhancing Disaster Resilience with Digital Early Warning Systems. Journal of Xidian University, 20(1), 154–162. https://doi.org/10.5281/Zenodo.18204785
  3. Patel, C. (2025). Cyber Security, Privacy, and Network Security: Navigating the Evolving Threat Landscape. https://doi.org/10.2139/ssrn.5069925.

DOI:

Article DOI:

DOI URL:

Download Full Paper:

Download