This study presents a Hybrid Digital-Physical Risk Management Framework designed to enhance urban climate resilience through the integration of Industry 4.0 technologies. By combining real-time IoT sensor networks, AI-based predictive analytics, and blockchain-secured governance, the framework enables anticipatory risk detection, infrastructure adaptation, and decentralized disaster coordination. Applied to four climate-vulnerable cities in Türkiye Gaziantep, Mardin, Diyarbakır, and Adana the model demonstrated a 20% improvement in risk prediction accuracy, a 67% reduction in emergency response time, and a 35% increase in infrastructure resilience. AI models, including LSTM and CNN, were used for time-series forecasting and hazard classification, while digital twins simulated disaster impacts on urban systems. Results underscore the feasibility of deploying cyber-physical systems in mid-sized cities, despite challenges related to sensor coverage, data interoperability, and regulatory fragmentation. The framework offers a scalable, transferable solution for climate-adaptive urban governance, with significant implications for policy, infrastructure planning, and smart city transformation. Findings support broader application across other at-risk regions and highlight the need for ethical AI deployment, data transparency, and regulatory support. This work contributes a replicable model for future-ready cities seeking to operationalize digital intelligence for climate risk mitigation.  

Keywords - Smart City Resilience, Industry 4.0 in Urban Governance, AI-Driven Disaster Risk Management, Digital Twin for Urban Planning, Blockchain for Emergency Response, IoT-Based Climate Risk Monitoring, Hybr

[1] V. Kartal et al., "Climate change trends in the Southeastern Anatolia region of Türkiye: Precipitation and drought," J. Water Clim. Change, 2024. doi: 10.2166/wcc.2024.503.

[2] S. Yılmaz et al., "Emergency Medicine Association of Turkey Disaster Committee Summary of Field Observations of February 6th Kahramanmaraş Earthquakes," Prehosp. Disaster Med., vol. 38, pp. 415–418, 2023. doi: 10.1017/S1049023X23000523.

[3] E. O. Çalıkoğlu et al., "Gaziantep Modeli: Afetler Sonrası Sağlıklı Toplumlar İçin Bütünleşik Anne-Çocuk Sağlığı Hizmetleri," Celal Bayar Üniversitesi Sağlık Bilimleri Enstitüsü Dergisi, 2023. doi: 10.34087/cbusbed.1400223.

[4] Y. Melikoğlu and K. S. Kayihan, "Potential Usage of Mosques in Earthquake Management in Contemporary Cities: The Case of Diyarbakır," Mimarlık Bilimleri ve Uygulamaları Dergisi (MBUD), 2023. doi: 10.30785/mbud.1291616.

[5] B. Ilmaz, "Analysis Of Hydrological Drought Trends In The Gap Region (Southeastern Turkey) By Mann-Kendall Test And Innovative Şen Method," 2019. [Online]. Available: https://api.semanticscholar.org/CorpusID:199415562.

[6] K. K. Singh, "Risk management in smart cities using big data," Int. J. Adv. Res. Commerce, Manag. Soc. Sci., 2025. doi: 10.62823/ijarcmss/8.1(i).7179.

[7] T. F. Himdi, "A blockchain and AI-driven security framework for enhancing cybersecurity in cognitive cities," Adv. Artif. Intell. Mach. Learn., vol. 4, pp. 2908–2926, 2024. doi: 10.54364/aaiml.2024.44169.

[8] B. Ojo et al., "Smart urban infrastructure: Leveraging IoT for enhanced resilience to climate change," Int. J. Sci. Res. Archive, 2024. doi: 10.30574/ijsra.2024.12.2.1363.

[9] A. Saigal, "Advancements in smart transportation: A data analytics approach for urban mobility," World J. Adv. Res. Rev., 2025. doi: 10.30574/wjarr.2025.25.2.0581.

[10] J. Tupa, J. Šimota, and F. Steiner, "Aspects of risk management implementation for Industry 4.0," Procedia Manuf., vol. 11, pp. 1223–1230, 2017. doi: 10.1016/j.promfg.2017.07.248.

[11] V. Gümüş et al., "Evaluation of meteorological time series trends in Southeastern Anatolia, Turkey," Geofizika, 2023. doi: 10.15233/gfz.2023.40.3.

[12] M. Türkeş, "Climate and drought in Turkey," in Water Resources of Turkey, Springer, 2019. doi: 10.1007/978-3-030-11729-0_4.

[13] M. K. Keleş and E. Kavak, "Climate analysis and weather forecasting with data mining: The case of Adana Province in Turkey," Curr. Trends Nat. Sci., 2022. doi: 10.47068/ctns.2022.v11i21.033.

[14] J. Turek et al., "The role of Industry 4.0 technologies in driving the financial importance of sustainability risk management," Equilibrium. Q. J. Econ. Econ. Policy, 2023. doi: 10.24136/eq.2023.032.

[15] V. Tecim and C. Tarhan, "Turkey’s Industry 4.0 adventure: Dream or realization," KnE Soc. Sci., pp. 426–437, 2020. doi: 10.18502/kss.v4i1.6004.

[16] H. Zhang and X. Yang, "Design and implementation of an AI-driven hybrid framework for risk assessment," 2024. doi: 10.1109/ICAACE61206.2024.10548147.

[17] A. S. Ghani, "Revolutionizing supply chains: A comprehensive study of Industry 4.0 technologies (IoT, Big Data, AI, etc.)," Int. J. Sci. Res. Eng. Manag., 2024. doi: 10.55041/ijsrem30037.

[18] Suhail et al., "TRIPLE: A blockchain-based digital twin framework for cyber-physical systems security," 2024. doi: 10.1016/j.jii.2024.100706.

[19] X. Romão and F. Pereira, "Smart disaster risk reduction and emergency management in the built environment," in Structural Integrity, 2021. doi: 10.1007/978-3-030-82430-3_14.

[20] M. Lom, O. Přibyl, and M. Svítek, "Industry 4.0 as a part of smart cities," in 2016 Smart Cities Symposium Prague (SCSP), pp. 1–6, 2016. doi: 10.1109/SCSP.2016.7501015.

[21] A. Pandit, "Digital twins in construction: Creating real-time replicas of large-scale projects," Int. J. Sci. Res. Eng. Manag., 2025. doi: 10.55041/ijsrem40755.

[22] V. Astarita et al., "Risk reduction in transportation systems: The role of digital twins according to a bibliometric-based literature review," Sustainability, 2024. doi: 10.3390/su16083212.

[23] M. Sasikala et al., "Integrating digital twins with AI for real-time intrusion detection in smart infrastructure networks," in 2024 Int. Conf. Intell. Algorithms Comput. Intell. Syst. (IACIS), pp. 1–6, 2024. doi: 10.1109/IACIS61494.2024.10721892.

[24] S. Guikema and R. Flage, "Digital twins as a security risk?" Risk Anal., 2024. doi: 10.1111/risa.15749.

[25] N. Halawi-Ghoson et al., "A review on the static and dynamic risk assessment methods for OT cybersecurity in Industry 4.0," Comput. Secur., vol. 150, p. 104295, 2024. doi: 10.1016/j.cose.2024.104295.

[26] E. Radvilė and R. Urbonas, "Digital transformation in energy systems: A comprehensive review of AI, IoT, blockchain, and decentralised energy models," Energetika, 2025. doi: 10.6001/energetika.2025.71.1.1.

[27] P. J. Ramal and E. Anbalagan, "Emphasis interaction based Industry 5.0 smart city digital twin’s technology," in 2024 7th Int. Conf. Circuit Power Comput. Technol. (ICCPCT), pp. 726–731, 2024. doi: 10.1109/ICCPCT61902.2024.10673201.

[28] M. Y. Herath et al., "Edge AI-enabled road fixture monitoring system," Buildings, 2024. doi: 10.3390/buildings14051220.

[29] S. Subramanian, "IoT and edge computing for smart manufacturing: Architecture and future trends," Int. J. Eng. Comput. Sci., 2024. doi: 10.18535/ijecs/v13i10.4922.

[30] G. Walia, M. Kumar, and S. Gill, "AI-empowered fog/edge resource management for IoT applications: A comprehensive review," IEEE Commun. Surv. Tutor., vol. 26, pp. 619–669, 2024. doi: 10.1109/COMST.2023.3338015.

[31] G. Bonino et al., "Machine learning methods to predict sea surface temperature and marine heatwave occurrence: A case study of the Mediterranean Sea," Ocean Sci., 2024. doi: 10.5194/os-20-417-2024.

[32] P. Charan et al., "AI-enhanced early warning systems for natural disaster detection and mitigation using wireless sensor networks," in 2024 Second Int. Conf. Comput. Characterization Technol. Eng. Sci. (IC3TES), pp. 1–6, 2024. doi: 10.1109/ic3tes62412.2024.10877562.

[33] D. Kolhatkar et al., "Blockchain applications in disaster management systems," in 2023 4th Int. Conf. Emerg. Technol. (INCET), pp. 1–7, 2023. doi: 10.1109/INCET57972.2023.10170452.

[34] J. S. Oliha, P. W. Biu, and O. C. Obi, "Securing the smart city: A review of cybersecurity challenges and strategies," Eng. Sci. Technol. J., 2024. doi: 10.51594/estj.v5i2.827.

[35] L. Kanigolla et al., "Architecting digital twin for smart city systems: A case study," in *2024 IEEE 21st Int. Conf. Softw. Archit. Companion (ICSA-C)*, pp. 326–334, 2024. doi: 10.1109/ICSA-C63560.2024.00061.

[36] S. Çağlak and M. Türkeş, "Spatial distribution and future projections of thermal comfort conditions during the hot period of the year in Diyarbakır City, Southeastern Turkey," Sustainability, 2023. doi: 10.3390/su151310473.

[37] I. Gkougkoustamos et al., "Correlation of ground deformation induced by the 6 February 2023 M7.8 and M7.5 earthquakes in Turkey inferred by Sentinel-2 and critical exposure in Gaziantep and Kahramanmaraş cities," GeoHazards, 2023. doi: 10.3390/geohazards4030015.

[38] O. Tan, "Long-term aftershock properties of the catastrophic 6 February 2023 Kahramanmaraş (Türkiye) earthquake sequence," Acta Geophys., 2024. doi: 10.1007/s11600-024-01419-y.

[39] M. Ozturk, M. H. Arslan, and H. H. Korkmaz, "Effect on RC buildings of 6 February 2023 Turkey earthquake doublets and new doctrines for seismic design," Eng. Fail. Anal., 2023. doi: 10.1016/j.engfailanal.2023.107521.

[40] S. Biswas et al., "Performance of a five-layer ANN model for earthquake magnitude prediction and spatial risk mapping in Turkey," Decis. Making Adv., 2024. doi: 10.31181/dma31202553.

[41] M. Ahmadi et al., "Eigenvector spatial filtering enhancing natural hazards vulnerability assessment in a susceptible urban environment: A case study of Izmir earthquake in Turkey," Environ. Technol. Innov., 2024. doi: 10.1016/j.eti.2024.103666.

[42] A. A. Kelam et al., "An evaluation of seismic hazard and potential damage in Gaziantep, Turkey using site specific models for sources, velocity structure and building stock," Soil Dyn. Earthq. Eng., 2022. doi: 10.1016/j.soildyn.2021.107129.

[43] A. Alloulbi, T. Öz, and A. Alzubi, "The use of artificial intelligence for smart decision-making in smart cities: A moderated mediated model of technology anxiety and internal threats of IoT," Math. Probl. Eng., 2022. doi: 10.1155/2022/6707431.

[44] M. Çavuşlu, "Assessing seismic crack performance of Diyarbakır Çüngüş masonry stone bridge considering 2023 earthquakes," Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 2023. doi: 10.17798/bitlisfen.1263557.

[45] Y. Li, "AI-Enhanced Digital Twins for Energy Efficiency and Carbon Footprint Reduction in Smart City Infrastructure," Appl. Comput. Eng., 2025. doi: 10.54254/2755-2721/2025.20569.

[46] J. Ali et al., "A deep dive into cybersecurity solutions for AI-driven IoT-enabled smart cities in advanced communication networks," Comput. Commun., 2024. doi: 10.1016/j.comcom.2024.108000.

[47] K. Wang et al., "Analyzing the adoption challenges of the Internet of Things (IoT) and Artificial Intelligence (AI) for smart cities in China," Sustainability, vol. 13, no. 19, p. 10983, 2021. doi: 10.3390/su131910983.

[48] Z. Ni et al., "Improving energy efficiency while preserving historic buildings with digital twins and artificial intelligence," IOP Conf. Ser.: Earth Environ. Sci., vol. 863, no. 1, 2021. doi: 10.1088/1755-1315/863/1/012041.

[49] A. S. K. Darwish et al., "Artificial intelligence for sustainable energy transition: Optimising renewable energy integration and management," ARID Int. J. Sci. Technol., 2024. doi: 10.36772/arid.aijst.2024.7134.