Digital Twins for Climate Resilience: Modeling Sustainable Urban Futures
Abstract
Digital twin technology offers a dynamic platform for simulating and managing urban ecosystems. This paper explores the application of digital twins in enhancing climate resilience through predictive modeling of heat islands, flood risks, and energy demand. Integrating IoT data, GIS mapping, and AI analytics, digital twins are shown to empower urban planners to test sustainability strategies in a virtual environment, improving real-world outcomes and resource optimization.
References
Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798-1828.
Gu, S., Kelly, B., & Xiu, D. (2018). Empirical Asset Pricing via Machine Learning. National Bureau of Economic Research, Working Paper No. 25398.
Kroll, J. A., Huey, J., Barocas, S., Felten, E. W., Reidenberg, J. R., Robinson, D. G., & Yu, H. (2017). Accountable Algorithms. University of Pennsylvania Law Review, 165(3), 633-705.
Narayanan, A., & Shmatikov, V. (2010). Myths and fallacies of "personally identifiable information". Communications of the ACM, 53(6), 24-26.
Chittineni, S. (2019). Optimizing Microservices Performance with Reinforcement Learning: A Case Study in Spring Boot Applications. American Journal of AI & Innovation, 1(1).
Chittineni, S. (2019). AI-Driven Optimization of Backend Systems: Enhancing Performance and Reducing Latency in Large-Scale Applications. Australian Journal of Cross-Disciplinary Innovation, 1(1).
Mohammed, C. S. A. (2019). Exploring the Features and Scope of SAP S/4HANA for Financial Products Subledger Management. Australian Journal of Cross-Disciplinary Innovation, 1(1).
Chittineni, S. (2020). Leveraging Machine Learning for Automated Thread Dump Analysis and Performance Tuning in Enterprise Java Applications. Australian Journal of Cross-Disciplinary Innovation, 2(2).
Mohammed, C. (2021). Revolutionizing Financial Operations: A Comprehensive Study on the Impact of SAP and Kyriba Integration. International Journal of Sustainable Development in Computing Science, 3(2), 1-19. Retrieved from https://ijsdcs.com/index.php/ijsdcs/article/view/696/260
The Critical Role of Accurate Balance Carry Forward in Preventing Financial Irregularities. (2022). International Journal of Interdisciplinary Finance Insights, 1(1), 1-13. https://injmr.com/index.php/ijifi/article/view/143
Chittineni, S. (2022). Automated API Performance Testing and Anomaly Detection Using Machine Learning in RESTful Architectures. American Journal of AI & Innovation, 4(4).
Chittineni, S. (2023). Enhancing Messaging Systems with AI: Predictive Load Balancing in JMS and IBM MQ. American Journal of AI & Innovation, 5(5).
Chittineni, S. (2023). Shadow Comparator with AI: A Machine Learning Approach for Anomaly Detection in Production Systems. Australian Journal of Cross-Disciplinary Innovation, 5(5).
Mitigating Risks and Ensuring Compliance: The Necessity of Regular Upgrades to SAP Financial Products Subledger (FPSL) (C. S. A. Mohammed , Trans.). (2023). International Journal of Creative Research In Computer Technology and Design, 5(5), 1-11. https://jrctd.in/index.php/IJRCTD/article/view/75
Chittineni, S. (2024). Real-Time Failure Prediction in Large-Scale Enterprise Applications Using Deep Learning Techniques. Australian Journal of Modern Research & Applications, 7(7).
Chittineni, S. (2024). Intelligent Payment Fraud Detection: Applying Deep Learning Models to Secure Financial Transactions. Australian Journal of Cross-Disciplinary Innovation, 6(6).
Chittineni, S. (2024). AI-Driven Code Refactoring: Improving Java Backend Code Quality with Machine Learning Models. American Journal of AI & Innovation, 6(6).
Brahmandam, B. A. (2024). Using artificial intelligence and AIOps, automated fault prediction and prevention in Cloud Native settings. International Journal of Computer Techniques, 11(6), 1-7.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
