AI for Natural Disaster Prediction and Management
DOI:
https://doi.org/10.64137/3108088X/IJAES-V2I1P101Keywords:
Artificial intelligence, Natural disaster prediction, Disaster management, Machine learning, Deep learning, Early warning systems, Risk assessment, Flood forecasting, Earthquake prediction, Hurricane modeling, Wildfire monitoring, Remote sensing, Geospatial analytics, Reinforcement learning, Emergency response planning, Predictive modeling, Climate Risk, Resilient infrastructureAbstract
Artificial intelligence (AI) is transforming the field of natural disaster prediction and management by providing advanced tools for forecasting, early warning, risk assessment, and emergency response. Natural disasters—including earthquakes, hurricanes, floods, wildfires, and tsunamis pose significant threats to human lives, infrastructure, and ecosystems. Traditional prediction methods often rely on historical data, physical modeling, and human expertise, which may be limited in accuracy and scalability. AI techniques, encompassing machine learning, deep learning, and reinforcement learning, can analyze complex spatiotemporal patterns in large datasets, integrate multi-source environmental information, and generate actionable predictions with improved precision and lead time. Applications extend beyond forecasting to include real-time monitoring, resource allocation, evacuation planning, and post-disaster damage assessment. Challenges include data scarcity, model interpretability, integration of heterogeneous data, and ethical considerations in emergency decision-making. Future directions involve multimodal AI integration, explainable and adaptive models, predictive simulations, and AI-assisted disaster resilience planning. AI-driven natural disaster prediction and management promise to reduce human and economic losses, optimize emergency response, and enhance resilience in disaster-prone regions.
References
[1] Jun Jiang, Xiaolong Ruan, and Shugong Xu, “CSI‑MAE: A masked autoencoder‑based channel foundation model for cross‑scenario generalization,” arXiv Preprint, 2026. Doi: https://doi.org/10.48550/arXiv.2601.03789
[2] Jiele Wu et al., “Hierarchical molecular representation learning via fragment‑based self‑supervised embedding prediction,” arXiv Preprint, 2026. Doi:https://doi.org/10.48550/arXiv.2602.20344
[3] Mohammad Majharul Islam Jabed et al., “Self-Supervised Learning for Efficient and Scalable AI: Towards Reducing Data Dependency in Deep Learning Models,” International Journal of Intelligent Systems and Applications in Engineering, vol. 10, no. 3s, pp. 317–326, 2022.
[4] D. Rajendran, Venkata Deepak Namburi, Vetrivelan Tamilmani, A. Arjun, V. Maniar, and Rami Reddy Kothamaram, “Middleware Architectures for Hybrid and Multi-cloud Environments: A Survey of Scalability and Security Approaches,” Asian Journal of Research in Computer Science, vol. 19, no. 1, pp. 106–120, Jan. 2026, doi: https://doi.org/10.9734/ajrcos/2026/v19i1808.
[5] Prajkta Waditwar, “De-Risking Returns: How AI Can Reinvent Big Tech’s China-Tied Reverse Supply Chains,” Open Journal of Business and Management, vol. 14, no. 01, pp. 104–124, Dec. 2026, doi: https://doi.org/10.4236/ojbm.2026.141007.
[6] Achuthananda Reddy Polu, B. Narra, Navya Vattikonda, A. K. Gupta, V. Kumar, and Hari, AI-POWERED SYNTHETIC COGNITION NETWORKS Leveraging Multi-Agent Machine Learning to Simulate and Optimize Human Decision-Making in Complex Crisis Scenarios. Global Pen Press UK, pp. 1-171, 2025.
[7] B. Narra et al., “Applications of Blockchain in Software Engineering: Enhancing Security, Traceability, and Transparency,” International Journal of Innovative Computer Science and IT Research, vol. 1, no. 2, pp. 63–75, 2025.
[8] A. R. Polu et al., “Analyzing the Role of Analytics in Insurance Risk Management: A Systematic Review of Process Improvement and Business Agility,” International Research Journal of Economics and Management Studies, vol. 2, no. 3, pp. 325-332, 2025.
[9] A. Attipalli, R. Kendyala, J. Kurma, J. V. Mamidala, V. Bitkuri, and S. J. Enokkaren, “Survey on Evolution of Java Web Technologies and Best Practices: from Servlets to Microservices,” Asian Journal of Research in Computer Science, vol. 18, no. 11, pp. 172–187, Nov. 2025, doi: https://doi.org/10.9734/ajrcos/2025/v18i11786.
[10] Jaya Vardhani Mamidala et al., “Explainable Machine Learning Models for Malware Identification in Modern Computing Systems,” European Journal of Applied Science Engineering and Technology, vol. 3, no. 5, pp. 153–170, Oct. 2025, doi: https://doi.org/10.59324/ejaset.2025.3(5).13
[11] Raghuvaran Kendyala, Jagan Kurma, Jaya Vardhani Mamidala, Sunil Jacob Enokkaren, Avinash Attipalli, and Varun Bitkuri, “Framework based on Machine Learning for Lung Cancer Prognosis with Big Data-Driven,” European Journal of Technology, vol. 9, no. 1, pp. 68–85, Oct. 2025, doi: https://doi.org/10.47672/ejt.2787.
[12] Varun Bitkuri et al., Predictive Governance Machine Learning for Public Policy and Administration, 1st ed, Global Pen Press UK, 2025.
[13] V. Maniar, R. R. Kothamaram, D. Rajendran, V. D. Namburi, V. Tamilmani, and A. A. S. Singh, “A Comprehensive Survey on Digital Transformation and Technology Adoption Across Small and Medium Enterprises,” European Journal of Applied Science, Engineering and Technology, vol. 3, no. 6, pp. 238–250, Dec. 2025, doi: https://doi.org/10.59324/ejaset.2025.3(6).18.
[14] Vetrivelan Tamilmani et al., “Automated Cloud Migration Pipelines: Trends, Tools, and Best Practices–A Survey,” Journal of Computer Science and Technology Studies, vol. 7, no. 11, pp. 121-134, 2025. https://doi.org/10.32996/jcsts.2025.7.11.14
[15] Sunil Jacob Enokkaren et al., Autonomous Frontiers AI at the Edge of Mobility and Transportation, Caneda Global Journal Group, pp. 1-212, 2025.
[16] Sri Krishna Kireeti Nandiraju et al., “Towards Early Forecast of Diabetes Mellitus via Machine Learning Systems in Healthcare,” European Journal of Technology, vol. 9, no. 1, pp. 35-50, 2025.
[17] M. Penmetsa, J. R. Bhumireddy, S. R. Vangala, R. M. Polam, B. Kamarthapu, and R. Chalasani, “Adversarial Machine Learning in Cybersecurity: A Review on Defending Against AI-Driven Attacks,” SSRN Electronic Journal, 2025, doi: https://doi.org/10.2139/ssrn.5515383.
[18] Ram Mohan Polam, Bhavana Kamarthapu, Mitra Penmetsa, Jayakeshav Reddy Bhumireddy, R. Chalasani, and Srikanth Reddy Vangala, “Advanced Machine Learning for Robust Botnet Attack Detection in Evolving Threat Landscapes,” Asian Journal of Research in Computer Science, vol. 18, no. 8, pp. 1–14, Aug. 2025, doi: https://doi.org/10.9734/ajrcos/2025/v18i8735.
[19] Bhavana Kamarthapu, Mitra Penmetsa, Jayakeshav Reddy Bhumireddy, R. Chalasani, Srikanth Reddy Vangala, and Ram Mohan Polam, “Data-Driven Detection of Network Threats Using Advanced Machine Learning Techniques for Cybersecurity,” International Journal of Applied Information Systems, vol. 13, no. 1, pp. 37–44, Aug. 2025, doi: https://doi.org/10.5120/ijais2025452028.
[20] M. Penmetsa, J. R. Bhumireddy, R. Chalasani, S. R. Vangala, R. M. Polam, and B. Kamarthapu, “Effectiveness of Deep Learning Algorithms in Phishing Attack Detection for Cybersecurity Frameworks,” Journal of Data Analysis and Information Processing, vol. 13, no. 03, pp. 331–346, 2025, doi: https://doi.org/10.4236/jdaip.2025.133021.
[21] R. M. Polam, B. Kamarthapu, A. B. Kakani, S. K. K. Nandiraju, S. K. Chundru, and S. R. Vangala, “Predictive Modeling for Property Insurance Premium Estimation Using Machine Learning Algorithms,” SSRN Electronic Journal, 2025, doi: https://doi.org/10.2139/ssrn.5515382.
[22] Ajay Babu Kakani, K. Kireeti, Sandeep Kumar Chundru, Srikanth Reddy Vangala, Ram Mohan Polam, and Bhavana Kamarthapu, “Leveraging NLP and Sentiment Analysis for ML-Based Fake News Detection with Big Data,” SSRN Electronic Journal, Jan. 2025, doi: https://doi.org/10.2139/ssrn.5515418.
[23] Prajkta Waditwar, “Quantum-Enhanced Travel Procurement: Hybrid Quantum–Classical Optimization for Enterprise Travel Management,” World Journal of Advanced Engineering Technology and Sciences, vol. 17, no. 3, pp. 375–386, Dec. 2025, doi: https://doi.org/10.30574/wjaets.2025.17.3.1572.
[24] Ajay Babu Kakani, K. Kireeti, Sandeep Kumar Chundru, Srikanth Reddy Vangala, Ram Mohan Polam, and Bhavana Kamarthapu, “Big Data and Predictive Analytics for Customer Retention: Exploring the Role of Machine Learning in E-Commerce,” International Journal of Emerging Trends in Computer Science and Information Technology, vol. 2, no. 1, pp. 26–34, Jan. 2021, doi: https://doi.org/10.63282/3050-9246.ijetcsit-v2i2p104.
[25] P. Waditwar, “Reimagining procurement payments: From transactional bottlenecks to strategic value creation,” World Journal of Advanced Research and Reviews, vol. 28, no. 1, pp. 588–598, Oct. 2025, doi: https://doi.org/10.30574/wjarr.2025.28.1.3480.
[26] P. Waditwar, “Agentic AI and sustainable procurement: Rethinking anti-corrosion strategies in oil and gas,” World Journal of Advanced Research and Reviews, vol. 27, no. 3, pp. 1591–1598, Sep. 2025, doi: https://doi.org/10.30574/wjarr.2025.27.3.3298.
[27] P. Waditwar, “Overcoming the AI Data Eclipse: Obstacles to the Full Adoption of Artificial Intelligence in the Procurement Technology Sector,” World Journal of Advanced Research and Reviews, vol. 27, no. 3, pp. 1583–1590, Sep. 2025, doi: https://doi.org/10.30574/wjarr.2025.27.3.3296.
[28] P. Waditwar, “Leading through the Synthetic Media Era: Platform Governance to Curb AI-Generated Fake News, Protect the Public, and Preserve Trust,” Open Journal of Leadership, vol. 14, no. 03, pp. 403–418, 2025, doi: https://doi.org/10.4236/ojl.2025.143020.
[29] P. Waditwar, “Agentic AI in Contract Analytics Harnessing Machine Learning for Risk Assessment and Compliance in Government Procurement Contracts,” Open Journal of Business and Management, vol. 13, no. 05, pp. 3385–3395, 2025, doi: https://doi.org/10.4236/ojbm.2025.135179.
[30] Prajkta Waditwar, “AI-Driven Smart Negotiation Assistant for Procurement—An Intelligent Chatbot for Contract Negotiation Based on Market Data and AI Algorithms,” Journal of Data Analysis and Information Processing, vol. 13, no. 02, pp. 140–155, Jan. 2025, doi: https://doi.org/10.4236/jdaip.2025.132009.
[31] P. Waditwar, “Smart Procurement in the Sports Industry: A Strategic Approach for Efficiency and Performance Enhancement,” Open Journal of Business and Management, vol. 13, no. 03, pp. 1743–1761, 2025, doi: https://doi.org/10.4236/ojbm.2025.133090.
[32] Prajkta Waditwar, “Transforming Government Procurement through Electronic Bidding—A Case Study on the City of Somerville’s Implementation of BidExpress Infotech,” Open Journal of Leadership, vol. 14, no. 01, pp. 165–175, Jan. 2025, doi: https://doi.org/10.4236/ojl.2025.141007.
[33] P. Waditwar, “AI-Driven Procurement in Ayurveda and Ayurvedic Medicines & Treatments,” Open Journal of Business and Management, vol. 13, no. 03, pp. 1854–1879, 2025, doi: https://doi.org/10.4236/ojbm.2025.133096.
[34] N. Rao, “The Roadmap to Mainframe Modernization: Bridging Legacy Systems with the Cloud,” International Journal of Scientific Research in Computer Science Engineering and Information Technology, vol. 11, no. 1, pp. 125–133, Jan. 2025, doi: https://doi.org/10.32628/cseit25111214.
[35] Narasimha Rao Vanaparthi, “Why digital transformation in fintech requires mainframe modernization: A cost-benefit analysis,” International Journal of Science and Research Archive, vol. 14, no. 1, pp. 1052–1062, Jan. 2025, doi: https://doi.org/10.30574/ijsra.2025.14.1.0161.
[36] N. R. Vanaparthi, “INTELLIGENT FINANCE: HOW AI IS RESHAPING THE FUTURE OF FINANCIAL SERVICES,” INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING AND TECHNOLOGY, vol. 16, no. 1, pp. 126–137, Jan. 2025, doi: https://doi.org/10.34218/ijcet_16_01_012.
[37] Narasimha Rao Vanaparthi, “REGULATORY COMPLIANCE IN THE DIGITAL AGE: HOW MAINFRAME MODERNIZATION CAN SUPPORT FINANCIAL INSTITUTIONS,” vol. 8, no. 1, pp. 383–396, Jan. 2025, doi: https://doi.org/10.34218/IJRCAIT_08_01_033.
[38] Subramanya Shashank Gollapudi Venkata, “SECURE SOFTWARE DEVELOPMENT: INTEGRATING ENCRYPTION PROTOCOLS FROM DESIGN TO DEPLOYMENT,” International Journal of Applied Mathematics, vol. 38, no. 2s, pp. 1190–1213, Oct. 2025, doi: https://doi.org/10.12732/ijam.v38i2s.714.
[39] S. S. Gollapudi Venkata, “From Code to Cloud: Navigating The Future of SoftwareEngineering and Testing Automation,” International Journal of Basic and Applied Sciences, vol. 14, no. 6, pp. 63–70, Oct. 2025, doi: https://doi.org/10.14419/1zwgxp78.
[40] S. S. gollapudi Venkata, “Audit: Risk Aware Software Security,” The AI Audit: Accountability, Integrity, and the Future of Finance, pp. 67–75, Jul. 2025, doi: https://doi.org/10.48001/978-81-988770-4-8-7.
[41] H. Kohli, A. Hadi, N. Mukhi, M. A. Miah, and K. Bushra Siddiqa, “Energy-Aware Intelligent Computing Framework for Sustainable AI Workloads in Next-Generation Smart Systems,” International Journal on Smart & Sustainable Intelligent Computing, vol. 2, no. 4, pp. 34–47, Jan. 2026, doi: https://doi.org/10.63503/j.ijssic.2025.201.
[42] K. K. Routhu, “Next-Generation Workforce Planning: AI-Enabled Forecasting and Strategic HR in Mergers and Acquisitions,” Journal of Artificial Intelligence, Machine Learning and Data Science, vol. 3, no. 4, pp. 2962–2967, Oct. 2025, doi: https://doi.org/10.51219/jaimld/kranthi-kumar-routhu/615.
[43] A. Jain, S. S. M. Kotha, S. Bhambri, and H. Kohli, “Machine Learning Pre-trained Language Models for English-French Neural Machine Translation using Topsis,” 2025 IEEE International Conference on Contemporary Computing and Communications (InC4), pp. 1–6, Mar. 2025, doi: https://doi.org/10.1109/inc465408.2025.11256193.
[44] K. Agarwal, S. Bhambri, V. K. Sridharan, N. Mohammed, H. Kohli, and J. A. Kapoor, “Performance Evaluation of different Machine Learning Techniques for Pothole Detection,” 2025 IEEE International Conference on Contemporary Computing and Communications (InC4), pp. 1–8, Mar. 2025, doi: https://doi.org/10.1109/inc465408.2025.11256464.
[45] H. Kohli, S. P. Mokashi, P. Sundaramoorthy, D. Jangid, and K. Chaganti, “AI-NLP Framework for Customer Segmentation and Personalized Recommendations in Digital Marketing Environments,” 2025 IEEE 4th World Conference on Applied Intelligence and Computing (AIC), pp. 146–151, Jul. 2025, doi: https://doi.org/10.1109/aic66080.2025.11211918.
[46] K. K. Routhu, “From Reactive to Predictive: A Strategic Framework for Attrition Analytics with Oracle 23AI,” European Journal of Advances in Engineering and Technology, vol. 12, no. 1, pp. 29-34, 2025.
[47] A. K. Gupta, “Leveraging deep learning models for intrusion detection systems for secure networks,” Journal of Computer Science and Technology Studies, vol. 6, no. 2, pp. 199-208, 2024. Doi: https://doi.org/10.32996/jcsts.2024.6.2.22
[48] B. Narra, D. V. Kumar Reddy Buddula, H. H. Sudheer Patchipulusu, N. Vattikonda, A. K. Gupta, and A. Reddy Polu, “The Integration of Artificial Intelligence in Software Development: Trends, Tools, and Future Prospects,” International Journal of Innovative Research in Multidisciplinary Education, vol. 03, no. 12, Dec. 2024, doi: https://doi.org/10.58806/ijirme.2024.v3i12n17.
[49] A. Reddy Polu, B. Narra, D. V. Kumar Reddy Buddula, H. H. Sudheer Patchipulusu, N. Vattikonda, and A. K. Gupta, “Evaluating Machine Learning Approaches for Personalized Movie Recommendations: A Comprehensive Analysis,” International Journal of Innovative Research in Multidisciplinary Education, vol. 03, no. 12, Dec. 2024, doi: https://doi.org/10.58806/ijirme.2024.v3i12n18.
[50] P. Waditwar, “The Intersection of Strategic Sourcing and Artificial Intelligence: A Paradigm Shift for Modern Organizations,” Open Journal of Business and Management, vol. 12, no. 06, pp. 4073–4085, 2024, doi: https://doi.org/10.4236/ojbm.2024.126204.
[51] Varun Bitkuri et al., “A Survey on Blockchain-Enabled ERP Systems for Secure Supply Chain Processes and Cloud Integration,” International Journal of Technology Management and Humanities, vol. 10, no. 02, pp. 52–65, Jun. 2024, doi: https://doi.org/10.21590/ijtmh.2024100209.
[52] Jaya Vardhani Mamidala et al., “Machine Learning Approaches to Salary Prediction in Human Resource Payroll Systems,” Journal of Computer Science and Technology Studies, vol. 7, no. 10, pp. 528–536, Oct. 2025, doi: https://doi.org/10.32996/jcsts.2025.7.10.52.
[53] P. Waditwar, “AI for Bathsheba Syndrome: Ethical Implications and Preventative Strategies,” Open Journal of Leadership, vol. 13, no. 03, pp. 321–341, 2024, doi: https://doi.org/10.4236/ojl.2024.133020.
[54] M. Zeeshan, K. Bhadauria, L. Pahal, P. Nagrath, and D. Kalla, “Ensemble-Based Deep Learning for Automated Diabetic- Retinopathy Detection Using CNNs and Transfer Learning,” Lecture Notes in Networks and Systems, pp. 216–228, 2026, doi: https://doi.org/10.1007/978-3-032-08859-8_16.
[55] A. Aggarwal, L. Agarwal, B. P. R. Rella, N. Nagpal, D. Kalla, and M. Sharma, “A Performance Comparison of Machine Learning Models for Rain Prediction,” Lecture Notes in Networks and Systems, pp. 319–328, Oct. 2025, doi: https://doi.org/10.1007/978-3-032-03527-1_25.
[56] Preeti Nagrath, I. Saini, M. Zeeshan, Komal, Komal, and D. Kalla, “Predicting Mental Health Disorders with Variational Autoencoders,” Lecture notes in networks and systems, pp. 38–51, Oct. 2025, doi: https://doi.org/10.1007/978-3-032-03751-0_4.
[57] Rahul Vadisetty, Anand Polamarasetti, V. Varadarajan, D. Kalla, and G. K. Ramanathan, “Cyber Warfare and AI Agents: Strengthening National Security Against Advanced Persistent Threats (APTs),” Communications in computer and information science, pp. 578–587, Oct. 2025, doi: https://doi.org/10.1007/978-3-032-07373-0_43.
