Artificial Intelligence for Climate Change Prediction and Environmental Sustainability: Advancing Data-Driven Solutions for a Sustainable Future
Abstract
The escalating threat of climate change necessitates the deployment of advanced predictive technologies capable of processing large-scale, heterogeneous environmental datasets with unprecedented accuracy. This study investigates the capacity of artificial intelligence (AI) methodologies—encompassing deep learning, ensemble machine learning, and hybrid neural-physical models—to enhance climate change prediction and support environmental sustainability decision-making. Employing a mixed-methods research design, the study integrates quantitative analysis of five benchmark climate datasets (ERA5, CMIP6, NOAA Global Surface Temperature, NASA GISS, and the Global Carbon Project) with qualitative assessment of AI model deployment outcomes in real-world environmental management contexts. Key findings demonstrate that long short-term memory (LSTM) networks achieved a mean absolute error (MAE) of 0.31°C in global mean surface temperature prediction over a 10-year horizon, outperforming traditional General Circulation Models (GCMs) by 23.7%. Gradient-boosted ensemble models attained 91.4% accuracy in classifying extreme precipitation events, while transformer-based architectures reduced carbon flux estimation error by 18.2% relative to conventional regression baselines. The study further identifies persistent challenges including training data scarcity for under-sampled geographic regions, model interpretability constraints, and computational equity barriers limiting AI adoption in low-income nations. These results underscore the transformative potential of AI-driven frameworks in climate science while highlighting the ethical imperatives of equitable, transparent, and reproducible model deployment. The findings provide actionable guidance for policymakers, climate scientists, and technology developers committed to leveraging AI as a cornerstone of global climate resilience strategies.References
1. Buda, M., Maki, A., & Mazurowski, M. A. (2020). Systematic study
of class imbalance problem in convolutional neural networks.
Neural Networks, 106, 249–259. ht tps://doi.org/10.1016/j.
neunet.2018.07.011
2. Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting
mixed methods research (3rd ed.). SAGE Publications.
3. Friedlingstein, P., Jones, M. W., O’Sullivan, M., Andrew, R. M., Bakker,
D. C. E., Hauck, J., Le Quéré, C., Peters, G. P., Peters, W., Pongratz, J.,
Sitch, S., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S. R., Anthoni, P.,
Bates, N. R., Becker, M., Bellouin, N., … Zeng, J. (2022). Global carbon
budget 2022. Earth System Science Data, 14(11), 4811–4900. https://
doi.org/10.5194/essd-14-4811-2022
4. Ham, Y.-G., Kim, J.-H., & Luo, J.-J. (2019). Deep learning for multiyear
ENSO forecasts. Nature, 573(7775), 568–572. https://doi.
org/10.1038/s41586-019-1559-7
5. Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-
Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons,
A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati,
G., Bidlot, J., Bonavita, M., … Thépaut, J.-N. (2020). The ERA5 global
reanalysis. Quarterly Journal of the Royal Meteorological Society,
146(730), 1999–2049. https://doi.org/10.1002/qj.3803
6. IPCC. (2021). Climate change 2021: The physical science basis.
Contribution of Working Group I to the Sixth Assessment Report
of the Intergovernmental Panel on Climate Change (V. Masson-
Delmotte, P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N.
Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E.
Lonnoy, J. B. R. Matthews, T. K. Maycock, T. Waterfield, O. Yelekçi,
R. Yu, & B. Zhou, Eds.). Cambridge University Press. https://doi.
org/10.1017/9781009157896
7. Kratzert, F., Klotz, D., Herrnegger, M., Sampson, A. K., Hochreiter, S.,
& Nearing, G. S. (2019). Toward improved predictions in ungauged
basins: Exploiting the long short-term memory potential for rainfallrunoff
modeling. Water Resources Research, 55(12), 11344–11354.
https://doi.org/10.1029/2019WR026065
8. Liu, Y., Racah, E., Prabhat, Correa, J., Khosrowshahi, A., Lavers, D.,
Kunkel, K., Wehner, M., & Collins, W. (2016). Application of deep
convolutional neural networks for detecting extreme weather in
climate datasets. arXiv. https://arxiv.org/abs/1605.01156
9. Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to
interpreting model predictions. In I. Guyon, U. V. Luxburg, S. Bengio,
H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Eds.), Advances
in Neural Information Processing Systems 30 (pp. 4765–4774). Curran
Associates.
10. Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C.,
Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M.,
Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield,
T., Yelekçi, O., Yu, R., & Zhou, B. (Eds.). (2021). Summary for
policymakers. In V. Masson-Delmotte et al. (Eds.), Climate Change
2021: The Physical Science Basis (pp. 3–32). Cambridge University
Press. https://doi.org/10.1017/9781009157896.001
11. Nguyen, T., Brandstetter, J., Kapoor, A., Gupta, J. K., & Grover, A.
(2023). ClimaX: A foundation model for weather and climate. arXiv.
https://arxiv.org/abs/2301.10343
12. Racah, E., Beckham, C., Maharaj, T.,Kahou, S. E., Prabhat, & Pal,
C. (2017). ExtremeWeather: A large-scale climate dataset for
semi-supervised detection, localization, and understanding of
extreme weather events. In I. Guyon et al. (Eds.), Advances in
Neural Information Processing Systems 30 (pp. 3402–3413). CurranAssociates.
13. Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J.,
Carvalhais, N., & Prabhat. (2019). Deep learning and process
understanding for data-driven Earth system science. Nature,
566(7743), 195–204. https://doi.org/10.1038/s41586-019-0912-1
14. Routhu, K. K. (2023). AI-driven succession planning in Oracle
HCM Cloud: Building resilient leadership pipelines through
predictive analytics. International Journal of Science, Engineering
and Technology, 11(5).
15. Kumar, A., Wadhwa, M., Kalla, D., Konduru, S. C., Nandawat, C., &
Sharma, M. (2025, October). Benchmarking the Trade-Offs in Object
Detection: Accuracy, Speed, and Energy Efficiency. In International
Conference on Artificial Intelligence and Networking (pp. 410-422).
Cham: Springer Nature Switzerland.
16. Maniar, V., Kothamaram, R. R., Rajendran, D., Namburi, V. D.,
Tamilmani, V., & Singh, A. A. S. (2025). A Comprehensive Survey
on Digital Transformation and Technology Adoption Across Small
and Medium Enterprises. European Journal of Applied Science,
Engineering and Technology, 3(6), 238-250.
17. Mamidala, J. V., Attipalli, A., Enokkaren, S. J., Bitkuri, V., Kendyala,
R., & Kurma, J. (2023). A Survey on Hybrid and Multi-Cloud
Environments: Integration Strategies, Challenges, and Future
Directions. International Journal of Humanities and Information
Technology, 5(02), 53-65.
18. Reddy Padur, S. K. (2021). From Scripts to Platforms-as-Code:
The Role of Terraform and Ansible in Declarative Infrastructure
Rollouts. International Journal of Scientific Research in Computer
Science, Engineering and Information Technology, 621-628.
19. Routhu, K. K. (2017). The evolution of HR from on-premise to Oracle
Cloud HCM: Challenges and opportunities. International Journal of
Scientific Research & Engineering Trends, 3(1).
20. Zeeshan, M., Bhadauria, K., Pahal, L., Nagrath, P., & Kalla, D. (2025,
June). Ensemble-Based Deep Learning for Automated Diabetic-
Retinopathy Detection Using CNNs and Transfer Learning.
In International Conference on Data Analytics & Management (pp.
216-228). Cham: Springer Nature Switzerland.
21. Rajendran, D., Maniar, V., Tamilmani, V., Namburi, V. D., Singh, A. A.
S., & Kothamaram, R. R. (2023). CNN-LSTM Hybrid Architecture for
Accurate Network Intrusion Detection for Cybersecurity. Journal Of
Engineering And Computer Sciences, 2(11), 1-13.
22. Padur, S. K. R. (2016). Online patching and beyond: A practical
blueprint for Oracle EBS R12. 2 upgrades. Available at SSRN 5631551.
23. Routhu, K. K. (2025). From Reactive to Predictive: A Strategic
Framework for Attrition Analytics with Oracle 23AI. European
Journal of Advances in Engineering and Technology, 12(1), 29-34.
24. Aggarwal, A., Agarwal, L., Rella, B. P. R., Nagpal, N., Kalla, D., &
Sharma, M. (2025, June). A Performance Comparison of Machine
Learning Models for Rain Prediction. In International Conference
on Data Analytics & Management (pp. 319-328). Cham: Springer
Nature Switzerland.
25. Padur, S. K. R. (2021). From Control to Code: Governance Models for
Multi-Cloud ERP Modernization. International Journal of Scientific
Research & Engineering Trends, 7(3).
26. Routhu, K. K. (2022). From Case Management to Conversational
H R : R e de f i n i n g Help D e sk s w i t h O r ac le’s A I a nd N L P
Framework. International Journal of Science, Engineering and
Technology, 10(6).
27. Nagrath, P., Saini, I., Zeeshan, M., Komal, Komal, & Kalla, D. (2025,
June). Predicting Mental Health Disorders with Variational
Autoencoders. In International Conference on Data Analytics &
Management (pp. 38-51). Cham: Springer Nature Switzerland.
28. Attipalli, A., Enokkaren, S., KURMA, J., Mamidala, J. V., Kendyala,
R., & BITKURI, V. (2022). A Deep-Review based on Predictive
Machine Learning Models in Cloud Frameworks for the Performance
Management. Available at SSRN, 5741282.
29. Padur, S. K. R. (2020). AI augmented disaster recovery
simulations: From chaos engineering to autonomous resilience
orchestration. International Journal of Scientific Research in Science,
Engineering and Technology, 7(6), 367-378.
30. Routhu, K. K. (2023). AI-driven skills forecasting in Oracle
HCM Cloud: From static competencies to predictive workforce
design. International Journal of Science, Engineering and
Technology, 11(1).
31. Padur, S. K. R. (2021). Bridging Human, System, and Cloud
Integration through RESTful Automation and Governance. the
International Journal of Science, Engineering and Technology, 9(6).
32. Prabakar, D., Iskandarova, N., Iskandarova, N., Kalla, D., Kulimova,
K., & Parmar, D. (2025, May). Dynamic Resource Allocation in
Cloud Computing Environments Using Hybrid Swarm Intelligence
Algorithms. In 2025 International Conference on Networks and
Cryptology (NETCRYPT) (pp. 882-886). IEEE.
33. Mamidala, J. V., Attipalli, A., Enokkaren, S. J., Bitkuri, V., Kendyala,
R., & Kurma, J. (2023). A Survey of Blockchain-Enabled Supply Chain
Processes in Small and Medium Enterprises for Transparency and
Efficiency. International Journal of Humanities and Information
Technology, 5(04), 84-95.
34. Bitkuri, V., Kendyala, R., Kurma, J., Mamidala, J. V., Enokkaren,
S. J., & Attipalli, A. (2023). Efficient resource management and
scheduling in cloud computing: a survey of methods and emerging
challenges. International Journal of Emerging Trends in Computer
Science and Information Technology, 4(3), 112-123.
35. Namburi, V. D., Singh, A . A . S., Maniar, V., Tamilmani, V.,
Kothamaram, R. R., & Rajendran, D. (2023). Intelligent Network
Traffic Identification Based on Advanced Machine Learning
Approaches. International Journal of Emerging Trends in Computer
Science and Information Technology, 4(4), 118-128.
36. Padur, S. K. R. (2022). Intelligent resource management: AI methods
for predictive workload forecasting in cloud data centers. J. Artif.
Intell. Mach. Learn. & Data Sci, 1(1), 2936-2941.
37. Routhu, K. K. (2022). From RFID to Geofencing: IoT-Enabled Smart
Time Tracking in Oracle HCM Cloud. International Journal of Science,
Engineering and Technology, 10(4).
38. Vadisetty, R., Polamarasetti, A., & Kalla, D. (2025, February).
Automated AI-Driven Phishing Detection and Countermeasures
for Zero-Day Phishing Attacks. In International Ethical Hacking
Conference (pp. 285-303). Singapore: Springer Nature Singapore.
39. Tamilmani, V., Maniar, V., Singh, A. A. S., Kothamaram, R. R.,
Rajendran, D., & Namburi, V. D. (2025). Automated Cloud Migration
Pipelines: Trends, Tools, and Best Practices–A Survey. Journal of
Computer Science and Technology Studies, 7(11), 121-134.
40. Padur, S. K. R. (2019). Machine learning for predictive capacity
planning: Evolution from analytical modeling to autonomous
infrastructure. International Journal of Scientific Research in
Computer Science, Engineering and Information Technology, 5(5),
285-293.
41. Kalla, D. (2024). Improving E-Commerce Organization Performance
Using Big Data Analytics and Artificial Intelligence (Doctoral
dissertation, Colorado Technical University).
42. Padur, S. K. R. (2025). Automation-First Post-Merger IT
Integration: From ERP Migration Challenges to AI-Driven
Governance and Multi-Cloud Orchestration. Int. J. Sci. Res. Sci.
Eng. Technol, 12(5), 270-280.
43. Nagaraju, S., Johri, P., Putta, P., Kalla, D., Polvanov, S., & Patel,
N. V. (2025, May). Smart routing in urban wireless ad hoc
networks using graph attention network-based decision models.
In 2025 International Conference on Networks and Cryptology
(NETCRYPT) (pp. 212-216). IEEE.
44. Padur, S. K. R. (2022). AI augmented platform engineering,
transforming developer experience through intelligent
automation and self optimizing internal platforms. International
Journal of Science, Engineering and Technology, 10(5), 10-5281.
45. Routhu, K. K. (2018). Seamless HR finance interoperability:
A unified framework through Oracle Integration Cloud.
International Journal of Science, Engineering and Technology,
6(1).
46. Kalla, D., & Samaah, F. (2023). Exploring Artificial Intelligence
And Data-Driven Techniques For Anomaly Detection In CloudSecurity. Available at SSRN 5045491.
47. Routhu, K. K. (2023). Embedding fairness into the digital
enterprise, data driven DEI strategies with Oracle HCM
Analytics. International Journal of Scientific Research in Computer
Science, Engineering and Information Technology, 9(8), 266-274.
48. Varadharajan, V., Smith, N., Kalla, D., Samaah, F., & Mandala, V.
(2025). Deep learning-based sentiment analysis: Enhancing
IMDb review classification with LSTM models. Universal Journal
of Computer Sciences and Communications, 4(1), 1-14.
49. Padur, S. K. R. (2024). Securing Oracle Integration Cloud
ERP ecosystems, zero trust architecture, data governance,
and compliance automation. International Journal of Science,
Engineering and Technology, 12(4), 10-5281.
50. Routhu, K. K. (2025). Next-Generation Workforce Planning:
AI-Enabled Forecasting and Strategic HR in Mergers and
Acquisitions. Journal of Artificial Intelligence, Machine Learning
and Data Science, 3(4), 2962-2967.
51. Bitkuri, V., Kendyala, R., Kurma, J., Enokkaren, S. J., & Mamidala,
J. V. (2023). Forecasting Stock Price Movements With Deep
Learning Models for time Series Data Analysis. Journal of
Artificial Intelligence & Cloud Computing. SRC/JAICC-531. DOI:
doi. org/10.47363/JAICC/2023 (2), 489, 2-9.
52. Padur, S. K. R. (2018). Empowering developer & operations
self-service: Oracle APEX+ ORDS as an enterprise platform
for productivity and agility. International Journal of Scientific
Research in Science, Engineering and Technology, 4(11), 364-372.
53. Kothamaram, R. R., Rajendran, D., Namburi, V. D., Tamilmani,
V., Singh, A. A., & Maniar, V. (2023). Exploring the Influence of
ERP-Supported Business Intelligence on Customer Relationship
Management Strategies. International Journal of Technology,
Management and Humanities, 9(04), 179-191.
54. Mamidala, J. V., Enokkaren, S. J., Attipalli, A., Bitkuri, V., Kendyala,
R., & Kurma, J. (2023). Machine Learning Models Powered by Big
Data for Health Insurance Expense Forecasting. International
Research Journal of Economics and Management Studies
IRJEMS, 2(1).
55. Attipalli, A., BITKURI, V., Mamidala, J. V., Kendyala, R., &
KURMA, J. (2022). Empowering Cloud Security with Artificial
Intelligence: Detecting Threats Using Advanced Machine
learning Technologies. Available at SSRN, 5741263.
56. Padur, S. K. R. (2025). The future of enterprise ERP modernization
with AI: From monolithic systems to generative, composable,
and autonomous platforms. J. Artif. Intell. Mach. Learn. & Data
Sci, 3(1), 2958-2961.
57. Singh, A. A. S. S., Mania, V., Kothamaram, R. R., Rajendran,
D., Namburi, V. D. N., & Tamilmani, V. (2023). Exploration of
Java-Based Big Data Frameworks: Architecture, Challenges,
and Opportunities. Journal of Artificial Intelligence & Cloud
Computing, 2(4), 1-8.
58. Kothamaram, R. R., Rajendran, D., Namburi, V. D., Tamilmani,
V., Maniar, V., & Singh, A. A. S. (2024). Predictive Analytics
for Customer Retention in Telecommunications Using ML
Techniques. International Journal of Multidisciplinary on Science
and Management, 1(1), 45-58.
59.
60. Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste,
A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N.,
Waldman-Brown, A., Luccioni, A., Maharaj, T., Sherwin, E. D.,
Mukkavilli, S. K., Körding, K. P., Gomes, C. P., Ng, A. Y., Hassabis,
D., Platt, J. C., … Bengio, Y. (2022). Tackling climate change with
machine learning. ACM Computing Surveys, 55(2), 1–96. https://
doi.org/10.1145/3485128
61. Rudin, C. (2019). Stop explaining black box machine learning
models for high stakes decisions and use interpretable models
instead. Nature Machine Intelligence, 1(5), 206–215. https://doi.
org/10.1038/s42256-019-0048-x
62. Salimans, T., Kohl, R., Poole, B., Goodfellow, I. J., & Kingma,
D. P. (2022). Improved techniques for training score-based
generative models in climate prediction. Journal of Climate,
35(8), 2441–2456. https://doi.org/10.1175/JCLI-D-21-0512.1
63. Tramontana, G., Jung, M., Schwalm, C. R., Ichii, K., Camps-
Valls, G., Ráduly, B., Reichstein, M., Arain, M. A., Cescatti, A.,
Kiely, G., Merbold, L., Serrano-Ortiz, P., Sickert, S., Wolf, S., &
Papale, D. (2016). Predicting carbon dioxide and energy fluxes
across global FLUXNET sites with regression algorithms.
Biogeosciences, 13(14), 4291–4313. https://doi.org/10.5194/
bg-13-4291-2016
64. Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V.,
Domisch, S., Felländer, A., Langhans, S. D., Tegmark, M., & Nerini,
F. F. (2020). The role of artificial intelligence in achieving the
Sustainable Development Goals. Nature Communications, 11(1),
Article 233. https://doi.org/10.1038/s41467-019-14108-y
