AI-Driven Lightning Strike Prediction Using Polymer-Integrated Sensor Platforms for Climate-Resilient Energy Systems in India

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Year : 2026 | Volume : 14 | 02 | Page :
    By

    Kusum Tharani,

  • K. Sudha,

  • Surinder Kaur,

  • Manish Talwar,

  • Neeraj Kumar,

  1. Professor, Department of Electrical and Electronics Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
  2. Associate Professor, Department of Electrical and Electronics Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
  3. Associate Professor, Department of Information Technology, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
  4. Assistant Professor, Department of Instrumentation and Control Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
  5. Associate Professor, Department of Electrical and Electronics Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India

Abstract

Lightning strikes are a major climate-related threat to India, resulting in severe human injuries as well as regular damages to the power transmission network and renewable energy infrastructure. This research aims to introduce the concept of an AI-based lightning strike prediction and mitigation system with the integration of polymers for making climate-resilient energy infrastructure. Multidata are collected based on satellite images, climate variables, as well as surface-based sensing modules, and are further processed with machine learning algorithms such as the Random Forest approach, Convolutional Neural Network models, and Long Short-Term Memory network models for accurate spatial-temporal predictions of lightning strikes. One of the primary contributions of this research work is the adaptation of advanced polymer and composite materials. Conductive polymers and polymer nanocomposites are proposed to be identified and analyzed for developing lightweight lightning sensors and protectors. The increased electronic conductivity and dielectric strength of these materials enable their effective use in smart grid applications. The development of AI-based predictive systems and the adaptation of advanced polymer and composite technologies and materials can improve lightning warning systems and create smart and reliable energy systems in India. The development of advanced AI-based predictive systems provides an increased degree of accuracy and reliability. The increased accuracy of AI-based systems reduces the reliance on manual calculations.

Keywords: Lightning Prediction, Artificial Intelligence, Machine Learning, Conductive Polymers, Smart Materials, Smart grid protection.

How to cite this article:
Kusum Tharani, K. Sudha, Surinder Kaur, Manish Talwar, Neeraj Kumar. AI-Driven Lightning Strike Prediction Using Polymer-Integrated Sensor Platforms for Climate-Resilient Energy Systems in India. Journal of Polymer & Composites. 2026; 14(02):-.
How to cite this URL:
Kusum Tharani, K. Sudha, Surinder Kaur, Manish Talwar, Neeraj Kumar. AI-Driven Lightning Strike Prediction Using Polymer-Integrated Sensor Platforms for Climate-Resilient Energy Systems in India. Journal of Polymer & Composites. 2026; 14(02):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=239665


References

[1] Bansal, R., Rai, T. & Budhiraja, V., 2025. Polymer Integrated Smart Biomedical Waste Management System Using AI and Sensor Based Segregation. Journal of Polymer and Composites, 13(04), pp.172–184. DOI: https://doi.org/10.37591/JOPC.v13i04.215040

[2] Gantla, H.R., Prasad, K.R., Mohanta, H.C., Kumar, G.A., Lanka, V.S.S.P.L.N.B. & Reshma, V.K., 2025. Real Time Air Quality Prediction Using IoT Integrated Polymer Sensors and Recurrent Neural Networks. Journal of Polymer and Composites, 13(06), pp.332–347. Available at: https://journals.stmjournals.com/jopc/article=2025/view=228943

[3] Karuppusamy, M., Thirumalaisamy, R., Palanisamy, S., Nagamalai, S., Massoud, E.E.S. & Ayrilmis, N., 2025. A review of machine learning applications in polymer composites: advancements, challenges, and future prospects. Journal of Materials Chemistry A, 13, pp.16290–16308. DOI: https://doi.org/10.1039/D5TA00982K

[4] Bao, R., Zhang, Y., Ma, B.J., Zhang, Z. & He, Z., 2022. An Artificial Neural Network for Lightning Prediction Based on Atmospheric Electric Field Observations. Remote Sensing, 14(17), p.4131. DOI: https://doi.org/10.3390/rs14174131

[5] Zhang, H., Liu, Y., Zhang, C. & Li, N., 2025. Machine Learning Methods for Weather Forecasting: A Survey. Atmosphere, 16(1), p.82. DOI: https://doi.org/10.3390/atmos16010082

[6] X. Wang et al., 2023. A Survey of Deep Learning Based Lightning Prediction. Atmosphere, 14(11), p.1698. DOI: https://doi.org/10.3390/atmos14111698

[7] Mandal, J., Chatterjee, C. & Das, S., 2024. An explainable machine learning technique to forecast lightning density over North Eastern India. Journal of Atmospheric and Solar Terrestrial Physics, 259, p.106255. DOI: https://doi.org/10.1016/j.jastp.2024.106255

[8] Baidya, K., Roy, A. & Das, K., 2024. A review of polymer-matrix piezoelectric composite coatings for energy harvesting and smart sensors. Journal of Coatings Technology and Research, 21, pp.55–85. DOI: https://doi.org/10.1007/s11998-023-00819-x

[9] Audus, D.J. & de Pablo, J.J., 2017. Polymer informatics: Opportunities and challenges. ACS Macro Letters, 6(10), pp.1078–1082. DOI: https://doi.org/10.1021/acsmacrolett.7b00228

[10] Chen, L., Pilania, G., Batra, R. & Huan, T.D., 2021. Polymer informatics: Current status and critical next steps. Materials Science and Engineering: R, 144, p.100595. DOI: https://doi.org/10.1016/j.mser.2020.100595

[11] Tao, L., Varshney, V. & Li, Y., 2021. Benchmarking Machine Learning Models for Polymer Informatics: Example of Glass Transition Temperature. Journal of Chemical Information and Modeling, 61, pp.5395–5413. DOI: https://doi.org/10.1021/acs.jcim.1c01031

[12] Mostajabi, F., Chen, G., et al., 2023. Machine-learning-based investigation of the variables affecting summertime lightning occurrence over the Southern Great Plains. Atmospheric Chemistry and Physics, 23, pp.14547 14568. DOI: https://doi.org/10.5194/acp-23-14547-2023

[13] Arifin, M.S., Sakib, A.N., Rayhan, Y. & Hashem, T., 2025. Lightning Prediction under Uncertainty: DeepLight with Hazy Loss. Preprint (arXiv). Available at: https://arxiv.org/abs/2508.07428

[14] Leinonen, J., Hamann, U. & Germann, U., 2022. Seamless lightning nowcasting with recurrent-convolutional deep learning. Preprint (arXiv). Available at: https://arxiv.org/abs/2203.10114

[15] Bansal, R., Tharani, K., Rai, T. & Budhiraja, V., 2025. Polymer-Integrated AI Systems for Automated Engineering Applications. Journal of Polymer and Composites, 13(04), pp.172–184. DOI: https://doi.org/10.37591/JOPC.v13i04.215040

[16] Wang, Y., Fan, Y. & Zhupanska, O.I., 2024. Challenges and future recommendations for lightning strike damage assessments of composites: laboratory testing and predictive modeling. Materials (Basel), 17(3), p.744. DOI: https://doi.org/10.3390/ma17030744

[17] Review and Analysis of Application Of Conductive Polymer Composites In Power-Efficient RF Circuits for 5G PAPR Reduction, 2025. Journal of Polymer and Composites, 13(03). Available at: https://journals.stmjournals.com/jopc/article=2025/view=0

[18] Karuppiah G, Kuttalam KC, Palaniappan M, Santulli C, Palanisamy S. Multiobjective Optimization of Fabrication Parameters of Jute Fiber/Polyester Composites with Egg Shell Powder and Nanoclay Filler. Molecules. 2020 Nov 27;25(23):5579. DOI: 10.3390/molecules25235579

[19] Santulli C, Palanisamy S, Kalimuthu M (2022) Pineapple fibers, their composites and applications, v Plant Fibers, their Composites, and Appl, Elsevier 323–346. https:/ /doi.org/ 10.1 016 /B9 78- 0-1 2- 82 45 28-6.00007-2

[20] Goutham, E. R. S., Hussain, S. S., Muthukumar, C., Krishnasamy, S., Kumar, T. S. M, Santulli, C., … and Jesuarockiam, N. (2023). “Drilling parameters and post-drilling residual tensile properties of natural-fiber-reinforced composites: A review,” Journal of Composites Science 7(4), article 136. https://doi.org/10.3390/jcs7040136

[21] Ayrilmis N, Kanat G, Yildiz Avsar E, Palanisamy S, Ashori A. Utilizing waste manhole covers and fibre board as reinforcing fillers for thermoplastic composites. J Reinf Plast Compos 2025; 44:1108–18.

[22] Aruchamy, Karthik & Karuppusamy, Manickaraj & Krishnakumar, Sivasankari & Palanisamy, Sivasubramanian & Jayamani, Manivannan & Sureshkumar, Kumar & Ali, Syed & Alfarraj, Saleh. (2024). Enhancement of mechanical properties of hybrid polymer composites using palmyra palm and coconut sheath fibers: The role of tamarind shell powder. Bio Resources. 20. 698-724. 10.15376/biores.20.1.698-724.

[23] Palanisamy, Sivasubramanian & Mayandi, K. & Dharmalingam, shanmugam & Rajini, N. & Santulli, Carlo & Mohammad, Faruq & Al-Lohedan, Hamad. (2022). Tensile Properties and Fracture Morphology of Acacia Caesia Bark Fibers Treated with Different Alkali Concentrations. Journal of Natural Fibers. 19. 10.1080/15440478.2021.2022562.

Ahead of Print Subscription Original Research
Volume 14
02
Received 23/01/2026
Accepted 13/02/2026
Published 03/04/2026
Publication Time 70 Days
33

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