Transformation of Meteorological Fire Data into Interactive Narrative Using Large Language Gemini Model Through Telegram Chatbot

cahyo adi nugroho; Giarno

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Keywords:

Chatbot Telegram

Large Language Model (LLM)

Natural Language

Understanding (NLU)

Two-Stage Pipeline

Abstract

Background: Weather information plays an important role in supporting people's daily activities, including travel, agriculture, outdoor activities, and decision-making. However, raw meteorological data delivered through Application Programming Interface (API) services is commonly presented in JSON format, which is highly technical and difficult for the general public to interpret.
Aims: This research aims to democratize access to raw weather data by developing an interactive and personalized Telegram chatbot that serves as an intermediary system (middleware) between users and external weather data APIs. The system is designed to provide weather information in a natural, contextual, and user-friendly form.
Methods: The proposed system implements a Two-Stage Large Language Model (LLM) Pipeline architecture utilizing the Gemini 3.1 Pro model. The architecture separates the LLM workload into two specialized stages. The first stage, the Router Engine, functions as a probabilistic semantic classifier to identify and categorize user intentions. The second stage, the Formatter Engine, acts as a contextual text generator that processes factual JSON weather data obtained from external APIs and transforms it into concise, casual, and understandable narratives
Result: The evaluation results demonstrate high system stability, with context recognition accuracy reaching 98.8%. The proposed processing architecture also achieves network response latency below 2.5 seconds. Furthermore, the graceful degradation mechanism demonstrates strong fault tolerance when the system encounters API quota limitations.
Conclusion: The separation of cognitive roles within the LLM, combined with external factual data extraction, effectively reduces cognitive barriers to weather information and mitigates the risk of hallucination.
References
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3. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623. https://doi.org/10.1145/3442188.3445922
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8. Demuth, J. L., Morss, R. E., Lazo, J. K., & Hilderbrand, D. C. (2012). Everyday perceptions and uses of weather forecasts. Bulletin of the American Meteorological Society, 93(7), 992-1004. https://doi.org/10.1175/BAMS-D-11-00144.1
9. Deriu, J., Rodrigo, A., Otegi, A., Echegoyen, G., Rosset, S., Agirre, E., & Cieliebak, M. (2021). Survey on evaluation methods for dialogue systems. Artificial Intelligence Review, 54(1), 755-810. https://doi.org/10.1007/s10462-020-09866-x
10. Firmansyah, A. (2022). The design of an IoT-based automatic weather station with a self-energy feature for monitoring environmental conditions. Journal of Information Technology and Computer Science, 9(5), 1037-1046. https://doi.org/10.25126/jtiik.2022956037
11. Følstad, A., & Brandtzæg, P. B. (2017). Chatbots and the new world of HCI. interactions, 24(4), 38-42. https://doi.org/10.1145/3085558
12. Gkatzia, D., Hastie, H., & Lemon, O. (2016). Natural language generation for meteorology: A systematic literature review. Computational Linguistics, 42(4), 605-645. https://doi.org/10.1162/COLI_a_00261
13. Hopcroft, J. E., Motwani, R., & Ullman, J. D. (2001). Introduction to automata theory, languages, and computation (2nd ed.). Addison-Wesley.
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19. Khot, T., Trivedi, H., Finlayson, M., Fu, Y., Richardson, K., Clark, P., & Sabharwal, A. (2022). Decomposed Prompting: A Modular Approach for Solving Complex Tasks. Proceedings of the International Conference on Learning Representations (ICLR). https://doi.org/10.48550/arXiv.2210.02406
20. Kocaballi, A. B., Berkovsky, S., Quiroz, J. C., Laranjo, L., Tong, H. L., Rezazadegan, D.,… & Coiera, E. (2019). The personalization of conversational agents in health care: Systematic review. Journal of Medical Internet Research, 21(11), e15360. https://doi.org/10.2196/15360
21. Kristianto, A., & Rani, A. P. (2019). The Relationship of Atmospheric Boundary Layer Altitude with Weather Conditions Based on Vertical Wind Profiles Based on Radisonde Observations, Weather Radar and WRF-ARW Model Output. Journal of Meteorology, Climatology and Geophysics, 5(1), 1–8. https://doi.org/10.36754/jmkg.v5i1.62
22. Lazo, J. K., Morss, R. E., & Demuth, J. L. (2009). 300 Billion Served: Sources, Perceptions, Uses, and Values of Weather Forecasts. Bulletin of the American Meteorological Society, 90(6), 785-798. https://doi.org/10.1175/2008BAMS2604.1
23. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N.,… & Kiela, D. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems, 33, 9459-9474. https://doi.org/10.48550/arXiv.2005.11401
24. Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023). Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys, 55(9), 1-35. https://doi.org/10.1145/3560815
25. McTear, M. (2020). Conversational AI: Dialogue Systems, Conversational Agents, and Chatbots. Morgan & Claypool Publishers. https://doi.org/10.2200/S01060ED1V01Y202010HLT048
26. Navajyoti. (2020). Chatbot for Weather Information Using RASA: Implementing Natural Language Understanding for Contextual AI Assistants. Navajyoti Journal, 5(1). https://navajyotijournal.org/August_2020/CHATBOT%20FOR%20WEATHER%20INFORMATION%20USING%20RASA%20_%20Aug_2020_18.pdf
27. Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P.,… & Lowe, R. (2022). Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35, 27730-27744. https://doi.org/10.48550/arXiv.2203.02155
28. Pautasso, C., Zimmermann, O., & Leymann, F. (2008). RESTful web services vs. “big” web services: Making the right architectural decision. Proceedings of the 17th International Conference on World Wide Web, 805-814. https://doi.org/10.1145/1367497.1367606
29. Peng, B., Li, C., He, P., Galley, M., & Gao, J. (2023). Instruction Tuning with GPT-4. arXiv preprint. https://doi.org/10.48550/arXiv.2304.03277
30. Pramono, A. H., et al. (2023). Social Media as an Instrument for Extreme Weather Information Governance: A Case Study of the DIY Coast. Gea Journal of Geography, 23(1), 1-10. https://doi.org/10.21009/jgg.151.01
31. Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI blog, 1(8), 9. https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf
32. Rapp, A., Curto, C., & Cena, F. (2021). The impact of designing a chatbot with human-like conversational capabilities on user engagement and cognitive load. Computers in Human Behavior, 122, 106820. https://doi.org/10.1016/j.chb.2021.106820
33. Reiter, E., & Dale, R. (2000). Building Natural Language Generation Systems. Cambridge University Press. https://doi.org/10.1017/CBO9780511527010
34. Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L.,… & Scialom, T. (2023). Toolformer: Language Models Can Teach Themselves to Use Tools. Advances in Neural Information Processing Systems, 36. https://doi.org/10.48550/arXiv.2302.04761
35. Schramm, P. J., Brown, L. M., Hossain, M. A., & Ebi, K. L. (2021). Communication and dissemination of climate data and weather information for public health protection. Journal of Environmental Health, 83(6), 26-31. https://www.neha.org/jeh
36. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N.,… & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998-6008. https://doi.org/10.48550/arXiv.1706.03762
37. Wang, C., Liu, S., Yang, H., Guo, J., Wu, Y., & Liu, J. (2023). Robust intent classification with large language models. Information Processing & Management, 60(5), 103422. https://doi.org/10.1016/j.ipm.2023.103422
38. Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E.,… & Zhou, D. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. Advances in Neural Information Processing Systems, 35, 24824-24837. https://doi.org/10.48550/arXiv.2201.11903
39. White, J., Hays, S., Fu, Q., Spencer-Smith, J., Schmidt, D. C., & White, G. W. (2023). A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT. arXiv preprint arXiv:2302.11382. https://doi.org/10.48550/arXiv.2302.11382
40. Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y.,… & Wen, J. R. (2023). A Survey of Large Language Models. arXiv preprint arXiv:2303.18223. https://doi.org/10.48550/arXiv.2303.18223

How to Cite

nugroho, cahyo adi, & Giarno. (2026). Transformation of Meteorological Fire Data into Interactive Narrative Using Large Language Gemini Model Through Telegram Chatbot. Journal of Earth and Planetary System Science, 1(1), 52–61. Retrieved from https://journal.gomit.id/jepss/article/view/161

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[1] Adamopoulou, E., & Moussiades, L. (2020). Chatbots: History, technology, and applications. Machine Learning with Applications, 2, 100006. https://doi.org/10.1016/j.mlwa.2020.100006

[2] Arif, A. M., et al. (2025). The Role of Interpersonal Communication in the Delivery of Weather and Early Warning Information by BMKG Juanda: A Case Study on Aviation and Public Services. Journal of Multidisciplinary Research of the Nation, 1(12), 2150–2164. https://doi.org/10.59837/jpnmb.v1i12.426

[3] Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623. https://doi.org/10.1145/3442188.3445922

[4] Brandtzaeg, P. B., & Følstad, A. (2017). Why people use chatbots. Internet Research, 27(4), 777-798. https://doi.org/10.1108/IntR-12-2016-0389

[5] Bray, T. (2014). The JavaScript Object Notation (JSON) Data Interchange Format (RFC 7159). Internet Engineering Task Force (IETF). https://doi.org/10.17487/RFC7159

[6] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P.,… & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877-1901. https://doi.org/10.48550/arXiv.2005.14165

[7] Chen, M., Song, X., Li, C., Wang, C., Qiu, D., & Wang, M. (2023). API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 3102-3116. https://doi.org/10.48550/arXiv.2304.08244

[8] Demuth, J. L., Morss, R. E., Lazo, J. K., & Hilderbrand, D. C. (2012). Everyday perceptions and uses of weather forecasts. Bulletin of the American Meteorological Society, 93(7), 992-1004. https://doi.org/10.1175/BAMS-D-11-00144.1

[9] Deriu, J., Rodrigo, A., Otegi, A., Echegoyen, G., Rosset, S., Agirre, E., & Cieliebak, M. (2021). Survey on evaluation methods for dialogue systems. Artificial Intelligence Review, 54(1), 755-810. https://doi.org/10.1007/s10462-020-09866-x

[10] Firmansyah, A. (2022). The design of an IoT-based automatic weather station with a self-energy feature for monitoring environmental conditions. Journal of Information Technology and Computer Science, 9(5), 1037-1046. https://doi.org/10.25126/jtiik.2022956037

[11] Følstad, A., & Brandtzæg, P. B. (2017). Chatbots and the new world of HCI. interactions, 24(4), 38-42. https://doi.org/10.1145/3085558

[12] Gkatzia, D., Hastie, H., & Lemon, O. (2016). Natural language generation for meteorology: A systematic literature review. Computational Linguistics, 42(4), 605-645. https://doi.org/10.1162/COLI_a_00261

[13] Hopcroft, J. E., Motwani, R., & Ullman, J. D. (2001). Introduction to automata theory, languages, and computation (2nd ed.). Addison-Wesley.

[14] Huang, J., & Chang, K. C. C. (2022). Towards Reasoning in Large Language Models: A Survey. Findings of the Association for Computational Linguistics: ACL 2023, 1049-1065. https://doi.org/10.18653/v1/2023.findings-acl.67

[15] Hunter, J. D. (2007). Matplotlib: A 2D graphics environment. Computing in Science & Engineering, 9(3), 90-95. https://doi.org/10.1109/MCSE.2007.55

[16] Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y.,… & Fung, P. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12), 1-38. https://doi.org/10.1145/3571730

[17] Jurafsky, D., & Martin, J. H. (2023). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition (3rd ed. draft). Stanford University. https://web.stanford.edu/~jurafsky/slp3/

[18] Kasner, Z., & Dušek, O. (2022). Neural pipeline for zero-shot data-to-text generation. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, 3914–3932. https://doi.org/10.18653/v1/2022.acl-long.272

[19] Khot, T., Trivedi, H., Finlayson, M., Fu, Y., Richardson, K., Clark, P., & Sabharwal, A. (2022). Decomposed Prompting: A Modular Approach for Solving Complex Tasks. Proceedings of the International Conference on Learning Representations (ICLR). https://doi.org/10.48550/arXiv.2210.02406

[20] Kocaballi, A. B., Berkovsky, S., Quiroz, J. C., Laranjo, L., Tong, H. L., Rezazadegan, D.,… & Coiera, E. (2019). The personalization of conversational agents in health care: Systematic review. Journal of Medical Internet Research, 21(11), e15360. https://doi.org/10.2196/15360

[21] Kristianto, A., & Rani, A. P. (2019). The Relationship of Atmospheric Boundary Layer Altitude with Weather Conditions Based on Vertical Wind Profiles Based on Radisonde Observations, Weather Radar and WRF-ARW Model Output. Journal of Meteorology, Climatology and Geophysics, 5(1), 1–8. https://doi.org/10.36754/jmkg.v5i1.62

[22] Lazo, J. K., Morss, R. E., & Demuth, J. L. (2009). 300 Billion Served: Sources, Perceptions, Uses, and Values of Weather Forecasts. Bulletin of the American Meteorological Society, 90(6), 785-798. https://doi.org/10.1175/2008BAMS2604.1

[23] Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N.,… & Kiela, D. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems, 33, 9459-9474. https://doi.org/10.48550/arXiv.2005.11401

[24] Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023). Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys, 55(9), 1-35. https://doi.org/10.1145/3560815

[25] McTear, M. (2020). Conversational AI: Dialogue Systems, Conversational Agents, and Chatbots. Morgan & Claypool Publishers. https://doi.org/10.2200/S01060ED1V01Y202010HLT048

[26] Navajyoti. (2020). Chatbot for Weather Information Using RASA: Implementing Natural Language Understanding for Contextual AI Assistants. Navajyoti Journal, 5(1). https://navajyotijournal.org/August_2020/CHATBOT%20FOR%20WEATHER%20INFORMATION%20USING%20RASA%20_%20Aug_2020_18.pdf

[27] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P.,… & Lowe, R. (2022). Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35, 27730-27744. https://doi.org/10.48550/arXiv.2203.02155

[28] Pautasso, C., Zimmermann, O., & Leymann, F. (2008). RESTful web services vs. “big” web services: Making the right architectural decision. Proceedings of the 17th International Conference on World Wide Web, 805-814. https://doi.org/10.1145/1367497.1367606

[29] Peng, B., Li, C., He, P., Galley, M., & Gao, J. (2023). Instruction Tuning with GPT-4. arXiv preprint. https://doi.org/10.48550/arXiv.2304.03277

[30] Pramono, A. H., et al. (2023). Social Media as an Instrument for Extreme Weather Information Governance: A Case Study of the DIY Coast. Gea Journal of Geography, 23(1), 1-10. https://doi.org/10.21009/jgg.151.01

[31] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI blog, 1(8), 9. https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf

[32] Rapp, A., Curto, C., & Cena, F. (2021). The impact of designing a chatbot with human-like conversational capabilities on user engagement and cognitive load. Computers in Human Behavior, 122, 106820. https://doi.org/10.1016/j.chb.2021.106820

[33] Reiter, E., & Dale, R. (2000). Building Natural Language Generation Systems. Cambridge University Press. https://doi.org/10.1017/CBO9780511527010

[34] Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L.,… & Scialom, T. (2023). Toolformer: Language Models Can Teach Themselves to Use Tools. Advances in Neural Information Processing Systems, 36. https://doi.org/10.48550/arXiv.2302.04761

[35] Schramm, P. J., Brown, L. M., Hossain, M. A., & Ebi, K. L. (2021). Communication and dissemination of climate data and weather information for public health protection. Journal of Environmental Health, 83(6), 26-31. https://www.neha.org/jeh

[36] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N.,… & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998-6008. https://doi.org/10.48550/arXiv.1706.03762

[37] Wang, C., Liu, S., Yang, H., Guo, J., Wu, Y., & Liu, J. (2023). Robust intent classification with large language models. Information Processing & Management, 60(5), 103422. https://doi.org/10.1016/j.ipm.2023.103422

[38] Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E.,… & Zhou, D. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. Advances in Neural Information Processing Systems, 35, 24824-24837. https://doi.org/10.48550/arXiv.2201.11903

[39] White, J., Hays, S., Fu, Q., Spencer-Smith, J., Schmidt, D. C., & White, G. W. (2023). A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT. arXiv preprint arXiv:2302.11382. https://doi.org/10.48550/arXiv.2302.11382

[40] Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y.,… & Wen, J. R. (2023). A Survey of Large Language Models. arXiv preprint arXiv:2303.18223. https://doi.org/10.48550/arXiv.2303.18223

Research Articles | Vol. 1 Issue 1 (2026) | Published in Aug 30, 2026

Transformation of Meteorological Fire Data into Interactive Narrative Using Large Language Gemini Model Through Telegram Chatbot

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