Skip to main content
SpringerLink
Log in

Explainable AI-Based Interface System for Weather Forecasting Model

  • Conference paper
  • First Online:
HCI International 2023 – Late Breaking Papers (HCII 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14059))

Included in the following conference series:

  • 686 Accesses

Abstract

Machine learning (ML) is becoming increasingly popular in meteorological decision-making. Although the literature on explainable artificial intelligence (XAI) is growing steadily, user-centered XAI studies have not extend to this domain yet. This study defines three requirements for explanations of black-box models in meteorology through user studies: statistical model performance for different rainfall scenarios to identify model bias, model reasoning, and the confidence of model outputs. Appropriate XAI methods are mapped to each requirement, and the generated explanations are tested quantitatively and qualitatively. An XAI interface system is designed based on user feedback. The results indicate that the explanations increase decision utility and user trust. Users prefer intuitive explanations over those based on XAI algorithms even for potentially easy-to-recognize examples. These findings can provide evidence for future research on user-centered XAI algorithms, as well as a basis to improve the usability of AI systems in practice.

Supported by the Korean Institute of Information & Communications Technology Planning & Evaluation (IITP) and the Korean Ministry of Science and ICT(MSIT) under grant agreement No. 2019-0-00075 (Artificial Intelligence Graduate School Program (KAIST)) and No. 2022-0-00984 (Development of Plug-and-Play Explainable Artificial Intelligence Method), and from the Korea Meteorological Administration (KMA) and Korean National Institute of Meteorological Sciences (NIMS) under grant agreement No. KMA2021-00123 (Developing Intelligent Assistant Technology and Its Application for Weather Forecasting Process).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6, 52138–52160 (2018)

    Article  Google Scholar 

  2. Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.R., Samek, W.: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS ONE 10(7), e0130140 (2015)

    Article  Google Scholar 

  3. Başağaoğlu, H., et al.: A review on interpretable and explainable artificial intelligence in hydroclimatic applications. Water 14(8), 1230 (2022)

    Article  Google Scholar 

  4. Bhatt, U., Weller, A., Moura, J.M.: Evaluating and aggregating feature-based model explanations. arXiv preprint arXiv:2005.00631 (2020)

  5. Bradley, C., et al.: Explainable artificial intelligence (XAI) user interface design for solving a Rubik’s Cube. In: Stephanidis, C., Antona, M., Ntoa, S., Salvendy, G. (eds.) HCI International 2022-Late Breaking Posters: 24th International Conference on Human-Computer Interaction, HCII 2022, Virtual Event, 26 June–1 July 2022, Proceedings, Part II, pp. 605–612. Springer, Heidelberg (2022). https://doi.org/10.1007/978-3-031-19682-9_76

  6. Chaput, R., Cordier, A., Mille, A.: Explanation for humans, for machines, for human-machine interactions? In: Explainable Agency in Artificial Intelligence WS, AAAI-2021 (2021)

    Google Scholar 

  7. Chromik, M., Butz, A.: Human-XAI interaction: a review and design principles for explanation user interfaces. In: Ardito, C., Lanzilotti, R., Malizia, A., Petrie, H., Piccinno, A., Desolda, G., Inkpen, K. (eds.) INTERACT 2021. LNCS, vol. 12933, pp. 619–640. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85616-8_36

    Chapter  Google Scholar 

  8. Dell, M., Jones, B.F., Olken, B.A.: What do we learn from the weather? The new climate-economy literature. J. Econ. Lit. 52(3), 740–798 (2014)

    Article  Google Scholar 

  9. Ding, Z., Han, X., Liu, P., Niethammer, M.: Local temperature scaling for probability calibration. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6889–6899 (2021)

    Google Scholar 

  10. Espeholt, L., et al.: Deep learning for twelve hour precipitation forecasts. Nat. Commun. 13(1), 5145 (2022)

    Article  Google Scholar 

  11. van der Geest, K., et al.: The impacts of climate change on ecosystem services and resulting losses and damages to people and society. In: Mechler, R., Bouwer, L.M., Schinko, T., Surminski, S., Linnerooth-Bayer, J.A. (eds.) Loss and Damage from Climate Change. CRMPG, pp. 221–236. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-72026-5_9

    Chapter  Google Scholar 

  12. Gilpin, L.H., Bau, D., Yuan, B.Z., Bajwa, A., Specter, M., Kagal, L.: Explaining explanations: an overview of interpretability of machine learning. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 80–89. IEEE (2018)

    Google Scholar 

  13. Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: International Conference on Machine Learning, pp. 1321–1330 (2017)

    Google Scholar 

  14. Haynes, K., Lagerquist, R., McGraw, M., Musgrave, K., Ebert-Uphoff, I.: Creating and evaluating uncertainty estimates with neural networks for environmental-science applications. In: Artificial Intelligence for the Earth Systems, pp. 1–58 (2023)

    Google Scholar 

  15. Kalnay, E.: Atmospheric Modeling, Data Assimilation and Predictability. Cambridge University Press (2003)

    Google Scholar 

  16. Kim, C., Yun, S.Y.: Precipitation nowcasting using grid-based data in South Korea region. In: 2020 International Conference on Data Mining Workshops (ICDMW), pp. 701–706. IEEE (2020)

    Google Scholar 

  17. Ko, J., Lee, K., Hwang, H., Oh, S.G., Son, S.W., Shin, K.: Effective training strategies for deep-learning-based precipitation nowcasting and estimation. Comput. Geosci. 161, 105072 (2022)

    Article  Google Scholar 

  18. Korea Meteorological Agency: Haneulsarang (2022). https://www.kma.go.kr/download_01/kma_202002.pdf

  19. Liao, Q.V., Gruen, D., Miller, S.: Questioning the AI: informing design practices for explainable AI user experiences. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–15 (2020)

    Google Scholar 

  20. Liao, Q.V., Pribić, M., Han, J., Miller, S., Sow, D.: Question-driven design process for explainable AI user experiences. arXiv preprint arXiv:2104.03483 (2021)

  21. Liao, Q.V., Varshney, K.R.: Human-centered explainable AI (XAI): from algorithms to user experiences. arXiv preprint arXiv:2110.10790 (2021)

  22. McGovern, A., Ebert-Uphoff, I., Gagne, D.J., Bostrom, A.: Why we need to focus on developing ethical, responsible, and trustworthy artificial intelligence approaches for environmental science. Environ. Data Sci. 1, e6 (2022)

    Article  Google Scholar 

  23. McGovern, A., Gagne, D.J., Williams, J.K., Brown, R.A., Basara, J.B.: Enhancing understanding and improving prediction of severe weather through spatiotemporal relational learning. Mach. Learn. 95, 27–50 (2014)

    Article  MathSciNet  Google Scholar 

  24. Mizutori, M., Guha-Sapir, D.: Economic losses, poverty and disasters 1998–2017. United Nations Office for Disaster Risk Reduction, vol. 4, pp. 9–15 (2017)

    Google Scholar 

  25. Murdoch, W.J., Singh, C., Kumbier, K., Abbasi-Asl, R., Yu, B.: Definitions, methods, and applications in interpretable machine learning. Proc. Natl. Acad. Sci. 116(44), 22071–22080 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  26. Naeini, M.P., Cooper, G.F., Hauskrecht, M.: Obtaining well calibrated probabilities using Bayesian Binning. In: 2015 Proceedings of the AAAI Conference on Artificial Intelligence, pp. 2901–2907 (2015)

    Google Scholar 

  27. Nauta, M., et al.: From anecdotal evidence to quantitative evaluation methods: a systematic review on evaluating explainable AI. arXiv preprint arXiv:2201.08164 (2022)

  28. Platt, J.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Advances in Large Margin Classifiers, vol. 10, no. 3, pp. 61–74 (1999)

    Google Scholar 

  29. Rasp, S., Thuerey, N.: Data-driven medium-range weather prediction with a Resnet pretrained on climate simulations: a new model for WeatherBench. J. Adv. Model. Earth Syst. 13(2), e2020MS002405 (2021)

    Article  Google Scholar 

  30. Ravuri, S., et al.: Skilful precipitation nowcasting using deep generative models of radar. Nature 597(7878), 672–677 (2021)

    Article  Google Scholar 

  31. Ren, X., et al.: Deep learning-based weather prediction: a survey. Big Data Res. 23, 100178 (2021)

    Article  Google Scholar 

  32. Roebber, P.J.: Visualizing multiple measures of forecast quality. Weather Forecast. 24(2), 601–608 (2009)

    Article  Google Scholar 

  33. Samek, W., Binder, A., Montavon, G., Lapuschkin, S., Müller, K.R.: Evaluating the visualization of what a deep neural network has learned. IEEE Trans. Neural Netw. Learn. Syst. 28(11), 2660–2673 (2016)

    Article  MathSciNet  Google Scholar 

  34. Schwalbe, G., Finzel, B.: A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts. Data Min. Knowl. Disc., 1–59 (2023). https://doi.org/10.1007/s10618-022-00867-8

  35. Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017)

    Google Scholar 

  36. Shin, Y., Kim, J.H., Chun, H.Y., Jang, W., Son, S.W.: Classification of synoptic patterns with mesoscale mechanisms for downslope windstorms in Korea using a self-organizing map. J. Geophys. Res. Atmos. 127(6), e2021JD035867 (2022)

    Article  Google Scholar 

  37. Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034 (2013)

  38. Sønderby, C.K., et al..: MetNet: a neural weather model for precipitation forecasting. arXiv preprint arXiv:2003.12140 (2020)

  39. Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. In: International Conference on Machine Learning, pp. 3319–3328. PMLR (2017)

    Google Scholar 

  40. Yun, S.: Development of short-term precipitation prediction technology using artificial intelligence. Atmos. Res. 237, 104845 (2021)

    Google Scholar 

  41. Zadrozny, B., Elkan, C.P.: Obtaining calibrated probability estimates from decision trees and Naive Bayesian classifiers. In: International Conference on Machine Learning (2001)

    Google Scholar 

  42. Zhongming, Z., Linong, L., Xiaona, Y., Wangqiang, Z., Wei, L., et al.: Atlas of mortality and economic losses from weather, climate and water extremes (1970–2019). Weather Climate Water Temps Climate EAU (2021)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea

    Soyeon Kim, Junho Choi, Subeen Lee, Artyom Stitsyuk, Minkyoung Park, Seongyeop Jeong & Jaesik Choi

  2. SI-Analytics, Daejeon, Korea

    Yeji Choi

  3. National Institute of Meteorological Sciences (NIMS), Jeju, 63568, Korea

    You-Hyun Baek

  4. INEEJI, Gyeonggi, Korea

    Jaesik Choi

Authors
  1. Soyeon Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Junho Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yeji Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Subeen Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Artyom Stitsyuk
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Minkyoung Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Seongyeop Jeong
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. You-Hyun Baek
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jaesik Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jaesik Choi .

Editor information

Editors and Affiliations

  1. Siemens Corporation, Princeton, NJ, USA

    Helmut Degen

  2. Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Stavroula Ntoa

  3. San Jose State University, San Jose, CA, USA

    Abbas Moallem

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kim, S. et al. (2023). Explainable AI-Based Interface System for Weather Forecasting Model. In: Degen, H., Ntoa, S., Moallem, A. (eds) HCI International 2023 – Late Breaking Papers. HCII 2023. Lecture Notes in Computer Science, vol 14059. Springer, Cham. https://doi.org/10.1007/978-3-031-48057-7_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-48057-7_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48056-0

  • Online ISBN: 978-3-031-48057-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation