Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

LightNet+: A dual-source lightning forecasting network with bi-direction spatiotemporal transformation

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Lightning disaster causes a huge threat to human lives and industrial facilities. Data-driven lightning forecasting plays an effective role in alleviating such disaster losses. The forecasting process usually faces multi-source meteorological data characterized by spatiotemporal structure. However, established data-driven forecasting methods are mostly built on classic convolutional and recurrent neural blocks which processes one local neighborhood at a time, failing to capture long-range spatiotemporal dependencies within data. To address this issue, we propose a dual-source lightning forecasting network with bi-direction spatiotemporal transformation, referred to as LightNet\(+\). The core of LightNet\(+\) is a novel module, namely bi-directional spatiotemporal propagator, which aims to model long-range connections among different spatiotemporal locations, going beyond the constraints of the receptive field of a local neighborhood. Moreover, a spatiotemporal encoder is introduced to extract historical trend information from recent observation data. Finally, all the obtained features are organically fused via a non-local spatiotemporal decoder, which then produces final forecasting results. We evaluate LightNet\(+\) on a real-world lightning dataset from North China and compare it with several state-of-the-art data-driven lightning forecasting methods. Experimental results show that the proposed LightNet\(+\) yields overall best performance.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. Convolutional Neural Network

  2. Recurrent Neural Network

  3. The WRF model is generally run on a supercomputer platform. So it is almost impossible to directly process raw WRF input data for our limited computation resources.

  4. https://github.com/Andyflying/LightNet-plus

References

  1. Betz HD, Schmidt K, Oettinger WP, Montag B (2008) Cell-tracking with lightning data from linet. Advances in Geosciences 17:55–61

    Article  Google Scholar 

  2. Carion, N, Massa, F, Synnaeve, G, Usunier, N, Kirillov, A, Zagoruyko, S: End-to-end object detection with transformers. ECCV, 2020 (2020)

  3. Chen L, Zhang Y, Lu W, Zheng D, Zhang Y, Chen S, Huang Z (2012) Performance evaluation for a lightning location system based on observations of artificially triggered lightning and natural lightning flashes. Journal of Atmospheric and Oceanic Technology 29(12):1835–1844

    Article  Google Scholar 

  4. Ding C, Liu K, Cheng F, Belyaev E (2021) Spatio-temporal attention on manifold space for 3d human action recognition. Applied Intelligence 51(1):560–570

    Article  Google Scholar 

  5. Geng, YA, Li, Q, Lin, T, Jiang, L, Xu, L, Zheng, D, Yao, W, Lyu, W, Zhang, Y: Lightnet: A dual spatiotemporal encoder network model for lightning prediction. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 2439–2447 (2019)

  6. Goodfellow, I, Bengio, Y, Courville, A, Bengio, Y: Deep Learning, vol 1. MIT Press Cambridge (2016)

  7. Jalali, SMJ, Ahmadian, S, Kavousi-Fard, A, Khosravi, A, Nahavandi, S: Automated deep cnn-lstm architecture design for solar irradiance forecasting. IEEE Transactions on Systems, Man, and Cybernetics: Systems (2021)

  8. Jalali, SMJ, Ahmadian, S, Khodayar, M, Khosravi, A, Ghasemi, V, Shafie-khah, M, Nahavandi, S, Catalão, JP: Towards novel deep neuroevolution models: chaotic levy grasshopper optimization for short-term wind speed forecasting. Engineering with Computers pp 1–25 (2021)

  9. Jalali, SMJ, Ahmadian, S, Khosravi, A, Shafie-khah, M, Nahavandi, S, Catalao, JP: A novel evolutionary-based deep convolutional neural network model for intelligent load forecasting. IEEE Transactions on Industrial Informatics (2021)

  10. Ke, G, Meng, Q, Finley, T, Wang, T, Chen, W, Ma, W, Ye, Q, Liu, TY: Lightgbm: A highly efficient gradient boosting decision tree. In: Advances in neural information processing systems, pp 3146–3154 (2017)

  11. Kingma, DP, Ba, J: Adam: A method for stochastic optimization. ICLR, 2014 (2014)

  12. Kohn M, Galanti E, Price C, Lagouvardos K, Kotroni V (2011) Nowcasting thunderstorms in the mediterranean region using lightning data. Atmospheric Research 100(4):489–502

    Article  Google Scholar 

  13. Li W, Qie X, Fu S, Su D, Shen Y (2016) Simulation of quasi-linear mesoscale convective systems in northern china: Lightning activities and storm structure. Advances in Atmospheric Sciences 33(1):85–100

    Article  Google Scholar 

  14. Lin T, Li Q, Geng YA, Jiang L, Xu L, Zheng D, Yao W, Lyu W, Zhang Y (2019) Attention-based dual-source spatiotemporal neural network for lightning forecast. IEEE Access 7:158296–158307

    Article  Google Scholar 

  15. Liu, C, Heckman, S: The application of total lightning detection and cell tracking for severe weather prediction. In: 91st American meteorological society annual meeting, pp 1–10 (2011)

  16. McCaul EW Jr, Goodman SJ, LaCasse KM, Cecil DJ (2009) Forecasting lightning threat using cloud-resolving model simulations. Weather and Forecasting 24(3):709–729

    Article  Google Scholar 

  17. Michalon N, Nassif A, Saouri T, Royer J, Pontikis C (1999) Contribution to the climatological study of lightning. Geophysical Research Letters 26(20):3097–3100

    Article  Google Scholar 

  18. Noh, H, Hong, S, Han, B: Learning deconvolution network for semantic segmentation. In: Proceedings of the IEEE international conference on computer vision, pp 1520–1528 (2015)

  19. Pielke Sr, R.A.: Mesoscale meteorological modeling. Academic Press (2013)

  20. Price C, Rind D (1992) A simple lightning parameterization for calculating global lightning distributions. Journal of Geophysical Research: Atmospheres 97(D9):9919–9933

    Article  Google Scholar 

  21. Schön, C, Dittrich, J, Müller, R: The error is the feature: How to forecast lightning using a model prediction error. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 2979–2988 (2019)

  22. Shen, B, Liang, X, Ouyang, Y, Liu, M, Zheng, W, Carley, KM: Stepdeep: A novel spatial-temporal mobility event prediction framework based on deep neural network. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 724–733 (2018)

  23. Shi, X, Chen, Z, Wang, H, Yeung, DY, Wong, WK, Woo, WC: Convolutional lstm network: A machine learning approach for precipitation nowcasting. Advances in Neural Information Processing Systems 28, 802–810 (2015)

  24. Shi, X, Gao, Z, Lausen, L, Wang, H, Yeung, DY, Wong, WK, Woo, WC: Deep learning for precipitation nowcasting: A benchmark and a new model. In: Advances in neural information processing systems, pp 5617–5627 (2017)

  25. Skamarock, WC, Klemp, JB, Dudhia, J, Gill, DO, Barker, DM, Duda, MG, Huang, XY, Wang, W, Powers, JG: G.: A description of the advanced research wrf version 3. In: NCAR Tech. Note NCAR/TN-475+ STR. Citeseer (2008)

  26. Vaswani, A, Shazeer, N, Parmar, N, Uszkoreit, J, Jones, L, Gomez, AN, Kaiser, Ł, Polosukhin, I: Attention is all you need. In: Advances in neural information processing systems, pp 5998–6008 (2017)

  27. Wang D, Ma Q, Wang N, Fan X, Lu M, Liu H (2021) Aonet: Active offset network for crowd flow prediction. Engineering Applications of Artificial Intelligence 97:104022

    Article  Google Scholar 

  28. Wang, X, Girshick, R, Gupta, A, He, K: Non-local neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7794–7803 (2018)

  29. Wang, Y, Jiang, L, Yang, MH, Li, LJ, Long, M, Fei-Fei, L: Eidetic 3d lstm: A model for video prediction and beyond. In: International conference on learning representations (2018)

  30. Wang, Y, Long, M, Wang, J, Gao, Z, Philip, SY: Predrnn: Recurrent neural networks for predictive learning using spatiotemporal lstms. In: Advances in neural information processing systems, pp 879–888 (2017)

  31. Wang, Y, Zhang, J, Zhu, H, Long, M, Wang, J, Yu, PS: Memory in memory: A predictive neural network for learning higher-order non-stationarity from spatiotemporal dynamics. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 9154–9162 (2019)

  32. Wang Dongfang Sun Zhuling, YS: Beijing broadband lightning network and the spatiotemporal evolution of lightning flashes during a thunderstorm. Chinese J Atmospheric Sci., 44(4), 851–864 (2019)

  33. Wong, J, Barth, M, Noone, D: Evaluating a lightning parameterization based on cloud-top height for mesoscale numerical model simulations. Geoscientific Model Development 6(2) (2013)

  34. Xu L, Zhang Y, Wang F, Zheng D (2014) Simulation of the electrification of a tropical cyclone using the wrf-arw model: An idealized case. Journal of Meteorological Research 28(3):453–468

    Article  Google Scholar 

  35. Zhang W, Meng Q, Ma M, Zhang Y (2011) Lightning casualties and damages in china from 1997 to 2009. Natural Hazards 57(2):465–476

    Article  Google Scholar 

  36. Zheng D, Zhang Y, Meng Q, Chen L, Dan J (2016) Climatological comparison of small-and large-current cloud-to-ground lightning flashes over southern china. Journal of Climate 29(8):2831–2848

    Article  Google Scholar 

  37. Zhou K, Zheng Y, Dong W, Wang T (2020) A deep learning network for cloud-to-ground lightning nowcasting with multisource data. Journal of Atmospheric and Oceanic Technology 37(5):927–942

    Article  Google Scholar 

  38. Zhu, X, Hu, H, Lin, S, Dai, J: Deformable convnets v2: More deformable, better results. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 9308–9316 (2019)

  39. Zhu, X, Su, W, Lu, L, Li, B, Wang, X, Dai, J: Deformable detr: Deformable transformers for end-to-end object detection. ICLR, 2021 (2020)

Download references

Acknowledgements

This work is supported in part by the National Key Research and Development Program of China (No. 2017YFC1501503) and the Fundamental Research Funds for the Central Universities under Grant (No. 2020JBZD010).

Author information

Authors and Affiliations

  1. Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China

    Xinyuan Zhou, Yangli-ao Geng, Haomin Yu & Qingyong Li

  2. State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China

    Liangtao Xu, Wen Yao & Dong Zheng

  3. Department of Atmospheric and Oceanic Sciences, Fudan University, Shanghai, China

    Yijun Zhang

Authors
  1. Xinyuan Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yangli-ao Geng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Haomin Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qingyong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Liangtao Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Wen Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Dong Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yijun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qingyong Li.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, X., Geng, Ya., Yu, H. et al. LightNet+: A dual-source lightning forecasting network with bi-direction spatiotemporal transformation. Appl Intell 52, 11147–11159 (2022). https://doi.org/10.1007/s10489-021-03089-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-021-03089-5

Keywords

Search

Navigation