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Will a tropical cyclone make landfall?

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Abstract

In the different development phases of a tropical cyclone, the most exciting and complex phase is its landfall, which is when a tropical cyclone moves over to the land after crossing the ocean’s coast. The location, time, and intensity at landfall of a tropical cyclone determine the extent of the disaster caused by it. In this work, we investigate a fundamental question: will a tropical cyclone make a landfall? Knowing the answer to this question with high accuracy will have huge benefits as the preparedness for a potential landfall involves mobilizing substantial human and economic resources. To answer this fundamental question, we have used high-resolution reanalysis data ERA5 (ECMWF reanalysis \(5^{th}\) generation) and best track data IBTrACS (International Best Track Archive for Climate Stewardship) to develop a deep learning model that can predict the landfall event in the early phase of a tropical cyclone—in particular, using any 12 hours or 24 hours of data from the first 72 hours of its inception with very high accuracy. We tested the model for six ocean basins of the world and achieved a fivefold accuracy in the range of \(97.6\%\) to \(99.2\%\) across all basins. The model can be trained within 05 to 20 minutes depending on the ocean basin and can predict the above-stated problem within seconds, making it suitable for real-time application.

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Code availability

The datasets used in this study are public. The code and required procedures to download the required dataset and reproduce the results are available at GitHUB—https://github.com/skashodhiya/Will-a-Tropical-Cyclone-Make-Landfall-

Notes

  1. https://www.nhc.noaa.gov/modelsummary.shtml.

  2. https://www.ncdc.noaa.gov/ibtracs/index.php?name=ib-v4-access.

  3. https://cds.climate.copernicus.eu/.

Abbreviations

TCs:

Tropical cyclones

NA:

North Atlantic

NI:

North Indian

SI:

South Indian

WP:

West Pacific

SP:

South Pacific

EP:

East Pacific

ANN:

Artificial neural network

CNN:

Convolutional neural network

RNN:

Recurrent neural network

LSTM:

Long short-term memory networks

IMD:

Indian Meteorological Department

IBTrACS:

International Best Track Archive for Climate Stewardship

ECMEF:

European Centre for Medium-range Weather Forecasts

ERA5:

ECMWF reanalysis version-5

FAR:

False alarm rate

POD:

Probability of detection

FPR:

False positive rate

FNR:

False negative rate

AUC:

Area under the curve

ROC:

Receiver operating characteristics curve

References

  1. Grinsted A, Ditlevsen P, Christensen JH (2019) Normalized us hurricane damage estimates using area of total destruction, 1900-2018. Proceedings of the National Academy of Sciences 116(48), 23942–23946 https://www.pnas.org/content/116/48/23942.full.pdf. https://doi.org/10.1073/pnas.1912277116

  2. Webersik C, Esteban M, Shibayama T (2010) The economic impact of future increase in tropical cyclones in Japan. Natural Hazards 55:233–250. https://doi.org/10.1007/s11069-010-9522-9

    Article  Google Scholar 

  3. Knutson T, Camargo SJ, Chan JCL, Emanuel K, Ho C-H, Kossin J, Mohapatra M, Satoh M, Sugi M, Walsh K, Wu L (2019) Tropical cyclones and climate change assessment: Part i: Detection and attribution. Bull Am Meteorol Soc 100(10):1987–2007. https://doi.org/10.1175/BAMS-D-18-0189.1

    Article  Google Scholar 

  4. Leroux M-D, Wood K, Elsberry RL, Cayanan EO, Hendricks E, Kucas M, Otto P, Rogers R, Sampson B, Yu Z (2018) Recent advances in research and forecasting of tropical cyclone track, intensity, and structure at landfall. Tropical Cyclone Res Rev 7(2):85–105. https://doi.org/10.6057/2018TCRR02.02

    Article  Google Scholar 

  5. Hall TM, Jewson S (2007) Statistical modeling of north atlantic tropical cyclone tracks. Tellus 59A:486–498. https://doi.org/10.1111/j.1600-0870.2007.00240.x

    Article  Google Scholar 

  6. Gupta V, Mittal M, Mittal V, Chaturvedi Y (2022) Detection of r-peaks using fractional fourier transform and principal component analysis. J Ambient Intell Humanized Comput 13(2):961–972

    Article  Google Scholar 

  7. Mia M, Roy S, Das SK, Rahman M et al (2020) Mango leaf disease recognition using neural network and support vector machine. Iran J Computer Sci 3(3):185–193

    Article  Google Scholar 

  8. Akman M, Uçar MK, Uçar Z, Uçar K, Baraklı B, Bozkurt MR (2022) Determination of body fat percentage by gender based with photoplethysmography signal using machine learning algorithm. IRBM 43(3):169–186. https://doi.org/10.1016/j.irbm.2020.12.003

    Article  Google Scholar 

  9. Chen R, Wang X, Zhang W, Zhu X, Li A, Yang C (2019) A hybrid cnn-lstm model for typhoon formation forecasting. Geoinformatica 23(3):375–396. https://doi.org/10.1007/s10707-019-00355-0

    Article  Google Scholar 

  10. Moradi Kordmahalleh M, Gorji Sefidmazgi M, Homaifar A (2016) A sparse recurrent neural network for trajectory prediction of atlantic hurricanes. GECCO ’16, pp. 957–964. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/2908812.2908834

  11. Chaudhuri S, Basu D, Das D, Goswami S, Varshney S (2017) Swarm intelligence and neural nets in forecasting the maximum sustained wind speed along the track of tropical cyclones over bay of bengal. Natural Hazards. https://doi.org/10.1007/s11069-017-2824-4

    Article  Google Scholar 

  12. Giffard-Roisin S, Yang M, Charpiat G, Kumler Bonfanti C, Kégl B, Monteleoni C (2020) Tropical cyclone track forecasting using fused deep learning from aligned reanalysis data. Front Big Data 3:1. https://doi.org/10.3389/fdata.2020.00001

    Article  Google Scholar 

  13. Boussioux L, Zeng C, Guénais T, Bertsimas D (2020) Hurricane forecasting: A novel multimodal machine learning framework. http://arxiv.org/abs/2011.06125

  14. Alemany S, Beltran J, Perez A, Ganzfried S (2018) Predicting hurricane trajectories using a recurrent neural network. Proceedings of the AAAI Conference on Artificial Intelligence 33. https://doi.org/10.1609/aaai.v33i01.3301468

  15. Gao S, Zhao P, Pan B, Li Y, Zhou M, Xu J, Zhong S, Shi Z (2018) A nowcasting model for the prediction of typhoon tracks based on a long short term memory neural network. Acta Oceanologica Sinica 37:8–12. https://doi.org/10.1007/s13131-018-1219-z

    Article  Google Scholar 

  16. Kim S, Kim H, Lee J, Yoon S, Kahou SE, Kashinath K, Prabhat M (2019) Deep-hurricane-tracker: Tracking and forecasting extreme climate events, pp. 1761–1769. https://doi.org/10.1109/WACV.2019.00192

  17. Kumar S, Biswas K, Pandey AK (2021) Track prediction of tropical cyclones using long short-term memory network. In: 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0251–0257. https://doi.org/10.1109/CCWC51732.2021.9376027

  18. Pradhan R, Aygun RS, Maskey M, Ramachandran R, Cecil DJ (2018) Tropical cyclone intensity estimation using a deep convolutional neural network. IEEE Trans Image Process 27(2):692–702. https://doi.org/10.1109/TIP.2017.2766358

    Article  MathSciNet  MATH  Google Scholar 

  19. Chen B-F, Chen B, Lin H-T, Elsberry RL (2019) Estimating tropical cyclone intensity by satellite imagery utilizing convolutional neural networks. Weather Forecast 34(2):447–465. https://doi.org/10.1175/WAF-D-18-0136.1

    Article  Google Scholar 

  20. Maskey M, Ramachandran R, Ramasubramanian M, Gurung I, Freitag B, Kaulfus A, Bollinger D, Cecil DJ, Miller J (2020) Deepti: Deep-learning-based tropical cyclone intensity estimation system. IEEE J Selected Topics Appl Earth Observ Remote Sens 13:4271–4281. https://doi.org/10.1109/JSTARS.2020.3011907

    Article  Google Scholar 

  21. Shakya S, Kumar S, Goswami M (2020) Deep learning algorithm for satellite imaging based cyclone detection. IEEE J Selected Topics Appl Earth Observ Remote Sens 13:827–839. https://doi.org/10.1109/JSTARS.2020.2970253

    Article  Google Scholar 

  22. Berrisford P, Coauthors (2011) The era-interim archive version 2.0 (1), 23

  23. Vitart F, Anderson D, Stockdale T (2003) Seasonal forecasting of tropical cyclone landfall over mozambique. J Clim 16(23):3932–3945

    Article  Google Scholar 

  24. Wahiduzzaman M, Oliver EC, Wotherspoon SJ, Holbrook NJ (2017) A climatological model of north indian ocean tropical cyclone genesis, tracks and landfall. Clim Dyn 49(7):2585–2603

    Article  Google Scholar 

  25. Mohapatra M, Nayak D, Sharma M, Sharma R, Bandyopadhyay B (2015) Evaluation of official tropical cyclone landfall forecast issued by india meteorological department. Journal of Earth System Science 124. https://doi.org/10.1007/s12040-015-0581-x

  26. Powell MD, Aberson SD (2001) Accuracy of united states tropical cyclone landfall forecasts in the atlantic basin (1976–2000). Bull Am Meteorol Soc 82(12):2749–2768. https://doi.org/10.1175/1520-0477(2001)082<2749:AOUSTC>2.3.CO;2

    Article  Google Scholar 

  27. Kumar S, Biswas K, Pandey AK (2021) Prediction of landfall intensity, location, and time of a tropical cyclone. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 14831–14839

  28. Kumar S, Biswas K, Pandey AK (2021) Predicting landfall’s location and time of a tropical cyclone using reanalysis data. http://arxiv.org/abs/2103.16108

  29. Knapp KR, Kruk MC, Levinson DH, Diamond HJ, Neumann CJ (2010) The international best track archive for climate stewardship (ibtracs): Unifying tropical cyclone data. Bull Am Meteorol Soc 91(3):363–376. https://doi.org/10.1175/2009BAMS2755.1

    Article  Google Scholar 

  30. 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, De Chiara G, Dahlgren P, Dee D, Diamantakis M, Dragani R, Flemming J, Forbes R, Fuentes M, Geer A, Haimberger L, Healy S, Hogan RJ, Hólm E, Janisková M, Keeley S, Laloyaux P, Lopez P, Lupu C, Radnoti G, de Rosnay P, Rozum I, Vamborg F, Villaume S, Thépaut J-N (2020) The era5 global reanalysis. Quarterly Journal of the Royal Meteorological Society 146(730), 1999–2049 https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/qj.3803. https://doi.org/10.1002/qj.3803

  31. McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5(4):115–133

    Article  MathSciNet  MATH  Google Scholar 

  32. Kiefer J, Wolfowitz J (1952) Stochastic estimation of the maximum of a regression function. Ann Math Statist 23(3):462–466. https://doi.org/10.1214/aoms/1177729392

    Article  MathSciNet  MATH  Google Scholar 

  33. Jordan MI (1990) Attractor Dynamics and Parallelism in a Connectionist Sequential Machine, pp. 112–127. IEEE Press, ???

  34. Cleeremans A, Servan-Schreiber D, McClelland JL (1989) Finite state automata and simple recurrent networks. Neural Comput 1(3):372–381. https://doi.org/10.1162/neco.1989.1.3.372

    Article  Google Scholar 

  35. Pearlmutter (1989) Learning state space trajectories in recurrent neural networks. In: International 1989 Joint Conference on Neural Networks, pp. 365–3722

  36. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  37. Gers FA, Schmidhuber J, Cummins F (1999) Learning to forget: continual prediction with lstm. In: 1999 Ninth International Conference on Artificial Neural Networks ICANN 99. (Conf. Publ. No. 470), vol. 2, pp. 850–8552

  38. Gers FA, Schraudolph NN, Schmidhuber J (2003) Learning precise timing with lstm recurrent networks. J Mach Learn Res 3:115–143. https://doi.org/10.1162/153244303768966139

    Article  MathSciNet  MATH  Google Scholar 

  39. Gers FA, Schmidhuber E (2001) Lstm recurrent networks learn simple context-free and context-sensitive languages. IEEE Trans Neural Netw 12(6):1333–1340

    Article  Google Scholar 

  40. LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1(4):541–551. https://doi.org/10.1162/neco.1989.1.4.541

    Article  Google Scholar 

  41. Krizhevsky A, Sutskever I, Hinton G (2012) Imagenet classification with deep convolutional neural networks. Neural Inf Process Syst 25:87. https://doi.org/10.1145/3065386

    Article  Google Scholar 

  42. Milletari F, Navab N, Ahmadi S (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. https://doi.org/10.1109/3DV.2016.79

  43. Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448–456. PMLR

  44. Ciresan DC, Meier U, Masci J, Gambardella LM, Schmidhuber J (2011) Flexible, high performance convolutional neural networks for image classification. In: Twenty-second International Joint Conference on Artificial Intelligence

  45. Chollet F (2015) Keras. GitHub

  46. Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. In: Fürnkranz, J., Joachims, T. (eds.) Proceedings of the 27th International Conference on Machine Learning (ICML-10), June 21-24, 2010, Haifa, Israel, pp. 807–814. Omnipress, ???. https://icml.cc/Conferences/2010/papers/432.pdf

  47. Han J, Moraga C (1995) The influence of the sigmoid function parameters on the speed of backpropagation learning. In: Mira J, Sandoval F (eds) From natural to artificial neural computation. Springer, Berlin, Heidelberg

    Google Scholar 

  48. Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M, Ghemawat S, Goodfellow I, Harp A, Irving G, Isard M, Jia Y, Jozefowicz R, Kaiser L, Kudlur M, Levenberg J, Mané D, Monga R, Moore S, Murray D, Olah C, Schuster M, Shlens J, Steiner B, Sutskever I, Talwar K, Tucker P, Vanhoucke V, Vasudevan V, Viégas F, Vinyals O, Warden P, Wattenberg M, Wicke M, Yu Y, Zheng X (2015) TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org. https://www.tensorflow.org/

  49. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830

    MathSciNet  MATH  Google Scholar 

  50. Kingma D, Ba J (2014) Adam: A method for stochastic optimization. International Conference on Learning Representations

  51. Barnes LR, Gruntfest EC, Hayden MH, Schultz DM, Benight C (2007) False alarms and close calls: a conceptual model of warning accuracy. Weather Forecast 22(5):1140–1147. https://doi.org/10.1175/WAF1031.1

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Mathematics, Shaheed Bhagat Singh College, University of Delhi, Delhi, India

    Sandeep Kumar

  2. Department of Computer Science, IIIT Delhi, Delhi, India

    Sandeep Kumar & Koushik Biswas

  3. Department of Mathematics, IIIT Delhi, Delhi, India

    Ashish Kumar Pandey

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  1. Sandeep Kumar
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Kumar, S., Biswas, K. & Pandey, A.K. Will a tropical cyclone make landfall?. Neural Comput & Applic 35, 5807–5818 (2023). https://doi.org/10.1007/s00521-022-07996-7

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