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Intelligent monitor for typhoon in IoT system of smart city

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Abstract

Accidents often occur in the earth—typhoons, floods, earthquakes, traffic accidents and so on. Whether these accidents can be timely and effectively responded to has been an important indicator to judge whether a region is advanced or not. IoT provide a possibility to solve such emergent problems by intelligent monitoring, diagnosis and repair. For example, coastal cities are often attacked by typhoons, if typhoon meteorological identification and early warning can be effectively carried out, many unnecessary property and personnel losses can be reduced. Accurate typhoon prediction has very important practical significance. However, current typhoon monitoring and prediction are mainly based on simulation with meteorological data; the accuracy still needs to be improved. Nowadays, the technology of Internet of Things (IoT) and remote sensing technology become more and more closely linked; many IoT systems in smart cities’ can obtain high-resolution remote sensing image data. Therefore, it is possible to use urban IoT system to realize the early warning of typhoon. In this paper, we propose a deep learning method for typhoon cloud recognition and typhoon center location, and design a general algorithm framework, including data preprocessing, model training and parameter selection, test and result analysis. Besides, we implement a typhoon early warning demo system. The experimental results show that our algorithm is better than the traditional methods in recognition accuracy.

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References

  1. Tsutsui K, Rokugawa S, Nakagawa H et al (2007) Detection and volume estimation of large-scale landslides based on elevation-change analysis using DEMs extracted from high-resolution satellite stereo imagery. IEEE Trans Geosci Remote Sens 45(6):1681–1696

    Article  Google Scholar 

  2. Pagano A, Giordano R, Portoghese I et al (2014) A Bayesian vulnerability assessment tool for drinking water mains under extreme events. Nat Hazards 74(3):2193–2227

    Article  Google Scholar 

  3. Lo SW, Wu JH, Lin FP et al (2015) Visual sensing for urban flood monitoring. Sensors 15(8):20006–20029

    Article  Google Scholar 

  4. Furquim G, Filho GPR, Jalali R et al (2018) How to improve fault tolerance in disaster predictions: a case study about flash floods using IoT, ML and real data. Sensors 18(3):907

    Article  Google Scholar 

  5. Wei CC (2012) Wavelet kernel support vector machines forecasting techniques: case study on water-level predictions during typhoons. Expert Syst Appl 39(5):5189–5199

    Article  Google Scholar 

  6. Hong JI, Heer J, Waterson S et al (2001) WebQuilt: a proxy-based approach to remote web usability testing. ACM Trans Inf Syst 19(3):263–285

    Article  Google Scholar 

  7. Sardar Z (2015) Postnormal times revisited. Futures 67:26–39

    Article  Google Scholar 

  8. Tsai SC, Kiang JF (2011) Floating dropsondes with DGPS receiver for real-time typhoon monitoring. IEEE Trans Geosci Remote Sens 49(11):4363–4373

    Article  Google Scholar 

  9. Wang X, Ren Y, Li X (2016) The warm-core structure of Super Typhoon Rammasun derived by FY-3C microwave temperature sounder measurements. Atmos Sci Lett 17(8):432–436

    Article  Google Scholar 

  10. Tsujino S, Tsuboki K, Kuo HC (2017) Structure and maintenance mechanism of long-lived concentric eyewalls associated with simulated typhoon bolaven (2012). J Atmos Sci 74(11):3609–3634

    Article  Google Scholar 

  11. Monteleoni C, Schmidt GA, Mcquade S (2013) Climate informatics: accelerating discovering in climate science with machine learning. Comput Sci Eng 15(5):32–40

    Article  Google Scholar 

  12. Moon W, Wettlaufer JS (2017) A unified nonlinear stochastic time series analysis for climate science. Sci Rep 7:44228

    Article  Google Scholar 

  13. Huang JZ, Li DS, Li HN et al (2018) Damage identification of a large cable-stayed bridge with novel cointegrated Kalman filter method under changing environments. Struct Control Health Monit 4:e2152

    Article  Google Scholar 

  14. Zhang Q, Yang W, Zhang S et al (2018) Characteristics of BeiDou navigation satellite system multipath and its mitigation method based on Kalman filter and Rauch-Tung-Striebel smoother. Sensors 18(1):198

    Article  Google Scholar 

  15. Keshtegar B, Heddam S (2017) Modeling daily dissolved oxygen concentration using modified response surface method and artificial neural network: a comparative study. Neural Comput Appl 30:2995–3006

    Article  Google Scholar 

  16. Torres-Barrán A, Alaíz CM, Dorronsoro JR (2017) Ν-SVM solutions of constrained Lasso and Elastic net. Neurocomputing 275:1921–1931

    Article  Google Scholar 

  17. Pradhan R, Aygun R, Maskey M et al (2017) Tropical cyclone intensity estimation using a deep convolutional neural network. IEEE Trans Image Process PP(99):1

    MATH  Google Scholar 

  18. Ali MM, Jagadeesh PSV, Lin II et al (2012) A neural network approach to estimate tropical cyclone heat potential in the Indian Ocean. IEEE Geosci Remote Sens Lett 9(6):1114–1117

    Article  Google Scholar 

  19. Loridan T, Crompton RP, Dubossarsky E (2017) A machine learning approach to modeling tropical cyclone wind field uncertainty. Mon Weather Rev 145(8):3203–3221

    Article  Google Scholar 

  20. Marmanis D, Datcu M, Esch T et al (2016) Deep learning earth observation classification using ImageNet pretrained networks. IEEE Geosci Remote Sens Lett 13(1):105–109

    Article  Google Scholar 

  21. Ren S, He K, Girshick R et al (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–1149

    Article  Google Scholar 

  22. Peng P, Tian Y, Xiang T et al (2017) Joint semantic and latent attribute modelling for cross-class transfer learning. IEEE Trans Pattern Anal Mach Intell 40(7):1625–1638

    Article  Google Scholar 

Download references

Acknowledgements

The work is supported in part by the National Natural Science Foundation of China (No. 61572157), Grant Nos. 2016A030313660 and 2017A030313365 from Guangdong Province Natural Science Foundation, Shenzhen Municipal Science and Technology Innovation Project, JCYJ20160608161351559, KQJSCX70726103044992, KQJSCX20170327161655607 and JCYJ20170811155158682.

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China

    Eric Ke Wang & Fan Wang

  2. Department of Mathematics, Ch. Charan Singh University, Meerut, India

    Saru Kumari

  3. Department of Computer Science, Boise State University, Idaho, USA

    Jyh-Haw Yeh

  4. College of Computer Science and Engineering, Shandong University of Science and Technology, Shandong, China

    Chien-Ming Chen

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  1. Eric Ke Wang
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  2. Fan Wang
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  3. Saru Kumari
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Correspondence to Chien-Ming Chen.

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Wang, E.K., Wang, F., Kumari, S. et al. Intelligent monitor for typhoon in IoT system of smart city. J Supercomput 77, 3024–3043 (2021). https://doi.org/10.1007/s11227-020-03381-0

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  • DOI: https://doi.org/10.1007/s11227-020-03381-0

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