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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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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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