M. Mizutori and D. Guha-Sapir, Economic Losses, Poverty and Disasters 1998-2017. United Nations Office for Disaster Risk Reduction, United Nations, New York, USA, 2017.
P. Wallemacq and H. Rowena, Economic Losses, Poverty & Disasters: 1998–2017. Brussels, Belgium: Centre for Research on the Epidemiology of Disasters, 2018.
F. Thomalla and H. Schmuck, ‘We all knew that a cyclone was coming’: Disaster preparedness and the cyclone of 1999 in Orissa, India, Disasters, vol. 28, no. 4, pp. 373–387, 2004.
L. Z. Jiang, S. M. Fu, and J. H. Sun, New method for detecting extratropical cyclones: The eight-section slope detecting method, Atmos. Ocean. Sci. Lett., vol. 13, no. 5, pp. 436–442, 2020.
U. Neu, M. G. Akperov, N. Bellenbaum, R. Benestad, R. Blender, R. Caballero, A. Cocozza, H. F. Dacre, Y. Feng, K. Fraedrich, et al., IMILAST: A community effort to intercompare extratropical cyclone detection and tracking algorithms, Bull. Am. Meteorol. Soc., vol. 94, no. 4, pp. 529–547, 2013.
A. Murata, S. I. I. Watanabe, H. Sasaki, H. Kawase, and M. Nosaka, The development of a resolution-independent tropical cyclone detection scheme for high-resolution climate model simulations, J. Meteorol. Soc. Japan. Ser. II, vol. 97, no. 2, pp. 519–531, 2019.
K. Rajesh, V. Ramaswamy, K. Kannan, and N. Arunkumar, Satellite cloud image classification for cyclone prediction using dichotomous logistic regression based fuzzy hypergraph model, Futur. Gener. Comput. Syst., vol. 98, pp. 688–696, 2019.
D. Matsuoka, M. Nakano, D. Sugiyama, and S. Uchida, Deep learning approach for detecting tropical cyclones and their precursors in the simulation by a cloud-resolving global nonhydrostatic atmospheric model, Prog. Earth Planet. Sci., vol. 5, no. 1, p. 80, 2018.
S. Shakya, S. Kumar, and M. Goswami, Deep learning algorithm for satellite imaging based cyclone detection, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 13, pp. 827–839, 2020.
J. G. Powers, J. B. Klemp, W. C. Skamarock, C. A. Davis, J. Dudhia, D. O. Gill, J. L. Coen, D. J. Gochis, R. Ahmadov, S. E. Peckham, et al., The weather research and forecasting model: Overview, system efforts, and future directions, Bull. Am. Meteorol. Soc., vol. 98, no. 8, pp. 1717–1737, 2017.
A. C. Lorenc, Analysis methods for numerical weather prediction, Q.J.R. Meteorol. Soc., vol. 112, no. 474, pp. 1177–1194, 1986.
G. E. Liston and R. A. Pielke, A climate version of the regional atmospheric modeling system, Theor. Appl. Climatol., vol. 66, nos. 1&2, pp. 29–47, 2000.
R. A. Pielke, W. R. Cotton, R. L. Walko, C. J. Tremback, W. A. Lyons, L. D. Grasso, M. E. Nicholls, M. D. Moran, D. A. Wesley, T. J. Lee, et al., A comprehensive meteorological modeling system-RAMS, Meteorol. Atmos. Phys., vol. 49, nos. 1–4, pp. 69–91, 1992.
M. Mu, W. S. Duan, and B. Wang, Conditional nonlinear optimal perturbation and its applications, Nonlin. Process. Geophys., vol. 10, no. 6, pp. 493–501, 2003.
J. J. Baik and H. S. Hwang, Tropical cyclone intensity prediction using regression method and neural network, J. Meteorol. Soc. Japan. Ser. II, vol. 76, no. 5, pp. 711–717, 1998.
M. Rütgers, S. Lee, and D. You, Typhoon track prediction using satellite images in a generative adversarial network, arXiv preprint arXiv: 1808.05382, 2018.
R. Kovordányi and C. Roy, Cyclone track forecasting based on satellite images using artificial neural networks, ISPRS J. Photogramm. Remote Sens., vol. 64, no. 6, pp. 513–521, 2009.
T. J. Sejnowski, The Deep Learning Revolution. Cambridge, MA, USA: MIT Press, 2018, pp. 1–10.
H. J. Yoo, Deep convolution neural networks in computer vision-a review, IEIE Trans. Smart Process. Comput., vol. 4, no. 1, pp. 35–43, 2015.
J. Salat, J. Pascual, M. Flexas, T. M. Chin, and J. Vazquez-Cuervo, Forty-five years of oceanographic and meteorological observations at a coastal station in the NW Mediterranean: A ground truth for satellite observations, Ocean Dynamics, vol. 69, no. 9, pp. 1067–1084, 2019.
A. K. Rai, N. Mandal, A. Singh, and K. K. Singh, Landsat 8 OLI satellite image classification using convolutional neural network, Procedia Comput. Sci., vol. 167, pp. 987–993, 2020.
A. Wimmers, C. Velden, and J. H. Cossuth, Using deep learning to estimate tropical cyclone intensity from satellite passive microwave imagery, Mon. Wea. Rev., vol. 147, no. 6, pp. 2261–2282, 2019.
B. Chen, B. F. Chen, and H. T. Lin, Rotation-blended CNNs on a new open dataset for tropical cyclone image-to-intensity regression, in Proc. 24th ACM SIGKDD Int. Conf. Knowledge Discovery & Data Mining, London, UK, 2018, pp. 90–99.
J. S. Combinido, J. R. Mendoza, and J. Aborot, A convolutional neural network approach for estimating tropical cyclone intensity using satellite-based infrared images, in 2018 24th Int. Conf. Pattern Recognition (ICPR), Beijing, China, 2018, pp. 1474–1480.
C. Kar, A. Kumar, and S. Banerjee, Tropical cyclone intensity detection by geometric features of cyclone images and multilayer perceptron, SN Appl. Sci., vol. 1, no. 9, p. 1099, 2019.
S. M. Chen, Y. M. Wang, and I. Tsou, Using artificial neural network approach for modelling rainfall-runoff due to typhoon, J. Earth Syst. Sci., vol. 122, no. 2, pp. 399–405, 2013.
C. C. Young and W. C. Liu, Prediction and modelling of rainfall-runoff during typhoon events using a physically-based and artificial neural network hybrid model, Hydrol. Sci.J., vol. 60, no. 12, pp. 2102–2116, 2015.
S. Kim, Y. Matsumi, S. Q. Pan, and H. Mase, A real-time forecast model using artificial neural network for after-runner storm surges on the Tottori coast, Japan, Ocean Eng., vol. 122, pp. 44–53, 2016.
Y. F. Wang, W. Zhang, and W. Fu, Back Propogation(BP)-neural network for tropical cyclone track forecast, in Proc. 2011 19th Int. Conf. Geoinformatics, Shanghai, China, 2011, pp. 1–4.
J. Y. Zhou, J. Xiang, and S. X. Huang, Classification and prediction of typhoon levels by satellite cloud pictures through GC-LSTM deep learning model, Sensors, vol. 20, no. 18, p. 5132, 2020.
S. S. Roy, V. Lakshmanan, S. K. R. Bhowmik, and S. B. Thampi, Doppler weather radar based nowcasting of cyclone Ogni, J. Earth Syst. Sci., vol. 119, no. 2, pp. 183–199, 2010.
S. N. K. B. Amit and Y. Aoki, Disaster detection from aerial imagery with convolutional neural network, in Proc. 2017 Int. Electronics Symp. Knowledge Creation and Intelligent Computing (IES-KCIC), Surabaya, Indonesia, 2017, pp. 239–245.
G. Kim and A. P. Barros, Quantitative flood forecasting using multisensor data and neural networks, J. Hydrol., vol. 246, nos. 1–4, pp. 45–62, 2001.
M. Kim, M. S. Park, J. Im, S. Park, and M. I. Lee, Machine learning approaches for detecting tropical cyclone formation using satellite data, Remote Sens., vol. 11, no. 10, p. 1195, 2019.
V. F. Dvorak, Tropical cyclone intensity analysis and forecasting from satellite imagery, Mon. Wea. Rev., vol. 103, no. 5, pp. 420–430, 1975.
B. B. Traore, B. Kamsu-Foguem, and F. Tangara, Deep convolution neural network for image recognition, Ecol. Inform., vol. 48, pp. 257–268, 2018.
A. F. Agarap, Deep learning using rectified linear units (ReLU), arXiv Preprint arXiv: 1803.08375, 2018.
S. Sharma, S. Sharma, and A. Athaiya, Activation functions in neural networks, Int.J. Eng. Appl. Sci. Technol., vol. 4, no. 12, pp. 310–316, 2020.
I. Kouretas and V. Paliouras, Simplified hardware implementation of the softmax activation function, in Proc. 2019 8th Int. Conf. Modern Circuits and Systems Technologies (MOCAST), Thessaloniki, Greece, 2019, pp. 1–4.
R. Rojas, Neural Networks-A Systematic Introduction. Berlin, Germany: Springer, 1996, pp. 149–182.
X. Y. Deng, Q. Liu, Y. Deng, and S. Mahadevan, An improved method to construct basic probability assignment based on the confusion matrix for classification problem, Inf. Sci., vols. 340&341, pp. 250–261, 2016.
R. Susmaga, Confusion matrix visualization, in Intelligent Information Processing and Web Mining, M. A. Kłpotek, S. T. Wierzchoń K. Trojanowski, eds. Berlin, Germany: Springer, 2004, pp. 107–116.