BBCNN: Bounding Box Convolution Neural Network for Cyclone Prediction and Monitoring
Keywords:Cyclone Detection, Cyclone Intensity, Deep Learning, Predictive Model, Satellite imagery
The correct identification of tropical cyclones is essential for the prevention and preparation for catastrophic events that they cause. The prediction of a cyclone’s surge, as well as the monitoring of its severity, is an important part of the predictive model. Despite the significant efforts made each year, a significant number of individuals still lose their lives as a consequence of cyclones. More appropriate predictive methods need to be developed to reduce the severity of this harm. Deep learning techniques provide perks in detecting challenges since they can increase the prediction algorithm’s stability and efficiency. The method discussed here uses artificial neural networks to analyse MOSDAC satellite imagery. With the help of satellite data, a several-layer neural-net model was trained to predict cyclones, much like biological visual perception. The findings suggest that the method has the potential to be further refined into an efficient instrument for cyclone track predictions by making use of a variety of different kinds of remote sensing imagery and information.
K. Emanuel and D. S. Nolan, "Tropical cyclone activity and the global climate system," in 26th conference on hurricanes and tropical meteorolgy, 2004.
J. P. Kossin, K. R. Knapp, T. L. Olander, and C. S. Velden, "Global increase in major tropical cyclone exceedance probability over the past four decades," Proceedings of the National Academy of Sciences, vol. 117, no. 22, pp. 11975-11980, 2020.
A. Persson, "The Coriolis Effect," History of Meteorology, vol. 2, pp. 1-24, 2005
R. DeMaria, "Automated tropical cyclone eye detection using discriminant analysis," Colorado State University, 2015.
R. S. Cerveny and L. E. Newman, "Climatological relationships between tropical cyclones and rainfall," Monthly Weather Review, vol. 128, no. 9, pp. 3329-3336, 2000.
M. F. Piñeros, E. A. Ritchie, and J. S. Tyo, "Detecting tropical cyclone genesis from remotely sensed infrared image data," IEEE Geoscience Remote sensing letters, vol. 7, no. 4, pp. 826-830, 2010.
N. Jaiswal and C. M. Kishtawal, "Automatic determination of center of tropical cyclone in satellite-generated IR images," IEEE Geoscience Remote Sensing Letters, vol. 8, no. 3, pp. 460-463, 2010.
T. Fiolleau and R. Roca, "An algorithm for the detection and tracking of tropical mesoscale convective systems using infrared images from geostationary satellite," IEEE transactions on Geoscience Remote Sensing, vol. 51, no. 7, pp. 4302-4315, 2013.
N. Jaiswal and C. M. Kishtawal, "Objective detection of center of tropical cyclone in remotely sensed infrared images," IEEE Journal of Selected Topics in Applied Earth Observations Remote Sensing, vol. 6, no. 2, pp. 1031-1035, 2013.
I. Dutta and S. Banerjee, "Elliptic fourier descriptors in the study of cyclone cloud intensity patterns," International Journal of Image Processing, vol. 7, no. 4, pp. 402-417, 2013.
C Kar, A. Kumar, D. Konar, and S. Banerjee, "Automatic region of interest detection of tropical cyclone image by center of gravity and distance metrics," in 2019 Fifth International Conference on Image Information Processing (ICIIP), 2019, pp. 141-145: IEEE.
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 Sensing, vol. 11, no. 10, p. 1195, 2019.
C. Kar, A. Kumar, and S. Banerjee, "Tropical cyclone intensity detection by geometric features of cyclone images and multilayer perceptron," SN Applied Sciences, vol. 1, no. 9, pp. 1-7, 2019.
Y. Liu et al., "Application of deep convolutional neural networks for detecting extreme weather in climate datasets," 2016.
D. Matsuoka, M. Nakano, D. Sugiyama, and S. Uchida, "Detecting precursors of tropical cyclone using deep neural networks," in The 7th International Workshop on Climate Informatics, CI, 2017.
S. Giffard-Roisin, M. Yang, G. Charpiat, C. Kumler Bonfanti, B. Kégl, and C. Monteleoni, "Tropical cyclone track forecasting using fused deep learning from aligned reanalysis data," Frontiers in big Data, vol. 3, p. 1, 2020.
T. Chen et al., "Xgboost: extreme gradient boosting," R package version 0.4-2, vol. 1, no. 4, pp. 1-4, 2015.
J. H. Friedman, "Greedy function approximation: a gradient boosting machine," Annals of statistics, pp. 1189-1232, 2001.
H. Ibrahem, A. Salem, and H.-S. Kang, "LEOD-Net: Learning Line-Encoded Bounding Boxes for Real-Time Object Detection," Sensors, vol. 22, no. 10, p. 3699, 2022.
M. A. Islam et al., "Comprehensive Analysis of CNN and YOLOv5 Object Detection Model to Classify Phytomedicine Tree’s Leaf Disease," 2022.
R. Chaganti, V. Ravi, T. D. J. J. o. I. S. Pham, and Applications, "Image-based malware representation approach with EfficientNet convolutional neural networks for effective malware classification," vol. 69, p. 103306, 2022.
L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, "Encoder-decoder with atrous separable convolution for semantic image segmentation," in Proceedings of the European conference on computer vision (ECCV), 2018, pp. 801-818.
V. Iglovikov and A. Shvets, "Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation," 2018.
S. Albawi, T. A. Mohammed, and S. Al-Zawi, "Understanding of a convolutional neural network," in 2017 international conference on engineering and technology (ICET), 2017, pp. 1-6: Ieee.
R. Chauhan, K. K. Ghanshala, and R. Joshi, "Convolutional neural network (CNN) for image detection and recognition," in 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC), 2018, pp. 278-282: IEEE.
X. Jiang, Y. Wang, W. Liu, S. Li, and J. Liu, "Capsnet, cnn, fcn: Comparative performance evaluation for image classification," International Journal of Machine Learning Computing, vol. 9, no. 6, pp. 840-848, 2019.
X. Cheng and H. Lei, "Remote sensing scene image classification based on mmsCNN–HMM with stacking ensemble model," Remote Sensing, vol. 14, no. 17, p. 4423, 2022.
Z. Ouyang, X. Sun, J. Chen, D. Yue, and T. Zhang, "Multi-view stacking ensemble for power consumption anomaly detection in the context of industrial internet of things," IEEE Access, vol. 6, pp. 9623-9631, 2018.
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