Abstract
Severe thunderstorm prediction by analysis of cloud imageries is becoming an interesting area in research field. It causes destruction to daily life. Therefore correct prediction of severe thunderstorm has important significance. Here in this study Support Vector Machine (SVM) has been applied on cloud imageries for the prediction purpose. Colour is one of the most important features of image. Here colour has been considered as only feature for the classification purpose. Two sets of cloud imageries have been considered here, one for ‘squall days’ and another for ‘no squall days’. The imageries for squall days have been indicated by ‘1’ and ‘no squall days’ by ‘0’. The linear Support Vector Classifier (SVC) has been applied here for classification. Principal Component Analysis (PCA) has been applied here for feature reduction purpose, which yields better result. This prediction has a lead time of 5–6 h which is enough to save society from devastation created by severe thunderstorm.
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References
H. Chakrabarty, S. Bhattacharya, Application of K-nearest neighbor technique to predict severe thunderstorms. Int. J. Comput. Appl. 110, 1–5 (2015)
S. Bhattachary, H. Bhattacharyya, Studies on radar imageries of thundercloud by image processing technique. Data Manag. Anal. Innov. 1042, 365–380 (2019)
G.G. Pushpa, H. Patoju, G.S. Charan, M.K. Charan, P. Jagtap, Weather forecasting using digital image processing. J. Compos. 13, 968–972 (2020)
A. Montesinos, F.J. Batlles, The use of a sky camera for solar radiation estimation based on digital image processing. Energy 90, 377–386 (2015)
Z. Zhen, Z. Wang, F. Wang, Z. Mi, K. Li, Research on a cloud image forecasting approach for solar power forecasting. Energy Proc. 142, 362–368 (2017)
J. Kleissl, Solar Energy Forecasting and Resource Assessment (Academic Press, 2013)
M. Matos, R. Bessa, A. Botterud, Z. Zhou, Forecasting and setting power system operating reserves. Renew. Energy Forecast. Models Appl. (2017). https://doi.org/10.1016/B978-0-08-100504-0.00011-1
F. Barbieri, S. Rajakaruna, A. Ghosh, Very short-term photovoltaic power forecasting with cloud modeling: a review. Renew. Sustain. Energy Rev. 75, 242–263 (2017)
J.Y. Gil, R. Kimmel, Efficient dilation, erosion, opening, and closing algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 24, 1606–1617 (2002)
W.C. Skamarock, J.B. Klemp, J. Dudhia, D.O. Gill, D.M. Barker, W. Wang, J.G. Powers, A Description of the Advanced Research WRF. Version 2 (NCAR Technical Note NCAR/TN-4681STR 88, 2008)
M. Gryschka, B. Witha, D. Etling, Scale analysis of convective clouds. Meteorol. Z. 17, 785–791 (2008)
Y.L. Lin, L.E. Joyce, A further study of mechanisms of cell regeneration, development and propagation within a two-dimensional multicell storm. J. Atmos. Sci. 58, 2957–2988 (2001)
Y.L. Lin, R.L. Deal, M.S. Kulie, Mechanisms of cell regeneration, propagation, and development within two-dimensional multicell storms. J. Atmos. Sci. 55, 1867–1886 (1998)
R.G. Fovell, P.H. Tan, Why multicell storms oscillate, in 18th Conference on Severe Local Storms (American Meteorological Society, San Francisco, CA, 1996), pp. 186–189 (Preprints)
S. Paris, S.W. Hasinoff, J. Kautz, Local Laplacian filters: edge-aware image processing with a Laplacian pyramid. ACM Trans. Graph. Proc. SIGGRAPH 30, 1–11 (2011)
Y. Li, L. Sharan, E.H. Adelson, Compressing and companding high dynamic range images with subband architectures. ACM Trans. Graph. Proc. ACM SIGGRAPH Conf. 24, 836–844 (2005). https://doi.org/10.1145/1186822.1073271
Z. Farbman, R. Fattal, D. Lischinski, R. Szeliski, Edge-preserving decompositions for multi-scale tone and detail manipulation. ACM Trans. Graph. Proc. SIGGRAPH 27, 1–10 (2008). https://doi.org/10.1145/1360612.1360666
R. Fattal, Edge-avoiding wavelets and their applications. ACM Trans. Graph. Proc. SIGGRAPH 28, 1–9
A. Khan, N.A. Syed, Image processing techniques for automatic detection of tumor in human brain using SVM. Int. J. Adv. Res. Comput. Commun. Eng. 4, 541–544 (2015)
A.A. Tzotsos, Support Vector Machine Approach for Object Based Image Analysis. Commission IV, WG IV/4—Commission VIII, WG VIII/11
F. Melgani, L. Bruzzone, Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans. Geosci. Remote Sens. 42 (2004)
S. Theodoridi, K. Koutroumbas, Pattern Recognition, 2nd edn. (Elsevier Academic Press, 2003)
G. Mercier, M. Lennon, Support vector machines for hyperspectral image classification with spectral-based kernels, in Proceedings of the IGARSS (IEEE Cat. No. 03CH37477, Toulouse, France, 2003)
S.Y. Chaganti, I. Nanda, K.R. Pandi, Image classification using SVM and CNN, in International Conference on Computer Science, Engineering and Applications (ICCSEA) (2020). https://doi.org/10.1109/ICCSEA49143.2020.9132851
D. Zhang, Z. Zhou, S. Chen, Diagonal principal component analysis for face recognition. Pattern Recogn. 39, 140–142 (2006)
V.H. Nguyen, Facial feature extraction based on wavelet transform, in Artificial Intelligence and Computational Intelligence. Lecture Notes in Computer Science, vol. 5855 (2009), pp. 330–339. https://doi.org/10.1007/978-3-642-05253-8_37
H.H. Barret, Foundations of Image Science, 3rd edn. (Wiley, New Jersey, UK, 2004)
R.C. Gonzales, R.E. Woods, Digital Image Processing, 2nd edn. (Prentice Hall, 2002). ISBN 0-201-18075-8
H.T. Le, N.T.D. Nguyen, H.S. Tran, Facial expression classification system integrating canny, in Principal Component Analysis and Artificial Neural Network. 3rd International Conference on Machine Learning and Computing, ICMLC Proceedings, vol. 4 (2011), pp. 306–309
K.S. Kurnima, D. Mondira, K. Balbindar, D. Chandralekha, D. Karen, Radon transform and PCA based feature extraction to design an Assamese character recognition system, in 3rd National Conference on Emerging Trends and Applications in Computer Science (2012)
Y. Zhang, B. Yu, Face recognition using combined non-negative principal component analysis and linear discriminant analysis, in 6th International Congress on Image and Signal Processing (CISP) (2013)
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Bhattacharya, S., Chakrabarty Bhattacharyya, H. (2023). Forecasting Severe Thunderstorm by Applying SVM Technique on Cloud Imageries. In: Goswami, S., Barara, I.S., Goje, A., Mohan, C., Bruckstein, A.M. (eds) Data Management, Analytics and Innovation. ICDMAI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 137. Springer, Singapore. https://doi.org/10.1007/978-981-19-2600-6_8
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