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International Journal of Advanced Artificial Intelligence Research

Open Access Peer Review International
Open Access

ENHANCED IDENTIFICATION OF EQUATORIAL PLASMA BUBBLES IN AIRGLOW IMAGERY VIA 2D PRINCIPAL COMPONENT ANALYSIS AND INTERPRETABLE AI

DOI https://doi.org/10.55640/ijaair-v02i02-01
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Dr. Ayesha Siddiqui 1 ,
4 Department of Atmospheric and Space Sciences, Savitribai Phule Pune University, India

Abstract

Equatorial Plasma Bubbles (EPBs) are ionospheric irregularities that disrupt communication and navigation systems, especially in low-latitude regions. This study presents an enhanced framework for identifying EPBs in airglow imagery by integrating two-dimensional Principal Component Analysis (2D-PCA) with interpretable artificial intelligence (AI) techniques. The proposed approach efficiently extracts spatial features from airglow images and leverages interpretable AI models to improve classification accuracy while maintaining transparency in decision-making. Experimental validation using real-world airglow datasets demonstrates the superiority of the 2D-PCA and interpretable AI combination over traditional detection methods in terms of accuracy, robustness, and explainability. The findings pave the way for more reliable space weather monitoring and early warning systems.

Keywords

Equatorial Plasma Bubbles, airglow imagery, 2D Principal Component Analysis, interpretable AI

References

Hysell, D.L.; Woodman, R.F. Imaging coherent backscatter radar observations of topside equatorial spread F. Radio Sci. 1997, 32, 2309–2320.
Kelley, M.C. The Earth’s Ionosphere; Academic Press: San Diego, CA, USA, 1989.
Basu, S.; Kudeki, E.; Basu, S.; Valladares, C.E.; Weber, E.J.; Zengingonul, H.P.; Espinoza, J. Scintillations, plasma drift, and neutral winds in the equatorial ionosphere after sunset. J. Geophys. Res. 1996, 101, 26795–26809.
Kintner, P.M.; Kil, H.; Ledvina, B.M.; Paula, E.D. Fading timescales associated with GPS signals and potential consequences. Radio Sci. 2001, 36, 731–743.
Straus, P.R.; Anderson, P.C.; Danaher, J.E. GPS occultation sensor observations of ionospheric scintillation. Geophys. Res. Lett. 2003, 30, 1436.
Park, J.; Noja, M.; Stolle, C.; Luhr, H. The ionospheric bubble index deduced from magnetic field and plasma observations onboard swarm. Earth Planets Space 2013, 65, 1333–1344.
Weber, E.J.; Brinton, H.C.; Buchau, J.; Moore, J.G. Coordinated airborne and satellite measurements of equatorial plasma depletions. J. Geophys. Res. 1982, 87, 10503–10513.
Tinsley, B.A.; Rohrbaugh, R.P.; Hanson, W.B.; Broadfoot, A.L. Images of transequatorial F region bubbles in 630- and 777-nm emissions compared with satellite measurements. J. Geophys. Res. 1997, 102, 2057–2077.
Su, S.-Y.; Yeh, H.C.; Heelis, R.A. ROCSAT-1 ionospheric plasma and electrodynamics instrument observations of equatorial spread F: An early transitional scale result. J. Geophys. Res. 2001, 106, 29153–29159.
Kelley, M.C.; Makela, J.J.; Ledvina, B.M.; Kintner, P.M. Observations of equatorial spread-F from Haleakala, Hawaii. Geophys. Res. Lett. 2002, 29, 2003.
Makela, J.J.; Miller, E.S. Optical observations of the growth and day-to-day variability of equatorial plasma bubbles. J. Geophys. Res. Space Phys. 2008, 113, 1–7.
Sobral, J.H.; Takahashi, H.; Abdu, M.A.; Muralikrishna, P.; Sahai, Y.; Zamlutti, C.J.; De Paura, R.E.; Batista, P.P. Determination of the quenching rate of the O(1D) by O(3P) from rocket-borne optical (630 nm) and electron density data. J. Geophys. Res. 1993, 98, 7791–7798.
Pimenta, A.A.; Fagundes, P.R.; Bittencourt, J.A.; Sahai, Y.; Gobbi, D.; Medeiros, A.F.; Taylor, M.J.; Takahashi, H. Ionospheric plasma bubble zonal drift: A methodology using OI 630 nm all-sky imaging systems. Adv. Space Res. 2001, 27, 1219–1224.
Weber, E.J.; Buchau, J.; Eather, R.H.; Mende, S.B. North-south aligned equatorial depletions. J. Geophys. Res. 1978, 83, 712–716.
Mendillo, M.; Baumgardner, J.; Sultan, P.J. Airglow characteristics of equatorial plasma depletions. J. Geophys. Res. 1982, 87, 7641–7652.
Taylor, M.J.; Eccles, J.V.; Labelle, J.; Sobral, J.H.A. High-resolution OI (630 nm) image measurements of F-region depletion drifts during the Guara campaign. Geophys. Res. Lett. 1997, 24, 1699–1702.
Fagundes, P.R.; Sahai, Y.; Batista, I.S.; Abdu, M.A.; Bittencourt, J.A.; Takahashi, H. Observations of day-to-day variability in precursor signatures to equatorial F-region plasma depletions. Ann. Geophys. 1999, 17, 1053–1063.
Makela, J.J.; Ledvina, B.M.; Kelley, M.C.; Kintner, P.M. Analysis of the seasonal variations of equatorial plasma bubble occurrence observed from Haleakala. Hawaii. Ann. Geophys. 2004, 22, 3109–3121.
Mendillo, M.; Zesta, E.; Shodhan, S.; Doe, R.; Sahai, Y.; Baumgardner, J. Observations and modeling of the coupled latitude-altitude patterns of equatorial plasma depletions. J. Geophys. Res. 2005, 110, A09303.
Rajesh, P.K.; Liu, J.Y.; Sinha, H.S.S.; Banerjee, S.B.; Misra, R.N.; Dutt, N.; Dadhania, M.B. Observations of plasma depletions in 557.7-nm images over Kavalur. J. Geophys. Res. Space Phys. 2007, 112, A07307.
Liu, J.Y.; Tsai, H.F.; Jung, T.K. Airglow observations over the equatorial ionization anomaly zone in Taiwan. Ann. Geophys. 2011, 29, 749–757.
Shiokawa, K.; Otsuka, Y.; Lynn, K.J.; Wilkinson, P.; Tsugawa, T. Airglow-imaging observation of plasma bubble disappearance at geomagnetically conjugate points. Earth Planet Space 2015, 67, 43.
Fukushima, D.; Shiokawa, K.; Otsuka, Y.; Nishioka, M.; Kubota, M.; Tsugawa, T.; Nagatsuma, T.; Komonjinda, S.; Yatini, C.Y. Geomagnetically conjugate observation of plasma bubbles and thermospheric neutral winds at low latitudes. J. Geophys. Res. Space Phys. 2015, 120, 2222–2231.
Okoh, D.; Rabiu, B.; Shiokawa, K.; Otsuka, Y.; Segun, B.; Falayi, E.; Onwuneme, S.; Kaka, R. First study on the occurrence frequency of equatorial plasma bubbles over West Africa using an all-sky airglow imager and GNSS receivers. J. Geophys. Res. Space Phys. 2017, 122, 12430–12444.
Ghodpage, R.N.; Gurav, O.B.; Taori, A.; Sau, S.; Patil, P.T.; Erram, V.C.; Sripathi, S. Observation of an intriguing equatorial plasma bubble event over Indian sector. J. Geophys. Res. Space Phys. 2021, 126, e2020JA028308.
Wrasse, C.M.; Figueiredo, C.A.O.B.; Barros, D.; Takahashi, H.; Carrasco, A.J.; Vital, L.F.R.; Rezende, L.C.A.; Egito, F.; Rosa, G.M.; Sampaio, A.H.R. Interaction between equatorial plasma bubbles and a medium-scale traveling ionospheric disturbance, observed by OI 630 nm airglow imaging at Bom Jesus de Lapa, Brazil. Earth Planet. Phys. 2021, 5, 397–406.
Liu, J.-Y.; Rajesh, P.K.; Liao, Y.-A.; Chum, J.; Kan, K.-W.; Lee, I.-T. Airglow observed by a full-band imager together with multi-instruments in Taiwan during nighttime of 1 November 2021. Adv. Space Res. 2024, 73, 663–671.
Gentile, L.C.; Burke, W.J.; Rich, F.J. A global climatology for equatorial plasma bubbles in the topside ionosphere. Ann. Geophys. 2006, 24, 163–172.
Huang, C.-S.; Roddy, P.A. Effects of solar and geomagnetic activities on the zonal drift of equatorial plasma bubbles. J. Geophys. Res. Space Phys. 2016, 121, 628–637.
Wan, X.; Xiong, C.; Rodriguez-Zuluaga, J.; Kervalishvili, G.N.; Stolle, C.; Wang, H. Climatology of the occurrence rate and amplitudes of local time distinguished equatorial plasma depletions observed by Swarm satellite. J. Geophys. Res. Space Phys. 2018, 123, 3014–3026.
Adkins, V.J.; England, S.L. Automated detection and tracking of equatorial plasma bubbles utilizing Global-Scale Observations of the Limb and Disk (GOLD) 135.6 nm Data. Earth Space Sci. 2023, 10, e2023EA002935.
Chakrabarti, S.; Patgiri, D.; Rathi, R.; Dixit, G.; Krishna, M.V.S.; Sarkhel, S. Optimizing a deep learning framework for accurate detection of the Earth’s ionospheric plasma structures from all-sky airglow images. Adv. Space Res. 2024, 73, 5990–6005.
Yang, J.; Zhang, D.; Frangi, A.F.; Yang, J. Two-dimensional PCA: A new approach to appearance-based face representation and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 2004, 26, 131–137.
Lin, J.-W. Ionospheric anomaly related to M=6.6, 26 August 2012, Tobelo earthquake near Indonesia: Two-dimensional principal component analysis. Acta Geod. Geophys. 2013, 48, 247–264.
Lin, J.-W. Generalized two-dimensional principal component analysis and two artificial neural network models to detect traveling ionospheric disturbances. Nat. Hazards 2022, 111, 1245–1270.
Lin, J.-W.; Chiou, J.-S.; Chao, C.-T. Detecting all possible ionospheric precursors by kernel-based two-dimensional principal component analysis. IEEE Access 2019, 7, 53650–53666.
Yu, J.; Liu, J. Two-dimensional principal component analysis-based convolutional autoencoder for wafer map defect detection. IEEE Trans. Ind. Electron. 2021, 68, 8789–8797.
Sun, Z.; Shao, Z.; Shang, Y.; Li, B.; Wu, J.; Bi, H. Randomized nonlinear two-dimensional principal component analysis network for object recognition. Mach. Vis. Appl. 2023, 34, 21.
Wang, L.; You, Z.H.; Yan, X. Using two-dimensional principal component analysis and rotation forest for prediction of protein-protein interactions. Sci. Rep. 2018, 8, 12874.
Abdelwahab, M.M.; Abdelrahman, S.A. Four layers image representation for prediction of lung cancer genetic mutations based on 2DPCA. In Proceedings of the 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), Boston, MA, USA, 6–9 August 2017; 2017; pp. 599–602.
Lundberg, S.M.; Lee, S.-I. A Unified Approach to Interpreting Model Predictions. Adv. Neural Inf. Process. Syst. (NeurIPS) 2017, 30.
Ribeiro, M.T.; Singh, S.; Guestrin, C. “Why should I trust you?” Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13–17 August 2016; pp. 1135–1144.
Retzlaff, C.O.; Angerschmid, A.; Saranti, A.; Schneeberger, D.; Müller, D.; Holzinger, A. Post-hoc vs ante-hoc explanations: xAI design guidelines for data scientists. Cogn. Syst. Res. 2024, 86, 101243.
Gonzalez, R.C.; Wintz, P. Digital Image Processing, 3rd ed.; Addison-Wesley: Reading, MA, USA, 1997.
Carsten, H. The Radon Transform; Aalborg University: Aalborg, Denmark, 2007; VGIS, 07gr721.
Yang, J.; Yang, J.Y. From image vector to matrix: A straightforward image projection technique—IMPCA vs. PCA. Pattern Recognit. 2002, 35, 1997–1999.
Breiman, L. Random Forests. Mach. Learn. 2001, 45, 5–32.
Guyon, I.; Weston, J.; Barnhill, S.; Vapnik, V. Gene selection for cancer classification using support vector machines. Mach. Learn. 2002, 46, 389–422.
Szegedy, C.; Vanhoucke, V.; Ioffe, S.; Shlens, J.; Wojna, Z. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27–30 June 2016; pp. 2818–2826.
He, K.; Zhang, X.; Ren, S.; Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27–30 June 2016; pp. 770–778.

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