Abstract
Weather prediction is the hottest topic in remote sensing to understand natural disasters and their intensity in an early stage. But in many cases, the typical imaging models have resulted in less forecasting rate. Hence, to overcome this problem, a novel buffalo-based Generalized Adversarial Cyclone Intensity Prediction System (BGACIPS) was designed for cyclone intensity prediction using space satellite images. The processed satellite images contained features like rain, snow, Tropical depression (T.Depression), thunderstorms (T.strom), and cyclone. Initially, the noise features were removed in the pre-processing module, and the refined data was entered into the classification layer. Consequently, the analysis of the features was performed, and the intensity of each feature and cyclone stages were identified. Furthermore, the planned design is executed in the python environment, and the improvement score has been analyzed regarding prediction exactness, mean errors, and error rate. Hence, the proposed novel BGACIPS has a lower error rate and higher prediction accuracy than the compared models.
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References
Chen Z, Yu X, Chen G, Zhou J (2018) Cyclone Intensity estimation using multispectral imagery from the fy-4 satellite. 2018 International Conference on Audio, Language and Image Processing (ICALIP), IEEE. https://doi.org/10.1109/ICALIP.2018.8455603
Chen G, Chen Z, Zhou F et al (2019) A semisupervised deep learning framework for tropical cyclone intensity estimation. 2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), IEEE. https://doi.org/10.1109/Multi-Temp.2019.8866970
Chen Z, Yu X (2020) A novel tensor network for tropical cyclone intensity estimation. IEEE Trans Geosci Remote Sens 59(4):3226–3243. https://doi.org/10.1109/TGRS.2020.3017709
Hewage P, Behera A, Trovati M et al (2020) Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station. Soft Comput 24(21):16453–16482. https://doi.org/10.1007/s00500-020-04954-0
Hewage P, Trovati M, Pereira E, Behera A (2021) Deep learning-based effective fine-grained weather forecasting model. Pattern Anal Appl 24(1):343–366. https://doi.org/10.1007/s10044-020-00898-1
Hu H, Han Y (2020) Comparing the Thermal Structures of Tropical Cyclones Derived From Suomi NPP ATMS and FY-3D Microwave Sounders. IEEE Trans Geosci Remote Sens 59(10):8073–8083. https://doi.org/10.1109/TGRS.2020.3034262
Hu L, Ritchie EA, Tyo JS (2020) Influence of Satellite Observation Angle to Tropical Cyclone Intensity Estimation Using the Deviation Angle Variance Technique. IEEE Trans Geosci Remote Sens 58(5):3703–3710. https://doi.org/10.1109/TGRS.2019.2961363
Jaiswal N, Deb SK, Kishtawal CM (2022) Intensification of tropical cyclone FANI observed by INSAT-3DR rapid scan data. Theor Appl Climatol 148:661–670. https://doi.org/10.1007/s00704-022-03957-1
Kar C, Banerjee S (2021) Tropical cyclone intensity classification from infrared images of clouds over Bay of Bengal and Arabian Sea using machine learning classifiers. Arab J Geosci 14(8):1–17. https://doi.org/10.1007/s12517-021-06997-5
Karevan Z, Suykens JAK (2020) Transductive LSTM for time-series prediction: An application to weather forecasting. Neural Netw 125:1–9. https://doi.org/10.1016/j.neunet.2019.12.030
Kaur M, Sood SK (2020) Hydro-meteorological hazards and role of ICT during 2010–2019: A scientometric analysis. Earth Sci Inform 13:1201–1223. https://doi.org/10.1007/s12145-020-00495-0
Khan RU, Bhattacharyya B, Singh G (2021) A novel African buffalo optimization for the minimization of cogging torque in modified permanent magnet DC motor. Sustain Energy Technol Assess 46:101240. https://doi.org/10.1016/j.seta.2021.101240
Lee YJ, Hall D, Liu Q, Liao WW, Huang MC (2021) Interpretable tropical cyclone intensity estimation using Dvorak-inspired machine learning techniques. Eng Appl Artif Intell 101:104233. https://doi.org/10.1016/j.engappai.2021.104233
Maskey M, Ramachandran R et al (2020) Deepti: Deep-learning-based tropical cyclone intensity estimation system. IEEE J Sel Top Appl Earth Obs Remote Sens 13:4271–4281. https://doi.org/10.1109/JSTARS.2020.3011907
Mubeen SMM, Priya MS, Vijayaraj M (2022) A novel approach for predicting the tc center of remotely sensed images using pso based density matrix. Earth Sci Inform 15:197–209. https://doi.org/10.1007/s12145-021-00711-5
Salman AG, Heryadi Y, Abdurahman E et al (2018) Single layer & multi-layer long short-term memory (LSTM) model with intermediate variables for weather forecasting. Procedia Comput Sci 135:89–98. https://doi.org/10.1016/j.procs.2018.08.153
Si Z, Yu Y, Yang M, Li P (2020) Hybrid solar forecasting method using satellite visible images and modified convolutional neural networks. IEEE Trans Ind Appl 57(1):5–16. https://doi.org/10.1109/TIA.2020.3028558
Singh N, Chaturvedi S, Akhter S (2019) Weather forecasting using machine learning algorithm. 2019 International Conference on Signal Processing and Communication (ICSC), IEEE. https://doi.org/10.1109/ICSC45622.2019.8938211
Varentsov MI, Grishchenko MY, Wouters H (2019) Simultaneous assessment of the summer urban heat island in Moscow megacity based on in situ observations, thermal satellite images and mesoscale modeling. Geogr Environ Sustain 12(4):74–95. https://doi.org/10.24057/2071-9388-2019-10
Venkatesh K, Maheswaran R, Devacharan J (2022) Framework for developing IDF curves using satellite precipitation: a case study using GPM-IMERG V6 data. Earth Sci Inform 15:671–687. https://doi.org/10.1007/s12145-021-00708-0
Wang C, Zheng G, Li X et al (2021) Tropical cyclone intensity estimation from geostationary satellite imagery using deep convolutional neural networks. IEEE Trans Geosci Remote Sens 60:1–16. https://doi.org/10.1109/TGRS.2021.3066299
Wang P, Wang P, Wang D, Xue B (2021) A Conformal Regressor with Random Forests for Tropical Cyclone Intensity Estimation. IEEE Trans Geosci Remote Sens 60. https://doi.org/10.1109/TGRS.2021.3139930
Xie GS, Zhang Z, Liu G et al (2021) Generalized zero-shot learning with multiple graph adaptive generative networks. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2020.3046924
Xu L, Wang S, Tang R (2019) Probabilistic load forecasting for buildings considering weather forecasting uncertainty and uncertain peak load. Appl Energy 237:180–195. https://doi.org/10.1016/j.apenergy.2019.01.022
Xu Z, Du J, Wang J et al (2019) Satellite image prediction relying on gan and lstm neural networks. ICC 2019–2019 IEEE International Conference on Communications (ICC), IEEE. https://doi.org/10.1109/ICC.2019.8761462
Zhang C, Wu M, Chen J et al (2019) Weather visibility prediction based on multimodal fusion. IEEE Access 7:74776–74786. https://doi.org/10.1109/ACCESS.2019.2920865
Zhang R, Liu Q, Hang R (2019b) Tropical cyclone intensity estimation using two-branch convolutional neural network from infrared and water vapor images. IEEE Trans Geosci Remote Sens 58(1):586–597. https://doi.org/10.1109/TGRS.2019.2938204
Zhang W, Maleki A, Rosen MA, Liu J (2019c) Sizing a stand-alone solar-wind-hydrogen energy system using weather forecasting and a hybrid search optimization algorithm. Energy Convers Manag 180:609–621. https://doi.org/10.1016/j.enconman.2018.08.102
Zhang CJ, Wang XJ, Ma LM, Lu XQ (2021) Tropical cyclone intensity classification and estimation using infrared satellite images with deep learning. IEEE J Sel Top Appl Earth Obs Remote Sens 14:2070–2086. https://doi.org/10.1109/JSTARS.2021.3050767
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Authors K.K.R.C., A.P.R., M.M.H., P.Y.V.S.S., R.M.B.V., and M.M.R. have contributed equally to the work.
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Reddy, C.K.K., Anisha, P.R., Hanafiah, M.M. et al. An intelligent optimized cyclone intensity prediction framework using satellite images. Earth Sci Inform 16, 1537–1549 (2023). https://doi.org/10.1007/s12145-023-00983-z
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DOI: https://doi.org/10.1007/s12145-023-00983-z