The document discusses tropical cyclones that frequently impact coastal regions in India, causing tremendous loss. It notes that predicting tropical cyclone intensity is challenging due to climate changes. Intensity is influenced by oceanic, atmospheric, and meteorological parameters. The proposed research aims to develop hybrid learning models combining machine learning techniques to improve intensity predictions and provide timely warnings. The objectives are to enhance prediction accuracy, extend lead times, and provide probabilistic forecasts to help decision-making.

1.  Coastal regions in India are very frequently hit
byTropical Cyclones, which result in tremendous loss.
 Its intensity prediction has been a challenging task because of
drastic climatic changes over the past few years in the world.
 Intensity of Tropical Cyclone is highly influenced by ocean,
atmospheric and meteorological parameters which makes the
task difficult to define the mechanism of Tropical Cyclone
intensity prediction.
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2.  To develop hybrid learning models that can improve the accuracy of tropical
cyclone intensity predictions. By combining multiple machine learning or
statistical techniques(using both empirical and numerical modelling techniques)
 To develop models that can provide reliable and timely predictions of cyclone
intensification well in advance. By improving the lead time and warning systems,
researchers aim to help communities and authorities make better-informed
decisions and take appropriate actions to mitigate the impact of tropical
cyclones.
 To quantify and address uncertainties associated with predictions. The objective
is to provide not only point forecasts but also probabilistic estimates, which can
help decision-makers understand the range of potential outcomes and associated
risks.
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3.  Here, a hybrid deep learning model is built using historical observations
collected from various sources to perform a data-driven prediction
ofTropical Cyclone’s intensity using regression model.
 This hybrid model utilizes Convolutional Neural Network architectures for
feature extraction and machine learning models for regression.
 Application and Real-Time Implementation: The study may explore the
practical application of the developed hybrid learning models in real-time
forecasting and operational settings.
 This could involve implementing the models within existing weather
forecasting systems or assessing their feasibility for integration into decision
support systems used by meteorological agencies or disaster management
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4.  Varalakshmi.P et al. investigated Tropical cyclone
intensity prediction based on hybrid learning techniques.
 Xin Wang et al. proposed Tropical cyclone intensity
change prediction based on surrounding environmental
conditions with Deep learning.
 Gao S et al. explained Improvements in typhoon intensity
change classification by incorporating an ocean coupling
potential intensity index into decision trees.
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5.  Li Y et al. explored Spectral spatial classification of hyper
spectral imagery with 3D convolutional neural network.
 Lee J ability to precisely extract Tropical cyclone intensity
estimation using multi-dimensional convolutional neural
networks from geostationary satellite data.
 Dvorak V et al. proposed Tropical cyclone intensity
analysis and forecasting from satellite imagery.
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6.  The existing literature provides a strong foundation for the proposed
study on tropical cyclone intensity prediction using hybrid learning
techniques.
 By building upon the previous research, this study aims to contribute
to the field by developing novel hybrid models, incorporating diverse
data sources, addressing uncertainty, and improving lead time and
warning systems.
 The findings from this work can have significant implications for
disaster management and community resilience in the face of tropical
cyclones.
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7.  Tropical cyclones pose significant threats to coastal
communities and require accurate intensity prediction for
effective disaster preparedness and response.
 This research proposal aims to explore the application of
hybrid learning techniques in improving tropical cyclone
intensity prediction.
 By combining multiple algorithms and integrating diverse
data sources, we aim to enhance forecast accuracy and
provide valuable insights into cyclone dynamics.
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8.  Design and develop hybrid learning models using a
combination of machine learning algorithms, statistical
methods, and physical models. Image processing
technology is also used to augment data from a small
number of tropical cyclone samples to generate the
training set.
 Train and validate the models using the collected data,
and evaluate their performance using appropriate metrics
such as accuracy, precision, recall, and error metrics
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9.  Overall, the proposed research introduces novelty by
combining hybrid learning techniques, diverse data
integration, uncertainty quantification, and improved lead
time in the context of tropical cyclone intensity prediction.
 By addressing these aspects, the research aims to advance
the field and contribute to more accurate and reliable
predictions, ultimately benefiting disaster management and
coastal community resilience.
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10.  P Varalakshmi, N Vasumathi, R Venkatesan, Tropical Cyclone intensity prediction based on hybrid
learning techniques, Journal of Earth System, volume 132, Article number: 28 (2023) Springer.
 Xin Wang, Wenke Wang and Bing Yan, Tropical Cyclone Intensity Change Prediction Based on
Surrounding EnvironmentalConditions with Deep Learning,Water 2020, 12, 2685.
 Gao S, Zhang W, Liu J, Lin I.I, Chiu L.S, Cao K, Improvements in Typhoon Intensity Change
Classification by Incorporating an Ocean Coupling Potential Intensity Index into Decision Trees,
Weather Forecast. 2016, 31, 95–106.
 Li Y, Zhang H, ShenQ, Spectral–Spatial Classification of Hyper spectral Imagery with 3D
Convolutional Neural Network. RemoteSens. 2017, 967.
 Lee J, Im J, Cha D, Park H, Sim S,TropicalCyclone Intensity Estimation Using Multi-Dimensional
Convolutional Neural Networks from Geostationary Satellite Data. Remote Sens. 2019, 12, 108.
 Chen B F, Chen B, Lin H T, Elsberry R.L, Estimating Tropical Cyclone Intensity by Satellite Imagery
UtilizingConvolutional Neural Networks.Weather Forecast. 2019, 34, 447–465.
 Dvorak V 1975 Tropical cyclone intensity analysis and forecasting from satellite imagery; Mon. Wea.
Rev. 103 420–430.
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11.  “Rice Quality Prediction using Convolution Neural Network”, IEEE
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ThankYou.
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