Tropical Cyclone Intensity Estimation Using Deep Convolutional Neural Networks

Tropical cyclone intensity estimation is a challenging task as it required domain knowledge while extracting features, significant pre-processing, various sets of parameters obtained from satellites, and human intervention for analysis. The inconsistency of results, significant pre-processing of data, complexity of the problem domain, and problems on generalizability are some of the issues related to intensity estimation. In this study, we design a deep convolutional neural network architecture for categorizing hurricanes based on intensity using graphics processing unit. Our model has achieved better accuracy and lower root-mean-square error by just using satellite images than ’state-of-the-art’ techniques. Visualizations of learned features at various layers and their deconvolutions are also presented for understanding the learning process.

[1]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[2]  Stuart E. Dreyfus,et al.  On derivation of MLP backpropagation from the Kelley-Bryson optimal-control gradient formula and its application , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[3]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[4]  J. Knaff,et al.  An Evaluation of Dvorak Technique-Based Tropical Cyclone Intensity Estimates , 2010 .

[5]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[6]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[7]  Yoshua Bengio,et al.  Convolutional networks for images, speech, and time series , 1998 .

[8]  Timothy L. Olander,et al.  The Dvorak Tropical Cyclone Intensity Estimation Technique: A Satellite-Based Method that Has Endured for over 30 Years , 2006 .

[9]  J. Scott Tyo,et al.  Estimating Tropical Cyclone Intensity from Infrared Image Data , 2011 .

[10]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[11]  Luca Maria Gambardella,et al.  Flexible, High Performance Convolutional Neural Networks for Image Classification , 2011, IJCAI.

[12]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[13]  V. F. Dvorak.  Tropical Cyclone Intensity Analysis and Forecasting from Satellite Imagery , 1975 .

[14]  Henry J. Kelley,et al.  Gradient Theory of Optimal Flight Paths , 1960 .

[15]  Patrice Y. Simard,et al.  High Performance Convolutional Neural Networks for Document Processing , 2006 .

[16]  Hod Lipson,et al.  Understanding Neural Networks Through Deep Visualization , 2015, ArXiv.

[17]  J. Scott Tyo,et al.  Objective Measures of Tropical Cyclone Structure and Intensity Change From Remotely Sensed Infrared Image Data , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[19]  Yann LeCun,et al.  Convolutional networks and applications in vision , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.

[20]  V. F. Dvorak.  A technique for the analysis and forecasting of tropical cyclone intensities from satellite pictures , 1973 .

[21]  Léon Bottou,et al.  Stochastic Gradient Descent Tricks , 2012, Neural Networks: Tricks of the Trade.

[22]  J. Scott Tyo,et al.  Tropical Cyclone Intensity Estimation in the North Atlantic Basin Using an Improved Deviation Angle Variance Technique , 2012 .

[23]  Lawrence D. Jackel,et al.  Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.

[24]  Y-Lan Boureau,et al.  Learning Convolutional Feature Hierarchies for Visual Recognition , 2010, NIPS.

[25]  Hyun Ah Song,et al.  Hierarchical Representation Using NMF , 2013, ICONIP.

[26]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[27]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[28]  Timothy L. Olander,et al.  Reprocessing the Most Intense Historical Tropical Cyclones in the Satellite Era Using the Advanced Dvorak Technique , 2016 .

[29]  J. Scott Tyo,et al.  Satellite-Derived Tropical Cyclone Intensity in the North Pacific Ocean Using the Deviation-Angle Variance Technique , 2014 .