research-article

A Sparse Recurrent Neural Network for Trajectory Prediction of Atlantic Hurricanes

Authors:

Mina Moradi Kordmahalleh,

Mohammad Gorji Sefidmazgi,

Abdollah HomaifarAuthors Info & Claims

GECCO '16: Proceedings of the Genetic and Evolutionary Computation Conference 2016

Pages 957 - 964

https://doi.org/10.1145/2908812.2908834

Published: 20 July 2016 Publication History

Abstract

Hurricanes constitute major natural disasters that lead to destruction and loss of lives. Therefore, to reduce economic loss and to save human lives, an accurate forecast of hurricane occurrences is crucial. Despite the availability of data and advanced forecasting techniques, there is a need for effective methods with higher accuracy of prediction. We propose a sparse Recurrent Neural Network (RNN) with flexible topology for trajectory prediction of the Atlantic hurricanes. Topology of the RNN along with the strength of the connections are evolved by a customized Genetic Algorithm. The network is particularly suitable for modeling of hurricanes which have complex systems with unknown dynamics. For prediction of the future trajectories of a target hurricane, the Dynamic Time Warping (DTW) distances between direction of the target hurricane over time, and other hurricanes in the dataset are determined and compared. The most similar hurricanes to the target hurricane are then used for training of the network. Comparisons between the actual tracks of the hurricanes DEAN, SANDY, ISSAC and HUMBERTO, and the generated predictions by the sparse RNN for one and two steps ahead of time show that our approach is quite promising for this aim.

References

[1]

Atlantic tropical storm tracking by year. http://weather.unisys.com/hurricane/atlantic/.

[2]

National hurricane center, latitude/longitude distance calculator. http://www.nhc.noaa.gov/gccalc.shtml.

[3]

National hurricane center, track and intensity models. http://www.nhc.noaa.gov/modelsummary.shtml.

[4]

J. Aach and G. M. Church. Aligning gene expression time series with time warping algorithms. Bioinformatics, 17(6):495--508, 2001.

[5]

D. J. Berndt and J. Clifford. Using dynamic time warping to find patterns in time series. In KDD workshop, volume 10, pages 359--370. Seattle, WA, 1994.

Digital Library

[6]

D. Dasgupta and S. Forrest. Novelty detection in time series data using ideas from immunology. In Proceedings of the international conference on intelligent systems, pages 82--87, 1996.

[7]

J. J. R. Diez and C. J. A. González. Applying boosting to similarity literals for time series classification. In Multiple Classifier Systems, pages 210--219. Springer, 2000.

Digital Library

[8]

J. L. Elman. Finding structure in time. Cognitive Science, 14(2):179--211, 1990.

[9]

S. B. Goldenberg, C. W. Landsea, A. M. Mestas-Nu\ nez, and W. M. Gray. The recent increase in atlantic hurricane activity: Causes and implications. Science, 293(5529):474--479, 2001.

[10]

S. G. Gopalakrishnan, S. Goldenberg, T. Quirino, X. Zhang, F. Marks Jr, K.-S. Yeh, R. Atlas, and V. Tallapragada. Toward improving high-resolution numerical hurricane forecasting: Influence of model horizontal grid resolution, initialization, and physics. Weather and Forecasting, 27(3):647--666, 2012.

[11]

M. Gorji Sefidmazgi and M. Farrokhi. Online Calibration of Inertial Sensors Using Kalman Filters and Artificial Neural Networks. In 17th Iranian Conference on Electrical Engineering (ICEE), volume 7, pages 249--253, 2009.

[12]

M. Gorji Sefidmazgi, M. Moradi Kordmahalleh, and A. Homaifar. Identification of switched models in non-stationary time series based on coordinate-descent and genetic algorithm. In Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, GECCO Companion '15, pages 1399--1400, New York, NY, USA, 2015. ACM.

Digital Library

[13]

M. Gorji Sefidmazgi, M. Sayemuzzaman, A. Homaifar, M. K. Jha, and S. Liess. Trend analysis using non-stationary time series clustering based on the finite element method. Nonlinear Processes in Geophysics, 21(3):605--615, 2014.

[14]

T. M. Hall and S. Jewson. Statistical modelling of north atlantic tropical cyclone tracks. Tellus A, 59(4):486--498, 2007.

[15]

M. Islam, A. Sattar, F. Amin, X. Yao, and K. Murase. A new adaptive merging and growing algorithm for designing artificial neural networks. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 39(3):705--722, June 2009.

Digital Library

[16]

M. I. Jordan. Supervised learning and systems with excess degrees of freedom. Report, University of Massachusetts, 1988.

Digital Library

[17]

S. Kang and C. Isik. Partially connected feedforward neural networks structured by input types. IEEE transactions on neural networks, 16(1):175--184, 2005.

Digital Library

[18]

E. Keogh and C. A. Ratanamahatana. Exact indexing of dynamic time warping. Knowledge and information systems, 7(3):358--386, 2005.

[19]

H. S. Kim, C. H. Ho, J. H. Kim, and P. S. Chu. Track-pattern-based model for seasonal prediction of tropical cyclone activity in the western north pacific. Journal of Climate, 25(13):4660--4678, 2012.

[20]

H. S. Kim, J. H. Kim, C. H. Ho, and P. S. Chu. Pattern classification of typhoon tracks using the fuzzy c-means clustering method. Journal of Climate, 24(2):488--508, 2011.

[21]

M. M. Kordmahalleh, A. Homaifar, and D. Bkc. Hierarchical multi-label gene function prediction using adaptive mutation in crowding niching. In Bioinformatics and Bioengineering (BIBE), 2013 IEEE 13th International Conference on, pages 1--6, Nov 2013.

[22]

M. M. Kordmahalleh, M. G. Sefidmazgi, and A. Homaifar. A bilevel parameter tuning strategy of partially connected anns. In 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), pages 793--798, Dec 2015.

[23]

M. M. Kordmahalleh, M. G. Sefidmazgi, A. Homaifar, A. Karimoddini, A. Guiseppi-Elie, and J. L. Graves. Delayed and hidden variables interactions in gene regulatory networks. In Bioinformatics and Bioengineering (BIBE), 2014 IEEE International Conference on, pages 23--29, Nov 2014.

Digital Library

[24]

T. N. Krishnamurti, C. Kishtawal, Z. Zhang, T. LaRow, D. Bachiochi, E. Williford, S. Gadgil, and S. Surendran. Multimodel ensemble forecasts for weather and seasonal climate. Journal of Climate, 13(23):4196--4216, 2000.

[25]

R. S. Lee and J. N. Liu. Tropical cyclone identification and tracking system using integrated neural oscillatory elastic graph matching and hybrid rbf network track mining techniques. Neural Networks, IEEE Transactions on, 11(3):680--689, 2000.

Digital Library

[26]

S. Macpherson, L. Garand, J. Aparicio, M. Buehner, G. Deblonde, M. Charron, M. Roch, C. Charette, and A. Beaulne. Recent developments in assimilation of satellite data in the msc 4d-var analysis and forecast system. Channels, 4:10.

[27]

M. Moradi Kordmahalleh, M. Gorji Sefidmazgi, A. Homaifar, D. B. KC, and A. Guiseppi-Elie. Time-series forecasting with evolvable partially connected artificial neural network. In Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, GECCO Comp '14, pages 79--80, New York, NY, USA, 2014. ACM.

Digital Library

[28]

T. S. Rao, S. S. Rao, and C. R. Rao. Time Series Analysis: Methods and Applications. Elsevier, 2012.

[29]

O. S. Rozanova, J. L. Yu, and C. K. Hu. Typhoon eye trajectory based on a mathematical model: Comparing with observational data. Nonlinear Analysis: Real World Applications, 11(3):1847--1861, 2010.

[30]

Y. Su, S. Chelluboina, M. Hahsler, and M. Dunham. A new data mining model for hurricane intensity prediction. In Data Mining Workshops (ICDMW), 2010 IEEE International Conference on, pages 98--105, Dec 2010.

Digital Library

[31]

G. A. Vecchi, M. Zhao, H. Wang, G. Villarini, A. Rosati, A. Kumar, I. M. Held, and R. Gudgel. Statistical-dynamical predictions of seasonal north atlantic hurricane activity. Monthly Weather Review, 139(4):1070--1082, 2011.

[32]

Y. Wang, W. Zhang, and W. Fu. Back propogation (bp)-neural network for tropical cyclone track forecast. In Geoinformatics, 2011 19th International Conference on, pages 1--4. IEEE, 2011.

[33]

J. M. Zamarre\ no and P. Vega. State space neural network. properties and application. Neural Networks, 11(6):1099--1112, 1998.

Digital Library

[34]

L. ZOU, A. REN, F. XU, and X. ZHANG. Typhoon track forecasting based on gis spatial analyses. Journal of Tsinghua University (Science and Technology), 12:002, 2008.

Cited By

Zhang CZhang JZhao JZhang T(2024)Prediction of Drift Trajectory in the Ocean Using Double-Branch Adaptive Span AttentionJournal of Marine Science and Engineering10.3390/jmse1206101612:6(1016)Online publication date: 18-Jun-2024
https://doi.org/10.3390/jmse12061016
Qi FMa XYang J(2024)Tropical cyclone tracking for autonomous underwater vehicles based on forecast path correction modelOcean Engineering10.1016/j.oceaneng.2024.116768293(116768)Online publication date: Feb-2024
https://doi.org/10.1016/j.oceaneng.2024.116768
Jeon SKim J(2024)Artificial intelligence to predict climate and weather changeJMST Advances10.1007/s42791-024-00068-yOnline publication date: 27-Feb-2024
https://doi.org/10.1007/s42791-024-00068-y
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Index Terms

A Sparse Recurrent Neural Network for Trajectory Prediction of Atlantic Hurricanes
1. Applied computing
  1. Operations research
    1. Forecasting
2. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Bio-inspired approaches
        Genetic algorithms
      2. Neural networks

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Published In

cover image ACM Conferences

GECCO '16: Proceedings of the Genetic and Evolutionary Computation Conference 2016

July 2016

1196 pages

ISBN:9781450342063

DOI:10.1145/2908812

Editor:
Tobias Friedrich
Hasso Plattner Institute
,
General Chair:
Frank Neumann
University of Adelaide
,
Program Chair:
Andrew M. Sutton
Hasso Plattner Institute

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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New York, NY, United States

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Published: 20 July 2016

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Expeditions in Computing by the National Science Foundation

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GECCO '16

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SIGEVO

GECCO '16: Genetic and Evolutionary Computation Conference

July 20 - 24, 2016

Colorado, Denver, USA

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GECCO '16 Paper Acceptance Rate 137 of 381 submissions, 36%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Zhang CZhang JZhao JZhang T(2024)Prediction of Drift Trajectory in the Ocean Using Double-Branch Adaptive Span AttentionJournal of Marine Science and Engineering10.3390/jmse1206101612:6(1016)Online publication date: 18-Jun-2024
https://doi.org/10.3390/jmse12061016
Qi FMa XYang J(2024)Tropical cyclone tracking for autonomous underwater vehicles based on forecast path correction modelOcean Engineering10.1016/j.oceaneng.2024.116768293(116768)Online publication date: Feb-2024
https://doi.org/10.1016/j.oceaneng.2024.116768
Jeon SKim J(2024)Artificial intelligence to predict climate and weather changeJMST Advances10.1007/s42791-024-00068-yOnline publication date: 27-Feb-2024
https://doi.org/10.1007/s42791-024-00068-y
Li TChe KLu JZeng YLv WLiang Z(2024)Tropical cyclone ensemble forecast framework based on spatiotemporal modelEarth Science Informatics10.1007/s12145-024-01418-z17:5(4791-4807)Online publication date: 29-Jul-2024
https://doi.org/10.1007/s12145-024-01418-z
GIFFARD‐ROISIN SMallet CChehata N(2024)Recent Advances in Tropical Cyclone Forecasting Using Machine Learning on Reanalysis and Remote SensingMultitemporal Earth Observation Image Analysis10.1002/9781394306657.ch7(223-251)Online publication date: 19-Jul-2024
https://doi.org/10.1002/9781394306657.ch7
Farmanifard SAlesheikh ASharif MAlizadeh D(2023)Tropical Storm Path Prediction Using Long Short-Term Memory Model, Similarity Measurement of Trajectories and Contextual InformationJournal of Geospatial Information Technology10.61186/jgit.11.2.111:2(1-16)Online publication date: 1-Sep-2023
https://doi.org/10.61186/jgit.11.2.1
Karn IManikandan SAbdullah A(2023)Disseminating the Process of Hurricane Path Prediction using Multilayer Perceptron and Support Vector Machine upon Varied Kernel Functions2023 International Conference for Advancement in Technology (ICONAT)10.1109/ICONAT57137.2023.10080280(1-5)Online publication date: 24-Jan-2023
https://doi.org/10.1109/ICONAT57137.2023.10080280
Li SJiang JZhou QLu X(2023)Pedstrian Trajectory Prediction Based on LSTM2023 7th CAA International Conference on Vehicular Control and Intelligence (CVCI)10.1109/CVCI59596.2023.10397459(1-5)Online publication date: 27-Oct-2023
https://doi.org/10.1109/CVCI59596.2023.10397459
Farmanifard SAsghar Alesheikh ASharif M(2023)A context-aware hybrid deep learning model for the prediction of tropical cyclone trajectoriesExpert Systems with Applications10.1016/j.eswa.2023.120701231(120701)Online publication date: Nov-2023
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Kapoor ANegi AMarshall LChandra R(2023)Cyclone trajectory and intensity prediction with uncertainty quantification using variational recurrent neural networksEnvironmental Modelling & Software10.1016/j.envsoft.2023.105654162(105654)Online publication date: Apr-2023
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