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Incremental Gaussian Granular Fuzzy Modeling Applied to Hurricane Track Forecasting

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Incremental Gaussian Granular Fuzzy Modeling Applied to Hurricane Track Forecasting. / Almeida Soares, Eduardo; Camargo, Heloisa A.; Camargo, Suzana J.; Leite, Daniel F.

2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2018.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Almeida Soares, E, Camargo, HA, Camargo, SJ & Leite, DF 2018, Incremental Gaussian Granular Fuzzy Modeling Applied to Hurricane Track Forecasting. in 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE. https://doi.org/10.1109/fuzz-ieee.2018.8491587

APA

Almeida Soares, E., Camargo, H. A., Camargo, S. J., & Leite, D. F. (2018). Incremental Gaussian Granular Fuzzy Modeling Applied to Hurricane Track Forecasting. In 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) IEEE. https://doi.org/10.1109/fuzz-ieee.2018.8491587

Vancouver

Almeida Soares E, Camargo HA, Camargo SJ, Leite DF. Incremental Gaussian Granular Fuzzy Modeling Applied to Hurricane Track Forecasting. In 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE. 2018 https://doi.org/10.1109/fuzz-ieee.2018.8491587

Author

Almeida Soares, Eduardo ; Camargo, Heloisa A. ; Camargo, Suzana J. ; Leite, Daniel F. / Incremental Gaussian Granular Fuzzy Modeling Applied to Hurricane Track Forecasting. 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2018.

Bibtex

@inproceedings{8e7e3e0c3ba9421e9a8c2a64a889da52,
title = "Incremental Gaussian Granular Fuzzy Modeling Applied to Hurricane Track Forecasting",
abstract = "This paper presents a Gaussian fuzzy set-based evolving modeling method, FBeM-G, to predict tropical cyclone tracks 6 hours in advance. FBeM-G summarizes similar data into Gaussian granules evolved from a sequence of data. It uses a recursive learning algorithm to update its parameters and structure over time and therefore is able to cope with nonstationarities. Past values of latitude, longitude, maximum sustained wind, pressure and wind radii in different quadrants of the Katrina, Sandy and Wilma tropical cyclones were obtained from the `best track' analysis provided by the National Hurricane Center (NOAA). An ensemble of cloud-based and fuzzy models was considered to compare the estimated tracks. FBeM-G provided more accurate 6-hourly track estimates using a smaller number of local models and parameters. Although less accurate, longer-term estimates given by the ensemble approach became closer to those provided by FBeM-G. An outer approximation of the pointwise track prediction is a particular characteristic of the method that is useful to determine risk areas and actions to be taken.",
author = "{Almeida Soares}, Eduardo and Camargo, {Heloisa A.} and Camargo, {Suzana J.} and Leite, {Daniel F.}",
year = "2018",
month = oct,
day = "15",
doi = "10.1109/fuzz-ieee.2018.8491587",
language = "English",
isbn = "9781509060214",
booktitle = "2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Incremental Gaussian Granular Fuzzy Modeling Applied to Hurricane Track Forecasting

AU - Almeida Soares, Eduardo

AU - Camargo, Heloisa A.

AU - Camargo, Suzana J.

AU - Leite, Daniel F.

PY - 2018/10/15

Y1 - 2018/10/15

N2 - This paper presents a Gaussian fuzzy set-based evolving modeling method, FBeM-G, to predict tropical cyclone tracks 6 hours in advance. FBeM-G summarizes similar data into Gaussian granules evolved from a sequence of data. It uses a recursive learning algorithm to update its parameters and structure over time and therefore is able to cope with nonstationarities. Past values of latitude, longitude, maximum sustained wind, pressure and wind radii in different quadrants of the Katrina, Sandy and Wilma tropical cyclones were obtained from the `best track' analysis provided by the National Hurricane Center (NOAA). An ensemble of cloud-based and fuzzy models was considered to compare the estimated tracks. FBeM-G provided more accurate 6-hourly track estimates using a smaller number of local models and parameters. Although less accurate, longer-term estimates given by the ensemble approach became closer to those provided by FBeM-G. An outer approximation of the pointwise track prediction is a particular characteristic of the method that is useful to determine risk areas and actions to be taken.

AB - This paper presents a Gaussian fuzzy set-based evolving modeling method, FBeM-G, to predict tropical cyclone tracks 6 hours in advance. FBeM-G summarizes similar data into Gaussian granules evolved from a sequence of data. It uses a recursive learning algorithm to update its parameters and structure over time and therefore is able to cope with nonstationarities. Past values of latitude, longitude, maximum sustained wind, pressure and wind radii in different quadrants of the Katrina, Sandy and Wilma tropical cyclones were obtained from the `best track' analysis provided by the National Hurricane Center (NOAA). An ensemble of cloud-based and fuzzy models was considered to compare the estimated tracks. FBeM-G provided more accurate 6-hourly track estimates using a smaller number of local models and parameters. Although less accurate, longer-term estimates given by the ensemble approach became closer to those provided by FBeM-G. An outer approximation of the pointwise track prediction is a particular characteristic of the method that is useful to determine risk areas and actions to be taken.

U2 - 10.1109/fuzz-ieee.2018.8491587

DO - 10.1109/fuzz-ieee.2018.8491587

M3 - Conference contribution/Paper

SN - 9781509060214

BT - 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

PB - IEEE

ER -