A Recurrent Neural Network Model for Weather Forecasting

Yunus Emre Cebeci

doi:10.1109/UBMK.2019.8907196

A Recurrent Neural Network Model for Weather Forecasting

Yunus Emre Cebeci^*

^*Corresponding author for this work

Graduate School

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Citations (Scopus)

Abstract

This paper compares data mining approaches for weather forecasting from one-dimensional and multidimensional meteorological weather data. Linear and nonlinear methods are applied and more successful results are obtained from nonlinear methods. The best result is obtained with LSTM(Long short-term memory). RFE(Recursive Feature Elimination) is used for subset feature selection and it increases one-dimensional MLP(Multi Layer Perceptron) model accuracy. In addition, Grid Search is used for hyperparameter tuning and early stopping is used to avoid overfitting and underfitting.

Original language	English
Title of host publication	UBMK 2019 - Proceedings, 4th International Conference on Computer Science and Engineering
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	591-595
Number of pages	5
ISBN (Electronic)	9781728139647
DOIs	https://doi.org/10.1109/UBMK.2019.8907196
Publication status	Published - Sept 2019
Event	4th International Conference on Computer Science and Engineering, UBMK 2019 - Samsun, Turkey Duration: 11 Sept 2019 → 15 Sept 2019

Publication series

Name	UBMK 2019 - Proceedings, 4th International Conference on Computer Science and Engineering

Conference

Conference	4th International Conference on Computer Science and Engineering, UBMK 2019
Country/Territory	Turkey
City	Samsun
Period	11/09/19 → 15/09/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

Adaptive Learning
Data Mining
Deep Learning
LSTM
RFE

Access to Document

10.1109/UBMK.2019.8907196

Cite this

Cebeci, Y. E. (2019). A Recurrent Neural Network Model for Weather Forecasting. In UBMK 2019 - Proceedings, 4th International Conference on Computer Science and Engineering (pp. 591-595). Article 8907196 (UBMK 2019 - Proceedings, 4th International Conference on Computer Science and Engineering). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/UBMK.2019.8907196

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abstract = "This paper compares data mining approaches for weather forecasting from one-dimensional and multidimensional meteorological weather data. Linear and nonlinear methods are applied and more successful results are obtained from nonlinear methods. The best result is obtained with LSTM(Long short-term memory). RFE(Recursive Feature Elimination) is used for subset feature selection and it increases one-dimensional MLP(Multi Layer Perceptron) model accuracy. In addition, Grid Search is used for hyperparameter tuning and early stopping is used to avoid overfitting and underfitting.",

keywords = "Adaptive Learning, Data Mining, Deep Learning, LSTM, RFE",

author = "Cebeci, {Yunus Emre}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 4th International Conference on Computer Science and Engineering, UBMK 2019 ; Conference date: 11-09-2019 Through 15-09-2019",

year = "2019",

month = sep,

doi = "10.1109/UBMK.2019.8907196",

language = "English",

series = "UBMK 2019 - Proceedings, 4th International Conference on Computer Science and Engineering",

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Cebeci, YE 2019, A Recurrent Neural Network Model for Weather Forecasting. in UBMK 2019 - Proceedings, 4th International Conference on Computer Science and Engineering., 8907196, UBMK 2019 - Proceedings, 4th International Conference on Computer Science and Engineering, Institute of Electrical and Electronics Engineers Inc., pp. 591-595, 4th International Conference on Computer Science and Engineering, UBMK 2019, Samsun, Turkey, 11/09/19. https://doi.org/10.1109/UBMK.2019.8907196

A Recurrent Neural Network Model for Weather Forecasting. / Cebeci, Yunus Emre.
UBMK 2019 - Proceedings, 4th International Conference on Computer Science and Engineering. Institute of Electrical and Electronics Engineers Inc., 2019. p. 591-595 8907196 (UBMK 2019 - Proceedings, 4th International Conference on Computer Science and Engineering).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - This paper compares data mining approaches for weather forecasting from one-dimensional and multidimensional meteorological weather data. Linear and nonlinear methods are applied and more successful results are obtained from nonlinear methods. The best result is obtained with LSTM(Long short-term memory). RFE(Recursive Feature Elimination) is used for subset feature selection and it increases one-dimensional MLP(Multi Layer Perceptron) model accuracy. In addition, Grid Search is used for hyperparameter tuning and early stopping is used to avoid overfitting and underfitting.

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A Recurrent Neural Network Model for Weather Forecasting

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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Cite this