Short-term localized weather forecasting by using different artificial neural network algorithm in tropical climate

Noor Zuraidin Mohd-Safar; David Lorater Ndzi; Ioannis Kagalidis; Yanyan Yang; Ammar Zakaria

doi:10.1007/978-3-319-56991-8_35

Short-term localized weather forecasting by using different artificial neural network algorithm in tropical climate

Noor Zuraidin Mohd-Safar, David Lorater Ndzi, Ioannis Kagalidis, Yanyan Yang, Ammar Zakaria

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

6 Citations (Scopus)

Abstract

This paper evaluates the performance of localized weather forecasting model using Artificial Neural Network (ANN) with different ANN algorithms in a tropical climate. Three ANN algorithms namely, Levenberg-Marquardt, Bayesian Regularization and Scaled Conjugate Gradient are used in the short-term weather forecasting model. The study focuses on the data from North-West Malaysia (Chuping). Meteorological data such as atmospheric pressure, temperature, dew point, humidity and wind speed are used as input parameters. One hour ahead forecasted results for atmospheric pressure, temperature and humidity were compared and analyzed and they show that ANN with Levenberg-Marquardt algorithm performs best.

Original language	English
Title of host publication	Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016
Editors	Yaxin Bi, Supriya Kapoor, Rahul Bhatia
Publisher	Springer International Publishing AG
Pages	463-476
ISBN (Electronic)	978-3-319-56991-8
ISBN (Print)	978-3-319-56990-1
DOIs	https://doi.org/10.1007/978-3-319-56991-8_35
Publication status	E-pub ahead of print - 23 Aug 2017
Externally published	Yes

Publication series

Name	Lecture Notes in Networks and Systems
Publisher	Springer
ISSN (Print)	2367-3370

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 13 Climate Action

Keywords

artificial neural network, hort-term weather forecasting, tropical climate, ANN

Access to Document

10.1007/978-3-319-56991-8_35

Cite this

Mohd-Safar, N. Z., Ndzi, D. L., Kagalidis, I., Yang, Y., & Zakaria, A. (2017). Short-term localized weather forecasting by using different artificial neural network algorithm in tropical climate. In Y. Bi, S. Kapoor, & R. Bhatia (Eds.), Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016 (pp. 463-476). (Lecture Notes in Networks and Systems). Springer International Publishing AG. Advance online publication. https://doi.org/10.1007/978-3-319-56991-8_35

Mohd-Safar, Noor Zuraidin ; Ndzi, David Lorater ; Kagalidis, Ioannis et al. / Short-term localized weather forecasting by using different artificial neural network algorithm in tropical climate. Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. editor / Yaxin Bi ; Supriya Kapoor ; Rahul Bhatia. Springer International Publishing AG, 2017. pp. 463-476 (Lecture Notes in Networks and Systems).

@inproceedings{dbd467f7891c45c08ff360545a855a1f,

title = "Short-term localized weather forecasting by using different artificial neural network algorithm in tropical climate",

abstract = "This paper evaluates the performance of localized weather forecasting model using Artificial Neural Network (ANN) with different ANN algorithms in a tropical climate. Three ANN algorithms namely, Levenberg-Marquardt, Bayesian Regularization and Scaled Conjugate Gradient are used in the short-term weather forecasting model. The study focuses on the data from North-West Malaysia (Chuping). Meteorological data such as atmospheric pressure, temperature, dew point, humidity and wind speed are used as input parameters. One hour ahead forecasted results for atmospheric pressure, temperature and humidity were compared and analyzed and they show that ANN with Levenberg-Marquardt algorithm performs best. ",

keywords = "artificial neural network, hort-term weather forecasting, tropical climate, ANN",

author = "Mohd-Safar, \{Noor Zuraidin\} and Ndzi, \{David Lorater\} and Ioannis Kagalidis and Yanyan Yang and Ammar Zakaria",

year = "2017",

month = aug,

day = "23",

doi = "10.1007/978-3-319-56991-8\_35",

language = "English",

isbn = "978-3-319-56990-1",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer International Publishing AG",

pages = "463--476",

editor = "Yaxin Bi and Supriya Kapoor and Rahul Bhatia",

booktitle = "Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016",

address = "Switzerland",

}

Mohd-Safar, NZ, Ndzi, DL, Kagalidis, I, Yang, Y & Zakaria, A 2017, Short-term localized weather forecasting by using different artificial neural network algorithm in tropical climate. in Y Bi, S Kapoor & R Bhatia (eds), Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. Lecture Notes in Networks and Systems, Springer International Publishing AG, pp. 463-476. https://doi.org/10.1007/978-3-319-56991-8_35

Short-term localized weather forecasting by using different artificial neural network algorithm in tropical climate. / Mohd-Safar, Noor Zuraidin; Ndzi, David Lorater; Kagalidis, Ioannis et al.
Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. ed. / Yaxin Bi; Supriya Kapoor; Rahul Bhatia. Springer International Publishing AG, 2017. p. 463-476 (Lecture Notes in Networks and Systems).

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

TY - GEN

T1 - Short-term localized weather forecasting by using different artificial neural network algorithm in tropical climate

AU - Mohd-Safar, Noor Zuraidin

AU - Ndzi, David Lorater

AU - Kagalidis, Ioannis

AU - Yang, Yanyan

AU - Zakaria, Ammar

PY - 2017/8/23

Y1 - 2017/8/23

N2 - This paper evaluates the performance of localized weather forecasting model using Artificial Neural Network (ANN) with different ANN algorithms in a tropical climate. Three ANN algorithms namely, Levenberg-Marquardt, Bayesian Regularization and Scaled Conjugate Gradient are used in the short-term weather forecasting model. The study focuses on the data from North-West Malaysia (Chuping). Meteorological data such as atmospheric pressure, temperature, dew point, humidity and wind speed are used as input parameters. One hour ahead forecasted results for atmospheric pressure, temperature and humidity were compared and analyzed and they show that ANN with Levenberg-Marquardt algorithm performs best.

AB - This paper evaluates the performance of localized weather forecasting model using Artificial Neural Network (ANN) with different ANN algorithms in a tropical climate. Three ANN algorithms namely, Levenberg-Marquardt, Bayesian Regularization and Scaled Conjugate Gradient are used in the short-term weather forecasting model. The study focuses on the data from North-West Malaysia (Chuping). Meteorological data such as atmospheric pressure, temperature, dew point, humidity and wind speed are used as input parameters. One hour ahead forecasted results for atmospheric pressure, temperature and humidity were compared and analyzed and they show that ANN with Levenberg-Marquardt algorithm performs best.

KW - artificial neural network, hort-term weather forecasting, tropical climate, ANN

U2 - 10.1007/978-3-319-56991-8_35

DO - 10.1007/978-3-319-56991-8_35

M3 - Conference contribution

SN - 978-3-319-56990-1

T3 - Lecture Notes in Networks and Systems

SP - 463

EP - 476

BT - Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016

A2 - Bi, Yaxin

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PB - Springer International Publishing AG

ER -

Mohd-Safar NZ, Ndzi DL, Kagalidis I, Yang Y, Zakaria A. Short-term localized weather forecasting by using different artificial neural network algorithm in tropical climate. In Bi Y, Kapoor S, Bhatia R, editors, Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. Springer International Publishing AG. 2017. p. 463-476. (Lecture Notes in Networks and Systems). Epub 2017 Aug 23. doi: 10.1007/978-3-319-56991-8_35

Short-term localized weather forecasting by using different artificial neural network algorithm in tropical climate

Abstract

Publication series

UN SDGs

Keywords

Access to Document

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