Title

Bayesian network model for temperature forecasting in Dubai

Author First name, Last name, Institution

L. Smail, Zayed University

Document Type

Conference Proceeding

Source of Publication

AIP Conference Proceedings

Publication Date

10-25-2018

Abstract

© 2018 Author(s). In this paper, we deal with the problem of weather forecasting using Bayesian networks. The study focuses on the data representing Dubai weather conditions. The variables used in this study are as follows: maximum and minimum temperature, mean temperature, mean relative humidity, rainfall, and wind speed. The National Centre of Meteorology and Seismology (NCMS) gathered the weather data from Dubai Airport Station located at latitude: 25°15' and longitude: 55°20' starting from January 2004 to December 2014. The values available represent month averages. We used these data to learn the Bayesian network structure and parameters. Inference in the Bayesian network helped in forecasting the maximum and minimum temperature of the succeeding months through dynamic Bayesian networks. The model showed 72% and 90% overall precision in forecasting minimum and maximum temperature, respectively.

ISBN

9780735417458

ISSN

0094-243X

Publisher

American Institute of Physics Inc.

Volume

2025

Disciplines

Life Sciences

Scopus ID

85056182941

Indexed in Scopus

yes

Open Access

no

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