Weather Forecasting Using Artificial Neural Network and Bayesian Network
Klent Gomez Abistado*, Catherine N. Arellano**,
and Elmer A. Maravillas**
*Advanced World Systems Inc., Cebu City, Philippines
**Department of Computer Science, Cebu Institute of Technology University, Cebu City, Philippines
This paper presents a scheme of weather forecasting using artificial neural network (ANN) and Bayesian network. The study focuses on the data representing central Cebu weather conditions. The parameters used in this study are as follows: mean dew point, minimum temperature, maximum temperature, mean temperature, mean relative humidity, rainfall, average wind speed, prevailing wind direction, and mean cloudiness. The weather data were collected from the PAG-ASA Mactan-Cebu Station located at latitude: 10°19´, longitude: 123°59´ starting from January 2011 to December 2011 and the values available represent daily averages. These data were used for training the multi-layered backpropagation ANN in predicting the weather conditions of the succeeding days. Some outputs from the ANN, such as the humidity, temperature, and amount of rainfall, are fed to the Bayesian network for statistical analysis to forecast the probability of rain. Experiments show that the system achieved 93%–100% accuracy in forecasting weather conditions.
and Elmer A. Maravillas, “Weather Forecasting Using Artificial Neural Network and Bayesian Network,” J. Adv. Comput. Intell. Intell. Inform., Vol.18, No.5, pp. 812-817, 2014.
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