GDL

Journal from gdlhub / 2021-01-25 09:11:58
By : Mishka Alditya Priatna, Esmeralda C. Djamal, Telkomnika
Created : 2021-01-25, with 1 files

Keyword : deep learning; long short-term memory; meteorology data; precipitation prediction; recurrent neural networks;
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/14887
Document Source : web

Prediction of meteorological variables such as precipitation, temperature, wind speed, and solar radiation is beneficial for human life. The variable observations data is available from time to time for more than thirty years, scattered each observation station makes the opportunity to map patterns into predictions. However, the complexity of weather variables is very high, one of which is influenced by Decadal phenomena such as El-Nino Southern Oscillation and IOD. Weather predictions can be reviewed for the duration, prediction variables, and observation stations. This research proposed precipitation prediction using recurrent neural networks and long short-term memory. Experiments were carried out using the prediction duration factor, the period as a feature and the amount of data set used, and the optimization model. The results showed that the time-lapse as a shorter feature gives good accuracy. Also, the duration of weekly predictions provides more accuracy than monthly, which is 85.71% compared to 83.33% of the validation data

Give Comment ?#(0) | Bookmark

Property	Value
Publisher ID	gdlhub
Organization	Telkomnika
Contact Name	Herti Yani, S.Kom
Address	Jln. Jenderal Sudirman
City	Jambi
Region	Jambi
Country	Indonesia
Phone	0741-35095
Fax	0741-35093
Administrator E-mail	elibrarystikom@gmail.com
CKO E-mail	elibrarystikom@gmail.com