TELKOMNIKA Telecommunication, Computing, Electronics and Control
Precipitation prediction using recurrent neural networks and long short-term memory
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Precipitation prediction using recurrent neural networks and long short-term memory
Precipitation prediction using recurrent neural networks and long short-term memory
Subject
Deep learning, Long short-term memory, Meteorology data, Precipitation prediction, Recurrent neural networks
Description
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.
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.
Creator
Mishka Alditya Priatna, Esmeralda C. Djamal
Source
DOI: 10.12928/TELKOMNIKA.v18i5.14887
Publisher
Universitas Ahmad Dahlan
Date
October 2020
Contributor
Sri Wahyuni
Rights
ISSN: 1693-6930
Relation
http://journal.uad.ac.id/index.php/TELKOMNIKA
Format
PDF
Language
English
Type
Text
Coverage
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Files
Collection
Citation
Mishka Alditya Priatna, Esmeralda C. Djamal, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
Precipitation prediction using recurrent neural networks and long short-term memory,” Repository Horizon University Indonesia, accessed October 31, 2025, https://repository.horizon.ac.id/items/show/4080.
Precipitation prediction using recurrent neural networks and long short-term memory,” Repository Horizon University Indonesia, accessed October 31, 2025, https://repository.horizon.ac.id/items/show/4080.