TELKOMNIKA Telecommunication, Computing, Electronics and Control
Prediction of rainfall using improved deep learning with particle swarm optimization
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Prediction of rainfall using improved deep learning with particle swarm optimization
Prediction of rainfall using improved deep learning with particle swarm optimization
Subject
Deep PSO, Forecasting, Improved deep learning, Particle swarm optimization, Rainfall
Description
Rainfall is a natural factor that is very important for farmers or certain
institutions to predict the planting period of a plant. The problem is that rainfall is very difficult to predict. Trials to get optimal rainfall prediction have been carried out by BMKG through research with variety of methods in various fields, including meteorology, climatology and geophysics. The results of the study unfortunately obtained a less optimal success rate in predicting rainfall. Today, there are many new methods for predicting events. These methods include deep learning (DL) and Particle swarm optimization (PSO). The use of the deep learning method is very susceptible to initial weights that are less than optimal, so it requires a process of optimization using a metaheuristic technique, which is the PSO algorithm, because this algorithm has a level of complexity that is much lower than genetic algorithms. In this study, this method is utilized to predict rainfall by determining the exact regression equation model according to the number of layers in hidden nodes based on the size of the kernel and the weight between the layers. This research is approved achieved get more optimal rainfall prediction results that those of previous research that without optimization with PSO.
institutions to predict the planting period of a plant. The problem is that rainfall is very difficult to predict. Trials to get optimal rainfall prediction have been carried out by BMKG through research with variety of methods in various fields, including meteorology, climatology and geophysics. The results of the study unfortunately obtained a less optimal success rate in predicting rainfall. Today, there are many new methods for predicting events. These methods include deep learning (DL) and Particle swarm optimization (PSO). The use of the deep learning method is very susceptible to initial weights that are less than optimal, so it requires a process of optimization using a metaheuristic technique, which is the PSO algorithm, because this algorithm has a level of complexity that is much lower than genetic algorithms. In this study, this method is utilized to predict rainfall by determining the exact regression equation model according to the number of layers in hidden nodes based on the size of the kernel and the weight between the layers. This research is approved achieved get more optimal rainfall prediction results that those of previous research that without optimization with PSO.
Creator
Imam Cholissodin, Sutrisno
Source
DOI: 10.12928/TELKOMNIKA.v18i5.14665
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
Imam Cholissodin, Sutrisno, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
Prediction of rainfall using improved deep learning with particle swarm optimization,” Repository Horizon University Indonesia, accessed November 22, 2024, https://repository.horizon.ac.id/items/show/4074.
Prediction of rainfall using improved deep learning with particle swarm optimization,” Repository Horizon University Indonesia, accessed November 22, 2024, https://repository.horizon.ac.id/items/show/4074.