Rainfall Forecasting in Banyuwangi Using Adaptive Neuro Fuzzy Inference System
| Main Authors: | Alfarisy, Gusti Ahmad Fanshuri, Mahmudy, Wayan Firdaus |
|---|---|
| Format: | Article info application/pdf Journal |
| Bahasa: | eng |
| Terbitan: |
Faculty of Computer Science (FILKOM) Brawijaya University
, 2017
|
| Online Access: |
http://jitecs.ub.ac.id/index.php/jitecs/article/view/12 http://jitecs.ub.ac.id/index.php/jitecs/article/view/12/8 |
| ctrlnum |
article-12 |
|---|---|
| fullrecord |
<?xml version="1.0"?>
<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><title lang="en-US">Rainfall Forecasting in Banyuwangi Using Adaptive Neuro Fuzzy Inference System</title><creator>Alfarisy, Gusti Ahmad Fanshuri</creator><creator>Mahmudy, Wayan Firdaus</creator><description lang="en-US">Rainfall forcasting is a non-linear forecasting process that varies according to area and strongly influenced by climate change. It is a difficult process due to complexity of rainfall trend in the previous event and the popularity of Adaptive Neuro Fuzzy Inference System (ANFIS) with hybrid learning method give high prediction for rainfall as a forecasting model. Thus, in this study we investigate the efficient membership function of ANFIS for predicting rainfall in Banyuwangi, Indonesia. The number of different membership functions that use hybrid learning method is compared. The validation process shows that 3 or 4 membership function gives minimum RMSE results that use temperature, wind speed and relative humidity as parameters.</description><publisher lang="en-US">Faculty of Computer Science (FILKOM) Brawijaya University</publisher><contributor lang="en-US"/><date>2017-02-08</date><type>Journal:Article</type><type>Other:info:eu-repo/semantics/publishedVersion</type><type>Journal:Article</type><type>File:application/pdf</type><identifier>http://jitecs.ub.ac.id/index.php/jitecs/article/view/12</identifier><identifier>10.25126/jitecs.20161212</identifier><source lang="en-US">Journal of Information Technology and Computer Science; Vol 1, No 2: November 2016; 65-71</source><source>2540-9824</source><source>2540-9433</source><source>10.25126/jitecs.201612</source><language>eng</language><relation>http://jitecs.ub.ac.id/index.php/jitecs/article/view/12/8</relation><rights lang="en-US">Copyright (c) 2017 Journal of Information Technology and Computer Science</rights><recordID>article-12</recordID></dc>
|
| language |
eng |
| format |
Journal:Article Journal Other:info:eu-repo/semantics/publishedVersion Other File:application/pdf File Journal:Journal |
| author |
Alfarisy, Gusti Ahmad Fanshuri Mahmudy, Wayan Firdaus |
| title |
Rainfall Forecasting in Banyuwangi Using Adaptive Neuro Fuzzy Inference System |
| publisher |
Faculty of Computer Science (FILKOM) Brawijaya University |
| publishDate |
2017 |
| url |
http://jitecs.ub.ac.id/index.php/jitecs/article/view/12 http://jitecs.ub.ac.id/index.php/jitecs/article/view/12/8 |
| contents |
Rainfall forcasting is a non-linear forecasting process that varies according to area and strongly influenced by climate change. It is a difficult process due to complexity of rainfall trend in the previous event and the popularity of Adaptive Neuro Fuzzy Inference System (ANFIS) with hybrid learning method give high prediction for rainfall as a forecasting model. Thus, in this study we investigate the efficient membership function of ANFIS for predicting rainfall in Banyuwangi, Indonesia. The number of different membership functions that use hybrid learning method is compared. The validation process shows that 3 or 4 membership function gives minimum RMSE results that use temperature, wind speed and relative humidity as parameters. |
| id |
IOS5163.article-12 |
| institution |
Universitas Brawijaya |
| affiliation |
mill.onesearch.id |
| institution_id |
30 |
| institution_type |
library:university library |
| library |
Fakultas Ilmu Komputer |
| library_id |
1383 |
| collection |
Journal of Information Technology and Computer Science (JITeCS) |
| repository_id |
5163 |
| subject_area |
Computer Science Information System Computer Engiineering Information Technology |
| city |
KOTA MALANG |
| province |
JAWA TIMUR |
| repoId |
IOS5163 |
| first_indexed |
2018-01-25T01:42:21Z |
| last_indexed |
2019-05-24T12:09:03Z |
| recordtype |
dc |
| _version_ |
1686314747655356416 |
| score |
17.538404 |