Weather parameters forecasting as variables for rainfall prediction using adaptive neuro fuzzy inference system (ANFIS) and support vector regression (SVR)
| Main Authors: | Novitasari, D.C.R, Rohayani, H., Suwanto, , Arnita, , Rico, , Junaidi, R., Setyowati, Rr. D.N, Pramulya, R., Setiawan, F. |
|---|---|
| Format: | Proceeding PeerReviewed Book |
| Bahasa: | ind |
| Terbitan: |
, 2019
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| Subjects: | |
| Online Access: |
http://digilib.unimed.ac.id/51019/1/Turnitin.pdf http://digilib.unimed.ac.id/51019/ https://iopscience.iop.org/article/10.1088/1742-6596/1501/1/012012/pdf |
| ctrlnum |
51019 |
|---|---|
| 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"><relation>http://digilib.unimed.ac.id/51019/</relation><title>Weather parameters forecasting as variables for rainfall prediction using adaptive neuro fuzzy inference system (ANFIS) and support vector regression (SVR)</title><creator>Novitasari, D.C.R</creator><creator>Rohayani, H.</creator><creator>Suwanto, </creator><creator>Arnita, </creator><creator>Rico, </creator><creator>Junaidi, R.</creator><creator>Setyowati, Rr. D.N</creator><creator>Pramulya, R.</creator><creator>Setiawan, F.</creator><subject>QA Mathematics</subject><subject>QA273 Probabilities. Mathematical statistics</subject><subject>QA299 Analysis</subject><subject>QA76 Computer software</subject><subject>QA801 Analytic mechanics</subject><description>. The weather anomaly phenomenon that occurs can have some negative impact such
as flooding, floods will paralyze the economic activities of the community, transportation
activities, damage public infrastructure. In this research forecasting weather parameters as a
variable for predicting the amount of rainfall using the ANFIS method and Support Vector
Regression (SVR) with the aim to provide information on future weather conditions quickly and
accurately. The people can prepare themselves and prepare the equipment needed to deal with
it. Rainfall predicted based on synop data such us relative humidity, wind, and temperature. Each
parameters must forcasted by using ANFIS and the result used for predict rainfall. Accurate
prediction calculated using MSE and RMSE. Predictions of parameters that affect rainfall using
the ANFIS method shown that for wind speed predictions having RMSE of 1.975004,
temperature predictions have RMSE of 0.742332, and predictions of relative humidity have
RMSE of 3.871590. Predicted rainfall based on the data results of the nearest method preprocessing using the Support Vector Regression (SVR) method produces an MSE error value of
0.0928</description><date>2019-11-02</date><type>Journal:Proceeding</type><type>PeerReview:PeerReviewed</type><type>Book:Book</type><language>ind</language><identifier>http://digilib.unimed.ac.id/51019/1/Turnitin.pdf</identifier><identifier> Novitasari, D.C.R and Rohayani, H. and Suwanto, and Arnita, and Rico, and Junaidi, R. and Setyowati, Rr. D.N and Pramulya, R. and Setiawan, F. (2019) Weather parameters forecasting as variables for rainfall prediction using adaptive neuro fuzzy inference system (ANFIS) and support vector regression (SVR). In: International Conference on Science & Technology (ICoST 2019), 2 – 3 November 2019,, Yogyakarta, Indonesia. </identifier><relation>https://iopscience.iop.org/article/10.1088/1742-6596/1501/1/012012/pdf</relation><recordID>51019</recordID></dc>
|
| language |
ind |
| format |
Journal:Proceeding Journal PeerReview:PeerReviewed PeerReview Book:Book Book |
| author |
Novitasari, D.C.R Rohayani, H. Suwanto, Arnita, Rico, Junaidi, R. Setyowati, Rr. D.N Pramulya, R. Setiawan, F. |
| title |
Weather parameters forecasting as variables for rainfall prediction using adaptive neuro fuzzy inference system (ANFIS) and support vector regression (SVR) |
| publishDate |
2019 |
| topic |
QA Mathematics QA273 Probabilities. Mathematical statistics QA299 Analysis QA76 Computer software QA801 Analytic mechanics |
| url |
http://digilib.unimed.ac.id/51019/1/Turnitin.pdf http://digilib.unimed.ac.id/51019/ https://iopscience.iop.org/article/10.1088/1742-6596/1501/1/012012/pdf |
| contents |
. The weather anomaly phenomenon that occurs can have some negative impact such
as flooding, floods will paralyze the economic activities of the community, transportation
activities, damage public infrastructure. In this research forecasting weather parameters as a
variable for predicting the amount of rainfall using the ANFIS method and Support Vector
Regression (SVR) with the aim to provide information on future weather conditions quickly and
accurately. The people can prepare themselves and prepare the equipment needed to deal with
it. Rainfall predicted based on synop data such us relative humidity, wind, and temperature. Each
parameters must forcasted by using ANFIS and the result used for predict rainfall. Accurate
prediction calculated using MSE and RMSE. Predictions of parameters that affect rainfall using
the ANFIS method shown that for wind speed predictions having RMSE of 1.975004,
temperature predictions have RMSE of 0.742332, and predictions of relative humidity have
RMSE of 3.871590. Predicted rainfall based on the data results of the nearest method preprocessing using the Support Vector Regression (SVR) method produces an MSE error value of
0.0928 |
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Universitas Negeri Medan |
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