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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika Jurnal Varian Majalah Ilmiah Matematika dan Statistika (MIMS) Humanism : Jurnal Pengabdian Masyarakat Engineering, Mathematics and Computer Science Journal (EMACS) International Journal of Data Science, Engineering, and Analytics (IJDASEA) eProceedings of Engineering
Rifdatun Ni’mah
Institut Teknologi Telkom Surabaya
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Public Health Social Sciences Other

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Journal : Jurnal Varian

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Daily Rainfall Forecasting with ARIMA Exogenous Variables and Support Vector Regression Regita Putri Permata; Rifdatun Ni'mah; Andrea Tri Rian Dani
Jurnal Varian Vol 7 No 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i2.3202

Abstract

There is a seasonal element every year, with the dry season often lasting from May to October and the rainy season lasting from November to April. However, climate change causes the changing of the rainy and dry seasons to be erratic, so it is necessary to anticipate weather conditions. Prediction of rainfall is used to see natural conditions in the future with time series modeling. The rainfall modeling method at the six Surabaya observation posts used is the Autoregressive Integrated Moving Average with exogenous variables (ARIMAX) and Support Vector Regression. The exogenous variable used is the captured seasonal pattern of rainfall. The SVR model uses input lags from the ARIMAX model and parameter tuning uses the Kernel Radial Based Function. Selection of the best model uses the minimum RMSE value. The results showed that the average occurrence of rain at the six rainfall observation posts occurred in January, February, March, April, November and December. The ARIMAX method in this study is well used to predict rainfall in Gubeng and rainfall in Wonorejo. The SVR input lag ARIMAX method is good for predicting rainfall for Keputih, Kedung Cowek, Wonokromo and Gunung Sari. Nonparametric methods are better used to forecast rainfall data because they are able to capture data patterns with greater volatility than parametric methods, one of which is the SVR method.
Daily Rainfall Forecasting with ARIMA Exogenous Variables and Support Vector Regression Permata, Regita Putri; Ni'mah, Rifdatun; Dani, Andrea Tri Rian
Jurnal Varian Vol. 7 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i2.3202

Abstract

There is a seasonal element every year, with the dry season often lasting from May to October and the rainy season lasting from November to April. However, climate change causes the changing of the rainy and dry seasons to be erratic, so it is necessary to anticipate weather conditions. Prediction of rainfall is used to see natural conditions in the future with time series modeling. The rainfall modeling method at the six Surabaya observation posts used is the Autoregressive Integrated Moving Average with exogenous variables (ARIMAX) and Support Vector Regression. The exogenous variable used is the captured seasonal pattern of rainfall. The SVR model uses input lags from the ARIMAX model and parameter tuning uses the Kernel Radial Based Function. Selection of the best model uses the minimum RMSE value. The results showed that the average occurrence of rain at the six rainfall observation posts occurred in January, February, March, April, November and December. The ARIMAX method in this study is well used to predict rainfall in Gubeng and rainfall in Wonorejo. The SVR input lag ARIMAX method is good for predicting rainfall for Keputih, Kedung Cowek, Wonokromo and Gunung Sari. Nonparametric methods are better used to forecast rainfall data because they are able to capture data patterns with greater volatility than parametric methods, one of which is the SVR method.
Co-Authors Alifah, Amalia Nur Amri Muhaimin Andrea Tri Rian Dani Bernadus Anggo Seno AjiAji Dani, Andrea Tri Rian Faradila Efaranti , Inge Helisyah Nur Fadhilah Mohammad Hamim Zajuli Al Faroby Mufaidah, Astikhatul Putri Permata , Regita Regita Putri Permata Rizkika, Rizal Rahman Sembiring, Fikrie Hartanta Tanzilal Mustaqim Taqhsya Dwiyana , Ananda Yoga Abira , Senna