Improving Short-Term Weather Forecasting using Support Vector Machine Method in North Barito
DOI:
https://doi.org/10.31172/jmg.v25i2.1096Keywords:
Integrating Forecasting System, Model Output Statistics, Support Vector Machine, rainfall prediction, Barito UtaraAbstract
Flooding is a recurring issue in North Barito Regency due to the overflow of the Barito River. Weather forecasts in the region rely mainly on Numerical Weather Prediction (NWP) models, which often fail to capture local details due to their grid-based homogenization. To address this limitation, statistical techniques such as Model Output Statistics (MOS) can enhance NWP outputs by representing local conditions more accurately . MOS establishes statistical relationships between response variables (predictands) and predictor variables derived from NWP outputs, enabling operational applications without the need for advanced instruments. This study utilizes rainfall data from 2021-2022 from the Beringin Meteorological Station in North Barito as the response variable, while data from the Integrating Forecasting System (IFS) model serve as the predictor variables. The Support Vector Machine (SVM) method is employed to identify the relationship between predictor and response variables. By integrating the MOS technique with the SVM method, this research aims to improve the accuracy of weather forecasting, particularly for short-term predictions in North Barito. This approach demonstrates the potential to enhance localized weather predictions by addressing the limitations of conventional NWP models. The results indicate a consistent reduction in RMSE across all experiments conducted. Furthermore, the SVM model showed notable improvements in bias values and exhibited a stronger correlation compared to the original outputs from the IFS model. The percentage improvement (%IM) in rainfall forecasts, following correction using the SVM model, increased by 5.03%. The percentage improvement (%IM) in rainfall forecasts, following correction using the SVM model, increased by 5.03% for use as a predictor variable in the applied SVM method. In contrast, a combination of surface pressure, temperature across various layers, and rainfall proved to be the the most effective input variables for enhancing the accuracy of weather forecasting in North Barito using the SVM model.References
Marlina, S., “Kajian Curah Hujan untuk Pemutahiran Tipe Iklim Beberapa Wilayah di Kalimantan Tengah”, Media Ilmiah Teknik Lingkungan, Vol. 1(2), August 2016, pp. 9–17.
Hara, M., Yoshikane, T., Takahashi, H. G., Kimura, F., Noda, A., and Tokioka, T., “Assessment of the Diurnal Cycle of Precipitation Over the Maritime Continent Simulated by a 20 Km Mesh GCM using TRMM PR data”, Journal of The Meteorological Society of Japan, Vol. 87A, pp. 413–424, 2009.
Hodzic, A., and Duvel, J. P., “Impact of biomass burning aerosols on the diurnal cycle of convective clouds and precipitation over a tropical island”, Journal of Geophysical Research: Atmospheres, Vol. 123, pp. 1017–1036, 2018.
Ichikawa, H., and Yasunari, T., “Time–Space Characteristics of Diurnal Rainfall over Borneo and Surrounding Oceans as Observed by TRMM-PR”, Journal of Climate, Vol. 19(7), pp. 1238–1260, 2006.
Neale, R., and Slingo, J., “The Maritime Continent and Its Role in the Global Climate: A GCM Study”, Journal of Climate, Vol. 16(5), pp. 834–848, 2003.
Kiki, and Alam, F., “Verifikasi Parameter Presipitasi Akumulasi 24 Jam Pada Model Cuaca Numerik Tahun 2017–2020”, Megasains, Vol. 12(2), pp. 11–16, 2021.
Fuller, D. O., and Murphy, K., “The ENSO-Fire Dynamic in Insular Southeast Asia”, Climatic Change, Vol. 74(4), pp. 435–455, 2006. doi:10.1007/s10584-006-0432-5
Chang, C. P., Harr, P. A., and Chen, H. J., “Synoptic Disturbances over the Equatorial South China Sea and Western Maritime Continent during Boreal Winter”, Monthly Weather Review, Vol. 133, 2005. doi:10.1175/MWR-2868.1
Wilks, D. S., “Statistical Methods in the Atmospheric Sciences. 2nd ed”, International Geophysics Series, Vol. 91, 2006.
Idowu, O. S., and Rautenbach, C. J. D., “Model Output Statistics to improve severe storms prediction over Western Sahel”, Atmospheric Research, Vol. 93(1–3), pp. 419–425, 2009.
Glahn, H., and Lowry, D., “The Use of Model Output Statistics (MOS) in Objective Weather Forecasting”, Journal of Applied Meteorology, Vol. 11, pp. 1203–1211, 1972.
Carter, G. M., Dallavalle, J. P., and Glahn, H. R., “Statistical forecasts based on the National Meteorological Center’s numerical weather prediction system”, Weather and Forecasting, Vol. 4, pp. 401–412, 1989.
Vapnik, V., “The Nature of Statistical Learning Theory”, Springer-Verlag, New York, 1995.
Vapnik, V., “Statistical Learning Theory”, Wiley-Interscience, New York, 1998.
Yin, G., Yoshikane, T., Yamamoto, K., Kubota, T., and Yoshimura, K., “A support vector machine-based method for improving real-time hourly precipitation forecast in Japan”, Journal of Hydrology, Vol. 612, 2022.
Aksornsingchai, P., and Srinilta, C., “Statistical Downscaling for Rainfall and Temperature Prediction in Thailand”, Proceedings of the International Multi Conference of Engineers and Computer Scientists, Vol. I, Hong Kong, March 16–18, 2011.
Kalra, A., and Ahmad, S., “Estimating annual precipitation for the Colorado River Basin using oceanic-atmospheric oscillations”, Water Resources Research, Vol. 48, 2012. doi:10.1029/2011WR010667
Mellit, A., Pavan, A. M., and Benghanem, M., “Least Squares Support Vector Machine for Short Term Prediction of Meteorological Time Series”, Theoretical and Applied Climatology, Vol. 111, pp. 297–307, 2013.
Tebbi, M. A., and Haddad, B., “Artificial intelligence systems for rainy area detection and convective cells’ delineation for the south shore of Mediterranean Sea during day and nighttime using MSG satellite images”, Atmospheric Research, Vol. 178, pp. 380–392, 2016.
Laia, M. L., and Setyawan, Y., “Perbandingan Hasil Klasifikasi Curah Hujan Menggunakan Metode SVM dan NBC”, Jurnal Statistika Industri dan Komputasi, Vol. 05(2), Juli 2020, pp. 51–61.
Tjasyono, B., Juaeni, I., and Harijono, S. W. B., “Proses Meteorologis Bencana Banjir di Indonesia”, Jurnal Meteorologi dan Geofisika, Vol. 8, 2007. doi:10.31172/jmg.v8i2.12
Baba, Y., “Diurnal cycle of precipitation over the Maritime Continent simulated by a spectral cumulus parameterization”, Dynamics of Atmospheres and Oceans, Vol. 91, 101160, 2020.
Compo, G. P., Whitaker, J. S., and Sardeshmukh, P. D., “Feasibility of a 100-year reanalysis using only surface pressure data”, Bulletin of the American Meteorological Society, Vol. 87(2), pp. 175–190, 2006.
Basist, A. N., Ropelewski, C. F., and Grody, N. C., “Comparison of Tropospheric Temperature Derived from the Microwave Sounding Unit and the National Meteorological Center Analysis”, Journal of Climate, Vol. 8(4), pp. 668–681, 1995.
Huang, W. R., Koralegedara, S. B., Tung, P. H., and Chiang, T. Y., “Seasonal changes in diurnal rainfall over Sri Lanka and possible mechanisms”, Atmospheric Research, Vol. 286, 106692, 2023.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Ayu Vista Wulandari
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
