Submit Your Paper
  1. Home
  2. Journals
  3. Journal of Intelligent Systems and Internet of Things

Journal of Intelligent Systems and Internet of Things

Journal DOI

https://doi.org/10.54216/JISIoT

Submit Your Paper

2690-6791ISSN (Online) 2769-786XISSN (Print)
Submit Your Paper

Journal Menu

Homepage Journal Overview Editorial Board Submitting Articles Indexing & Abstracting

Journal Volumes

Journal of Intelligent Systems and Internet of Things

Volume 11 , Issue 1 , PP: 29-43, 2024 | Cite this article as | XML | Html | PDF | Full Length Article

Prediction of Rainfall Trends Using Forecasting Approaches Based on Singular Spectrum Analysis

Kismiantini 1 * , Shazlyn Milleana Shaharudin 2 , Ezra Putranda Setiawan 3 , Dhoriva Urwatul Wutsqa 4 , Muhamad Afdal Ahmad Basri 5 , Hairulnizam Mahdin 6 , Salama A. Mostafa 7

  • 1 Study Program of Statistics, Universitas Negeri Yogyakarta, Indonesia - (kismi@uny.ac.id)
  • 2 Department of Mathematics, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris (UPSI) - (shazlyn@fsmt.upsi.edu.my)
  • 3 Study Program of Statistics, Universitas Negeri Yogyakarta, Indonesia - (ezra.ps@uny.ac.id)
  • 4 Study Program of Statistics, Universitas Negeri Yogyakarta, Indonesia - (dhoriva_uw@uny.ac.id)
  • 5 Universiti Pendidikan Sultan Idris, Malaysia - (muhamadafdal181@gmail.com)
  • 6 Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor, Malaysia - (hairuln@uthm.edu.my)
  • 7 Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor, Malaysia - (salama@uthm.edu.my)

    • Doi: https://doi.org/10.54216/JISIoT.110104

    Received: March 12, 2023 Revised: July 27, 2023 Accepted: November 28, 2023
    Abstract

    Advanced technologies such as the Internet of Things provide an integrated platform for weather focusing, including rainfall and flood prediction. Large rainfall data frequently contain noise, which can be difficult to analyze using a standard time series model due to violated assumptions. Singular spectrum analysis (SSA) is a model-free time series analysis method that is widely used. This study aims to predict the rainfall trends in the Special Region of Yogyakarta, Indonesia, using the Recurrent SSA (SSA-R) and Vector SSA (SSA-V). The SSA-R forecasts using the recurrent continuation directly with the linear recurrent formula, while the SSA-V is a modified recurrent method. This study used 50 years of monthly rainfall data (1970-2019) from 25 stations in the special region of Yogyakarta, Indonesia. The SSA steps for forecasting rainfall data include decomposition (embedding and singular value decomposition), reconstruction (grouping and diagonal averaging), and evaluating the SSA model using w-correlation (if w-correlation is close to zero, returning to the decomposition stage; otherwise, continue the process), forecasting, evaluating the forecast results using root mean square error (RMSE), mean absolute error, r, and mean forecast error, and finally selecting the best model (either the SSA-R or SSA-V model). The results showed that the SSA-R performed better than SSA-V due to the smallest RMSE in the dry, rainy, and inter-monsoon seasons. The SSA-R model’s forecast results revealed faint, constant patterns for the dry, and rainy seasons and an increasing pattern for the inter-monsoon season. The novelty of this study is to compare the performance of the SSA-R and SSA-V models in the large rainfall data in the special region of Yogyakarta, Indonesia.

    Keywords :

    Singular Spectrum Analysis , Recurrent Singular Spectrum Analysis , Vector Singular Spectrum Analysis , Internet of Things , Rainfall Patterns , Yogyakarta

    References

    [1] S. Al-Azzawi, and A. M. Hasan, “A New 4D Hidden Hyperchaotic System with Higher Largest Lyapunov Exponent and its Synchronization,” International Journal of Mathematics, Statistics, and Computer Science, vol. 2, pp. 63-74, 2023, doi: 10.59543/ijmscs.v2i.8469.

    [2] N. Obeid, “On the Product and Ratio of Pareto and Erlang Random Variables,” International Journal of Mathematics, Statistics, and Computer Science, vol. 1, pp. 33-47, 2023, doi: 10.59543/ijmscs.v1i.7737.

    [3] M. Khaleghi, H. Zeinivand, and S. Moradipour, “Rainfall and River Discharge Trend Analysis: A Case Study of Jajrood Watershed, Iran,” International Bulletin of Water Resources and Development, vol. 2, no. 3, pp. 7-8, 2014.

    [4] A. Mondal, S. Kundu, and A. Mukhopadhyay, “Rainfall Trend Analysis by Mann-Kendall Test: A Case Study of North-eastern Part of Cuttack District, Orissa,” International Journal of Geology, Earth and Environmental Sciences, vol. 2, no. 1, pp. 70-78, 2012.

    [5] J. B. Elsner, and A. A. Tsonis, “Singular Spectrum Analysis: A New Tool in Time Series Analysis,” Springer Science+Business Media, New York, 1996.

    [6] R. Mahmoudvand, and P. C. Rodrigues, “A New Parsimonious Recurrent Forecasting Model in Singular Spectrum Analysis,” Journal of Forecasting, vol. 37, no. 2, pp. 191-200, Mar. 2018, doi: 10.1002/for.2484.

    [7] P. Unnikrishnan, and V. Jothiprakash, “Daily Rainfall Forecasting for One Year in a Single Run using Singular Spectrum Analysis,” Journal of Hydrology, vol. 561, pp. 609-621, Jun. 2018, doi: 10.1016/J. JHYDROL.2018.04.032.

    [8] M. C. R. Leles, J. P. H. Sansão, L. A. Mozelli, and H. N. Guimarães, “Improving Reconstruction of Timeseries Based in Singular Spectrum Analysis: A Segmentation Approach,” Digital Signal Processing, vol. 77, pp. 63-76, Jun. 2018, doi: 10.1016/J.DSP.2017.10.025.

    [9] N. Golyandina, and A. Zhigljavsky, “Singular Spectrum Analysis for Time Series,” Springer Verlag, Berlin, 2013. doi: 10.1007/978-3-642-34913-3.

    [10] H. Hassani, and D. Thomakos, “A Review on Singular Spectrum Analysis for Economic and Financial Time Series,” Statistics and its Interface, vol. 3, pp. 377-397, 2010.

    [11] S. M. Shaharudin, N. Ahmad, and F. Yusof, “Effect of Window Length with Singular Spectrum Analysis in Extracting the Trend Signal on Rainfall Data,” AIP Conference Proceedings, vol. 1643, no. 1, pp. 321-326,Feb. 2015, doi: 10.1063/1.4907462.

    [12] S. M. Shaharudin, N. Ahmad, and N. H. Zainuddin, “Modified singular Spectrum Analysis in Identifying Rainfall Trend Over Peninsular Malaysia,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 15, no. 1, pp. 283-293, 2019, doi: 10.11591/ijeecs.v15.i1.pp283-293.

    [13] “Special Region of Yogyakarta.” Available from: https://en.wikipedia.org/wiki/special_region_of_ yogyakarta [Last accessed on 2021 Dec 12].

    [14] E. Aldrian, and R. Dwi Susanto, “Identification of Three Dominant Rainfall Regions within Indonesia and their Relationship to Sea Surface Temperature,” International Journal of Climatology, vol. 23, no. 12, bpp. 1435-1452, Oct. 2003, doi: 10.1002/JOC.950.

    [15] H. Hassani, “Singular Spectrum Analysis: Methodology and Comparison,” Journal of Data Science, vol. 5, no. 2, pp. 239-257, 2007.

    [16] L. J. Rodríguez-Aragón, and A. Zhigljavsky, “Singular Spectrum Analysis for Image Processing,” Statistics and its Interface, vol. 3, no. 3, pp. 419-426, 2010, doi: 10.4310/SII.2010.V3.N3.A14.

    [17] N. Golyandina, V. Nekrutkin, and A. Zhigljavsky, “Analysis of Time Series Structure: SSA and Related Techniques,” CRC Press, Boca Raton, 2001.

    [18] M. Ghodsi, H. Hassani, D. Rahmani, and E. S. Silva, “Vector and Recurrent Singular Spectrum Analysis: Which is Better at Forecasting?” Journal of Applied Statistics, vol. 45, no. 10, pp. 1872-1899, Jul. 2017, doi: 10.1080/02664763.2017.1401050.

    [19] S. Milleana Shaharudin, “Predictive Modelling of Covid-19 Cases in Malaysia based on Recurrent Forecasting-singular Spectrum Analysis Approach,” International Journal of Advanced Trends in Computer Science and Engineering, vol. 9, no. 1.4, pp. 175-183, 2020, doi: 10.30534/ijatcse/2020/2691.42020.

    [20] N. Golyandina, and A. Korobeynikov, “Basic Singular Spectrum Analysis and Forecasting with R,” Computational Statistics and Data Analysis, vol. 71, pp. 934-954, Mar. 2014, doi: 10.1016/J. CSDA.2013.04.009.

     

    Cite This Article As :
    , Kismiantini. , Milleana, Shazlyn. , Putranda, Ezra. , Urwatul, Dhoriva. , Afdal, Muhamad. , Mahdin, Hairulnizam. , A., Salama. Prediction of Rainfall Trends Using Forecasting Approaches Based on Singular Spectrum Analysis. Journal of Intelligent Systems and Internet of Things, vol. , no. , 2024, pp. 29-43. DOI: https://doi.org/10.54216/JISIoT.110104
    , K. Milleana, S. Putranda, E. Urwatul, D. Afdal, M. Mahdin, H. A., S. (2024). Prediction of Rainfall Trends Using Forecasting Approaches Based on Singular Spectrum Analysis. Journal of Intelligent Systems and Internet of Things, (), 29-43. DOI: https://doi.org/10.54216/JISIoT.110104
    , Kismiantini. Milleana, Shazlyn. Putranda, Ezra. Urwatul, Dhoriva. Afdal, Muhamad. Mahdin, Hairulnizam. A., Salama. Prediction of Rainfall Trends Using Forecasting Approaches Based on Singular Spectrum Analysis. Journal of Intelligent Systems and Internet of Things , no. (2024): 29-43. DOI: https://doi.org/10.54216/JISIoT.110104
    , K. , Milleana, S. , Putranda, E. , Urwatul, D. , Afdal, M. , Mahdin, H. , A., S. (2024) . Prediction of Rainfall Trends Using Forecasting Approaches Based on Singular Spectrum Analysis. Journal of Intelligent Systems and Internet of Things , () , 29-43 . DOI: https://doi.org/10.54216/JISIoT.110104
    K. , Milleana S. , Putranda E. , Urwatul D. , Afdal M. , Mahdin H. , A. S. [2024]. Prediction of Rainfall Trends Using Forecasting Approaches Based on Singular Spectrum Analysis. Journal of Intelligent Systems and Internet of Things. (): 29-43. DOI: https://doi.org/10.54216/JISIoT.110104
    , K. Milleana, S. Putranda, E. Urwatul, D. Afdal, M. Mahdin, H. A., S. "Prediction of Rainfall Trends Using Forecasting Approaches Based on Singular Spectrum Analysis," Journal of Intelligent Systems and Internet of Things, vol. , no. , pp. 29-43, 2024. DOI: https://doi.org/10.54216/JISIoT.110104

    Article Statistics

    View
    55643
    Download
    824

    Download