DOI: https://doi.org/10.14710/medstat.16.1.25-36

SUPPORT VECTOR REGRESSION (SVR) METHOD FOR PADDY GROWTH PHASE MODELING USING SENTINEL-1 IMAGE DATA

*Hengki Muradi  -  Remote Sensing Research Center National Research and Innovation Agency, Indonesia
Asep Saefuddin orcid  -  Department of Statistics Bogor Agricultural Institute, Indonesia
I Made Sumertajaya orcid  -  Department of Statistics Bogor Agricultural Institute, Indonesia
Agus Mohamad Soleh orcid  -  Department of Statistics Bogor Agricultural Institute, Indonesia
Dede Dirgahayu Domiri  -  Remote Sensing Research Center National Research and Innovation Agency, Indonesia
Open Access Copyright (c) 2023 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

Citation Format:
Abstract

Support Vector Machines (SVMs) have received extensive attention over the last decade because it is claimed to be able to produce models that are accurate and have good predictions in various situations. This study aims to test the SVR (Support Vector Regression) method for modeling the growth phase of paddy using sentinel-1 image data. This method was compared for its accuracy with the LR (Linear Model) method using RMSE and R2 statistics and model stability using 10 repetitions. The accuracy of the model with the two best predictors is when the NDPI and API Polarization Index are the predictors. The paddy age model from the SVR method is better than the paddy age model from the LR method, where the SVR method produces a model with an average RMSE of 11.13 and an average coefficient of determination of 88.10%. The accuracy of the SVR model with NDPI and API predictors can be improved by adding VH polarization to the model, where the average RMSE statistic decreases to 11.0 and the average coefficient of determination becomes 88.42%. In this scenario, the best model gives a minimum RMSE value of 10.35 and a coefficient of determination of 90.05%.

Note: This article has supplementary file(s).

Fulltext View|Download |  Research Instrument
SUPPORT VECTOR REGRESSION (SVR) METHOD FOR PADDY GROWTH PHASE MODELING USING SENTINEL-1 IMAGE DATA
Subject
Type Research Instrument
  Download (991KB)    Indexing metadata
 common.other
SUPPORT VECTOR REGRESSION (SVR) METHOD FOR PADDY GROWTH PHASE MODELING USING SENTINEL-1 IMAGE DATA
Subject
Type Other
  Download (570KB)    Indexing metadata
Keywords: Support Vector Machines;Linear Regression;Accuracy;Stability

Article Metrics:

Article Info
Section: Articles
Language : EN
Recent articles
BETA-BINOMIAL MODEL IN SMALL AREA ESTIMATION USING HIERARCHICAL LIKELIHOOD APPROACH KAPLAN-MEIER AND NELSON-AALEN ESTIMATORS FOR CREDIT SCORING CORPORATE FINANCIAL DISTRESS PREDICTION USING STATISTICAL EXTREME VALUE-BASED MODELING AND MACHINE LEARNING More recent articles
Most cited articles
ANALISIS PENGARUH KARAKTERISTIK WILAYAH (KELURAHAN) TERHADAP BANYAKNYA KASUS DEMAM BERDARAH DENGUE (DBD) DI KOTA SEMARANG APLIKASI GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) PADA PEMODELAN VOLUME KENDARAAN MASUK TOL SEMARANG ANALISIS PERBANDINGAN METODE FUZZY C-MEANS DAN SUBTRACTIVE FUZZY C-MEANS ANALISIS EFISIENSI BANK PERKREDITAN RAKYAT DI KOTA SEMARANG DENGAN PENDEKATAN DATA ENVOLEPMENT ANALYSIS ANALISIS DATA INFLASI DI INDONESIA PASCA KENAIKAN TDL DAN BBM TAHUN 2013 MENGGUNAKAN MODEL REGRESI KERNEL More cited articles
  1. Ahmadi, M., & Khashei, M. (2021). Generalized support vector machines (GSVMs) model for real-world time series forecasting. Soft Computing, 25(22), 14139–14154. https://doi.org/10.1007/s00500-021-06189-z
  2. Caraka, R. E., Lee, Y., Chen, R. C., & Toharudin, T. (2020). Using hierarchical likelihood towards support vector machine: Theory and its application. IEEE Access, 8, 194795–194807. https://doi.org/10.1109/ACCESS.2020.3033796
  3. Chai, K., Nelson, A., & Darvishzadeh, R. (2018). Detecting Paddy Cropping Patterns with Sentinel-1 Multitemporal Imagery
  4. Clark, M. (2013). An Introduction to Machine Learning with Applications in R. In An Introduction to Machine Learning with Applications in R. University of Notre Dame
  5. Dirgahayu, D., & Made Parsa, I. (2019). Detection Phase Growth of Paddy Crop Using SAR Sentinel-1 Data. IOP Conference Series: Earth and Environmental Science, 280(1). https://doi.org/10.1088/1755-1315/280/1/012020
  6. Faraway, J. J. (2004). Linear Models with R. In Linear Models with R. Chapman and Hall/CRC. https://doi.org/10.4324/9780203507278
  7. Gandharum, L., Mulyani, M. E., Hartono, D. M., Karsidi, A., & Ahmad, M. (2021). Remote sensing versus the area sampling frame method in paddy paddy acreage estimation in Indramayu regency, West Java province, Indonesia. International Journal of Remote Sensing, 42(5), 1738–1767. https://doi.org/10.1080/01431161.2020.1842541
  8. Gaurav, K. A., & Patel, L. (2020). Machine Learning With R (pp. 291–331). https://doi.org/10.4018/978-1-7998-2718-4.ch015
  9. Guo, C. Y., & Chou, Y. C. (2020). A novel machine learning strategy for model selections - Stepwise Support Vector Machine (StepSVM). PLoS ONE, 15(8 August), 1–18. https://doi.org/10.1371/journal.pone.0238384
  10. Kuhn, M., Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A., Cooper, T., Mayer, Z., & Kenkel, B. (2022). Classification and Regression Tree. In Environmental and Ecological Statistics with R (pp. 237–268). Chapman and Hall/CRC. https://doi.org/10.1201/b17172-15
  11. Luts, J., Molenberghs, G., Verbeke, G., Van Huffel, S., & Suykens, J. A. K. (2012). A mixed effects least squares support vector machine model for classification of longitudinal data. Computational Statistics and Data Analysis, 56(3), 611–628. https://doi.org/10.1016/j.csda.2011.09.008
  12. Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., Leisch, F., & Chang, C.-C. (2022). Package ‘e1071.’ https://cran.r-project.org/web/packages/e1071/e1071.pdf
  13. Moqaddasi Amiri, M., Tapak, L., & Faradmal, J. (2019). A mixed-effects least square support vector regression model for three-level count data. Journal of Statistical Computation and Simulation, 89(15), 2801–2812. https://doi.org/10.1080/00949655.2019.1636991
  14. Murphy, P. K. (2012). Machine learning A Probabilistic Perspective. In MIT Press (Vol. 5, Issue 2). https://doi.org/10.1111/j.1468-0394.1988.tb00341.x
  15. Onojeghuo, A. O., Blackburn, G. A., Wang, Q., Atkinson, P. M., Kindred, D., & Miao, Y. (2018). Mapping paddy paddy fields by applying machine learning algorithms to multi-temporal Sentinel-1A and Landsat data. International Journal of Remote Sensing, 39(4), 1042–1067. https://doi.org/10.1080/01431161.2017.1395969
  16. Ostertagová, E. (2012). Modelling using Polynomial Regression. Procedia Engineering, 48, 500–506. https://doi.org/10.1016/j.proeng.2012.09.545
  17. Rahmani, A. M., Yousefpoor, E., Yousefpoor, M. S., Mehmood, Z., Haider, A., Hosseinzadeh, M., & Ali Naqvi, R. (2021). Machine learning (Ml) in medicine: Review, applications, and challenges. Mathematics, 9(22). https://doi.org/10.3390/math9222970
  18. Raza, S. M. H., Mahmood, S. A., Gillani, S. A., Hassan, S. S., Aamir, M., Saifullah, M., Basheer, M., Ahmad, A., Rehman, S., & Ali, T. (2019). Estimation of Net Paddy Production by Remote Sensing and Multi Source Datasets. Sarhad Journal of Agriculture, 35(3), 955–965. https://doi.org/10.17582/journal.sja/2019/35.3.955.965
  19. Schölkop, B. (2003). An Introduction to Support Vector Machines. In Recent Advances and Trends in Nonparametric Statistics (pp. 3–17). Elsevier. https://doi.org/10.1016/B978-044451378-6/50001-6
  20. Stroup, W. (2013). Generalized Linear Mixed Models: Modern Concepts, Methods and Applications. In International Statistical Review (Vol. 81, Issue 3). https://doi.org/10.1111/insr.12042_24
  21. Sutanto, A., Trisakti, B., & Arimurti, A. M. (2014). Perbandingan Klasifikasi Berbasis Objek dan Klasifikasi Berbasis Piksel Pada Data Citra Satelit Synthetic Aperture Radar Untuk Pemetaan Lahan. Jurnal Pengindraan Jauh, 11(1), 63–75
  22. Triscowati, D. W., Sartono, B., Kurnia, A., & ... (2019). Klasifikasi fase tanam padi menggunakan supervised random forest pada data multitemporal citra landsat-8 classification of paddy plant phase using supervised …. Seminar Nasional …, October. https://www.researchgate.net/profile/Dwi-Wahyu-Triscowati/publication/336898937_Classification_of_Paddy_Plant_Phase_Using_Supervised_Random_Forest_Based_On_Multitemporal_Data_Landsat-8_Satellite/links/5db98d044585151435d26bc0/Classification-of-Paddy-Plant-P
  23. Wali, E., Tasumi, M., & Moriyama, M. (2020). Combination of linear regression lines to understand the response of sentinel-1 dual polarization SAR data with crop phenology-case study in Miyazaki, Japan. Remote Sensing, 12(1). https://doi.org/10.3390/rs12010189
  24. Wang, H., & Xu, D. (2017). Parameter Selection Method for Support Vector Regression Based on Adaptive Fusion of the Mixed Kernel Function. Journal of Control Science and Engineering, 2017. https://doi.org/10.1155/2017/3614790
  25. Wen, T., & Edelman, A. (2000). Support Vector Machine Lagrange Multipliers and Simplex Volume Decompositions 1 Introduction 2 Separating Two Point Sets. D, 1–32
  26. Zhao, R., Li, Y., & Ma, M. (2021). Mapping Paddy Paddy with Satellite Remote Sensing: A Review. Sustainability, 13(2), 503. https://doi.org/10.3390/su13020503

Last update:

  1. Rice Phenology Classification Model Based on Sentinel-1 Using Machine Learning Method on Google Earth Engine

    Hengki Muradi, Dede Dirgahayu Domiri, I Made Parsa, I Kadek Yoga, Alhadi Bustamam, Anisa Rarasati, Sri Harini, R. Johannes Manalu, Mokhamad Subehi. Canadian Journal of Remote Sensing, 50 (1), 2024. doi: 10.1080/07038992.2024.2368036

  2. Optimizing Carbon Capture Strategies through Advanced Analytics using Machine Learning

    Naufal Ziyaadaturrahman, Garin Putra Mahardhika, Renaldy Fredyan, Muhammad Amien Ibrahim. Procedia Computer Science, 245 , 2024. doi: 10.1016/j.procs.2024.10.263

Last update: 2024-12-27 08:31:02

No citation recorded.