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.

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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%.

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SUPPORT VECTOR REGRESSION (SVR) METHOD FOR PADDY GROWTH PHASE MODELING USING SENTINEL-1 IMAGE DATA
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Keywords: Support Vector Machines;Linear Regression;Accuracy;Stability

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Section: Articles
Language : EN
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