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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan
Sukmana, Ihwan
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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IMPUTATION OF MISSING DAILY RAINFALL DATA USING CONVOLUTIONAL NEURAL NETWORKS (CNN) WITH SPATIAL INTERPOLATION Sriwahyuni, Lilis; Nurdiati, Sri; Nugrahani, Endar Hasafah; Sukmana, Ihwan; Najib, Mohamad Khoirun
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2921-2936

Abstract

Accurate rainfall estimation is crucial in climate analysis and water resource planning. Observational data from weather stations play a vital role in climatological analysis as they represent actual conditions at specific locations. However, many observation stations in Indonesia need more complete data, hindering analysis and data-driven decision-making. To address this issue, this study aims to impute missing rainfall data for BMKG stations in East Java using the Convolutional Neural Network (CNN) method. Satellite data used in this study include ERA5 without interpolation and ERA5 with interpolation. The study employs a spatial interpolation approach. Data were split into training and testing datasets with various ratios: 95:5%, 90:10%, 80:20%, 70:30%, and 50:50%. The results show that the CNN method with spatially interpolated satellite data yields better results, with a Mean Absolute Error (MAE) of 7.50 on the training data and 7.05 on the testing data, indicating better generalization capability than the method without interpolation. The combination of CNN and ERA5 with interpolation was chosen for imputing missing rainfall data at BMKG stations in East Java due to its lower MAE.
Co-Authors Endar Hasafah Nugrahani Najib, Mohamad Khoirun Sri Nurdiati Sriwahyuni, Lilis