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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Penelitian Pendidikan IPA (JPPIPA)
Danang Eko Nuryanto
Badan Meteorologi Klimatologi dan Geofisika
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Education Materials Science & Nanotechnology Physics

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Communication Satellite-Based Rainfall Estimation for Flood Mitigation in Papua Raden Yudha Mardyansyah; Budhy Kurniawan; Santoso Soekirno; Danang Eko Nuryanto
Jurnal Penelitian Pendidikan IPA Vol 10 No 12 (2024): December
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i12.8409

Abstract

Papua, an equatorial region in Indonesia, faces unique geographical and natural challenges, including heavy annual rainfall. This heavy rainfall increases flooding risks and impacts infrastructure, the economy, and daily life. Despite the importance of rain gauges for monitoring floods and climate change, Papua's difficult geography and limited transportation infrastructure hinder their installation and maintenance. In this work, we investigate a deep learning one-dimensional convolution neural network (1DCNN) model to estimate rainfall intensity using energy per symbol to noise power density ratio (Es/No) of the signals received from a communication satellite signal coupled with additional data representing satellite daily movement. The findings of this study demonstrate that the performance of the proposed model has a higher accuracy for moderate to heavy rainfall than for light rainfall. The NRMSE values for light rain, moderate rain, and heavy rain are 47.09, 31.78, and 33.58%, respectively. These results show that this method is promising for monitoring heavy rainfall as a flood mitigation effort. However, there is still room to improve the accuracy of the estimation such as using other secondary data that is highly correlated with rain at the satellite transceiver location.
Rainfall detection on a tropical island using a communication satellite downlink signal and artificial intelligence models Raden Yudha Mardyansyah; Budhy Kurniawan; Santoso Soekirno; Danang Eko Nuryanto; Ferry Budhi Susetyo
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 5: October 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i5.26012

Abstract

The development of rainfall estimates based on satellite links in several high latitude regions shows good results. However, its development for tropical regions is still very less promising, leaving the question of which method is worth using. In this work, we investigate artificial intelligence (AI) models by utilizing the Ku-band satellite downlink signal for rainfall detection on a tropical island. Long-term evaluation of all the models is performed using one year of rainfall intensity data. The findings of this study demonstrate that one-dimensional convolution neural network (1DCNN), long short-term memory (LSTM), and random forest (RF) models can effectively identify rainfall occurrences on tropical islands, with the area under the receiver operating characteristic curve (ROC) value of 0.84, 0.84, and 0.83, respectively. These three models consistently provide better accuracy in detecting the region’s diverse rainfall patterns throughout the year. The 1DCNN has better performance compared to the other models, achieving accuracy values of 0.76 and 0.82 during the periods of lowest and highest rainfall, respectively. Considering the presence of numerous potential mislabeled rain samples in the dataset, this result is excellent. In addition, the utilization of higher resolution rainfall intensity data can be a great potential for real-time rainfall monitoring in the tropical region.
Co-Authors Budhy Kurniawan Raden Yudha Mardyansyah Santoso Soekirno Susetyo, Ferry Budhi