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Predicting the Extent of Sidoarjo Mud Flow Using Remote Sensing

Journal of ICT Research and Applications (JICTRA) · Vol. 16 No. 1 (2022) · pp. 56–69

DOI
10.5614/itbj.ict.res.appl.2022.16.1.4
ISSN
2338-5499, 2337-5787
Published
2022-04-30
Publisher
DRI - ITB
Language
eng

Abstract

The Sidoarjo mud flow in East Java is the result of a natural phenomenon in which hot mudflow occurs due to volcanic activity. The Sidoarjo mud flow resulted in a considerable ecological disaster in the area. In this study, by using the Modification of Normalized Difference Water Index (MNDWI) technique we measured the extension of the mudflow area from 2013 to 2020 using Landsat 8 satellite data imagery. This study is meant to predict the extension of the mud flow area in the research site by comparing regression and neural network techniques in order to find the best approach. The RPROP MLP neural network technique was used to predict the Sidoarjo mud-flowing area in 2021 to 2025. Surprisingly the results of these calculations showed that the RPROP MLP neural network with three hidden layers and 20 neurons performed the best, with an R square value for training of 0.77915565 and for testing of 0.78321550.

Keywords

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Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Predicting the Extent of Sidoarjo Mud Flow Using Remote Sensing</dc:title>
	<dc:creator>Wicaksono, Wishnumurti</dc:creator>
	<dc:creator>Isa, Sani M. </dc:creator>
	<dc:subject xml:lang="en-US">MNDWI</dc:subject>
	<dc:subject xml:lang="en-US">neural network</dc:subject>
	<dc:subject xml:lang="en-US">regression</dc:subject>
	<dc:subject xml:lang="en-US">remote sensing</dc:subject>
	<dc:subject xml:lang="en-US">Sidoarjo mudflow</dc:subject>
	<dc:description xml:lang="en-US">The Sidoarjo mud flow in East Java is the result of a natural phenomenon in which hot mudflow occurs due to volcanic activity. The Sidoarjo mud flow resulted in a considerable ecological disaster in the area. In this study, by using the Modification of Normalized Difference Water Index (MNDWI) technique we measured the extension of the mudflow area from 2013 to 2020 using Landsat 8 satellite data imagery. This study is meant to predict the extension of the mud flow area in the research site by comparing regression and neural network techniques in order to find the best approach. The RPROP MLP neural network technique was used to predict the Sidoarjo mud-flowing area in 2021 to 2025. Surprisingly the results of these calculations showed that the RPROP MLP neural network with three hidden layers and 20 neurons performed the best, with an R square value for training of 0.77915565 and for testing of 0.78321550.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2022-04-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/16664</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2022.16.1.4</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 16 No. 1 (2022); 56-69</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/16664/5777</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Journal of ICT Research and Applications</dc:rights>
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