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Estimation of TRMM rainfall for landslide occurrences based on rainfall threshold analysis

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 10 No. 3 (2020)

DOI
10.11591/ijece.v10i3.pp3208-3215
ISSN
2088-8708, 2088-8708
Published
2020-06-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc/4.0

Abstract

Landslide can be triggered by intense or prolonged rainfall. Precipitation data obtained from ground-based observation is very accurate and commonly used to do analysis and landslide prediction. However, this approach is costly with its own limitation due to lack of density of ground station, especially in mountain area. As an alternative, satellite derived rainfall techniques have become more favorable to overcome these limitations. Moreover, the satellite derived rainfall estimation needs to be validated on its accuracy and its capability to predict landslide which presumably triggered by rainfall. This paper presents the investigation of using the TRMM-3B42V7 data in comparison to the available rain-gauge data in Ulu Kelang, Selangor. The monthly average rainfall, cumulative rainfall and rainfall threshold analysis from 1998 to 2011 is compared using quantitative statistical criteria (Pearson correlation, bias, root mean square error, mean different and mean). The results from analysis showed that there is a significant and strong positive correlation between the TRMM 3B42V7 and rain gauge data. The threshold derivative from the satellite products is lower than the rain gauge measurement. The findings indicated that the proposed method can be applied using TRMM satellite estimates products to derive rainfall threshold for the possible landslide occurrence.

Collaboration network of the authors

28 authors · 66 co-authorship links · 19 across different organisations

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  • Universiti Teknologi MARA
  • Universiti Teknikal Malaysia Melaka
  • National Defence University of Malaysia
  • Malaysian Nuclear Agency
  • Sultan Zainal Abidin University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Azita Laily YusofUniversiti Teknologi MARA4
Murizah Binti KassimUniversiti Teknologi MARA3
Shafinaz IsmailUniversiti Teknikal Malaysia Melaka2
Nani Fadzlina NaimUniversiti Teknologi MARA2
Suzi Seroja SarninUniversiti Teknologi MARA2
Latifah Sarah SupianNational Defence University of Malaysia2
Ahmad Azhari Mohamad NorUniversiti Teknologi MARA2
Mohd Sabri MinhatMalaysian Nuclear Agency2
Yusmardiah YusufUniversiti Teknologi MARA2
Mohd Danial RozainiUniversiti Teknologi MARA1
Azlina IdrisUniversiti Teknologi MARA1
Ezmin AbdullahUniversiti Teknologi MARA1
Ainnur Eiza AzharUniversiti Teknologi MARA1
Siti Noor Maslizan SudinUniversiti Teknologi MARA1
Farid Hazwan AzmiUniversiti Teknologi MARA1
Aliya Syahira Mohd AnuarUniversiti Teknologi MARA1
Wan Norsyafizan W. MuhamadUniversiti Teknologi MARA1
Nik Nur Shaadah Nik DzulkefliUniversiti Teknologi MARA1
Mohd Azri Abdul AzizUniversiti Teknologi MARA1
Roslan UmarSultan Zainal Abidin University1
Wan Haszerila Wan HassanUniversiti Teknikal Malaysia Melaka1
Juwita Mohd SultanUniversiti Teknikal Malaysia Melaka1
Shajahan MaidinUniversiti Teknikal Malaysia Melaka1
Mohd Kamarulnizam Abdul RahimAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Estimation of TRMM rainfall for landslide occurrences based on rainfall threshold analysis</dc:title><dc:creator>Tajudin, Noraisyah</dc:creator><dc:creator>Ya’acob, Norsuzila</dc:creator><dc:creator>Mohd Ali, Darmawaty</dc:creator><dc:creator>Adnan, Nor Aizam</dc:creator><dc:description>&lt;jats:p&gt;Landslide can be triggered by intense or prolonged rainfall. Precipitation data obtained from ground-based observation is very accurate and commonly used to do analysis and landslide prediction. However, this approach is costly with its own limitation due to lack of density of ground station, especially in mountain area. As an alternative, satellite derived rainfall techniques have become more favorable to overcome these limitations. Moreover, the satellite derived rainfall estimation needs to be validated on its accuracy and its capability to predict landslide which presumably triggered by rainfall. This paper presents the investigation of using the TRMM-3B42V7 data in comparison to the available rain-gauge data in Ulu Kelang, Selangor. The monthly average rainfall, cumulative rainfall and rainfall threshold analysis from 1998 to 2011 is compared using quantitative statistical criteria (Pearson correlation, bias, root mean square error, mean different and mean). The results from analysis showed that there is a significant and strong positive correlation between the TRMM 3B42V7 and rain gauge data. The threshold derivative from the satellite products is lower than the rain gauge measurement. The findings indicated that the proposed method can be applied using TRMM satellite estimates products to derive rainfall threshold for the possible landslide occurrence.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2020-06-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v10i3.pp3208-3215</dc:identifier><dc:identifier>10.11591/ijece.v10i3.pp3208-3215</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 10 No 3 (2020); 3208</dc:source><dc:source>2088-8708</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/21040/pdf</dc:relation><dc:rights>http://creativecommons.org/licenses/by-nc/4.0</dc:rights></oai_dc:dc>