Analyzing Windstorm Pattern in Malaysia based on Extracted Twitter Data

Wind-rain interactions often lead to severe windstorm events and consequently cause damages and fatal destructions. The increase in frequency of recent windstorm events overwhelmed the nation. Thus, efforts in obtaining and recording these events are intensified with the help of current technology....

Full description

Bibliographic Details
Published in:IOP Conference Series: Earth and Environmental Science
Main Author: Isa N.A.; Salleh S.A.; Chan A.; Zakaria N.H.; Suif Z.; Abdul Halim M.
Format: Conference paper
Language:English
Published: Institute of Physics 2022
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130281369&doi=10.1088%2f1755-1315%2f1019%2f1%2f012011&partnerID=40&md5=98b38aed357eed0f632761bd22dcd8b1
id 2-s2.0-85130281369
spelling 2-s2.0-85130281369
Isa N.A.; Salleh S.A.; Chan A.; Zakaria N.H.; Suif Z.; Abdul Halim M.
Analyzing Windstorm Pattern in Malaysia based on Extracted Twitter Data
2022
IOP Conference Series: Earth and Environmental Science
1019
1
10.1088/1755-1315/1019/1/012011
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130281369&doi=10.1088%2f1755-1315%2f1019%2f1%2f012011&partnerID=40&md5=98b38aed357eed0f632761bd22dcd8b1
Wind-rain interactions often lead to severe windstorm events and consequently cause damages and fatal destructions. The increase in frequency of recent windstorm events overwhelmed the nation. Thus, efforts in obtaining and recording these events are intensified with the help of current technology. This study aims to analyze the pattern of recent windstorm events by utilizing big data and GIS. In this study, the reported windstorm events in Twitter application were extracted using R-programming. Prior to analyses, the extracted data were screened to remove any outliers found. The extracted data were selected based on the credibility of its sources to ensure the accuracy and quality. These selected data were extracted from trusted users such as Meteorological Department of Malaysia (MMD), Berita Harian, Bernama and others. This study has demonstrated the possibility of Twitter data as an alternative data source in windstorm studies based on its reasonable findings. It is exhibited that there is drastic increased of windstorm events frequency in years 2018-2020, especially in the northern and west-coast regions of Peninsular. The highest frequency was recorded in April (inter-monsoon season) while the lowest is in February and December (northeast monsoon). The increase of frequency in several locations in the Peninsular is very alarming especially in the Klang Valley since this region is highly populated and serves as Malaysia's important economic zones. Hence, risk control should be considered in this region to reduce the negative impacts as suggested in SDG11 and SDG13. © Published under licence by IOP Publishing Ltd.
Institute of Physics
17551307
English
Conference paper
All Open Access; Gold Open Access
author Isa N.A.; Salleh S.A.; Chan A.; Zakaria N.H.; Suif Z.; Abdul Halim M.
spellingShingle Isa N.A.; Salleh S.A.; Chan A.; Zakaria N.H.; Suif Z.; Abdul Halim M.
Analyzing Windstorm Pattern in Malaysia based on Extracted Twitter Data
author_facet Isa N.A.; Salleh S.A.; Chan A.; Zakaria N.H.; Suif Z.; Abdul Halim M.
author_sort Isa N.A.; Salleh S.A.; Chan A.; Zakaria N.H.; Suif Z.; Abdul Halim M.
title Analyzing Windstorm Pattern in Malaysia based on Extracted Twitter Data
title_short Analyzing Windstorm Pattern in Malaysia based on Extracted Twitter Data
title_full Analyzing Windstorm Pattern in Malaysia based on Extracted Twitter Data
title_fullStr Analyzing Windstorm Pattern in Malaysia based on Extracted Twitter Data
title_full_unstemmed Analyzing Windstorm Pattern in Malaysia based on Extracted Twitter Data
title_sort Analyzing Windstorm Pattern in Malaysia based on Extracted Twitter Data
publishDate 2022
container_title IOP Conference Series: Earth and Environmental Science
container_volume 1019
container_issue 1
doi_str_mv 10.1088/1755-1315/1019/1/012011
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130281369&doi=10.1088%2f1755-1315%2f1019%2f1%2f012011&partnerID=40&md5=98b38aed357eed0f632761bd22dcd8b1
description Wind-rain interactions often lead to severe windstorm events and consequently cause damages and fatal destructions. The increase in frequency of recent windstorm events overwhelmed the nation. Thus, efforts in obtaining and recording these events are intensified with the help of current technology. This study aims to analyze the pattern of recent windstorm events by utilizing big data and GIS. In this study, the reported windstorm events in Twitter application were extracted using R-programming. Prior to analyses, the extracted data were screened to remove any outliers found. The extracted data were selected based on the credibility of its sources to ensure the accuracy and quality. These selected data were extracted from trusted users such as Meteorological Department of Malaysia (MMD), Berita Harian, Bernama and others. This study has demonstrated the possibility of Twitter data as an alternative data source in windstorm studies based on its reasonable findings. It is exhibited that there is drastic increased of windstorm events frequency in years 2018-2020, especially in the northern and west-coast regions of Peninsular. The highest frequency was recorded in April (inter-monsoon season) while the lowest is in February and December (northeast monsoon). The increase of frequency in several locations in the Peninsular is very alarming especially in the Klang Valley since this region is highly populated and serves as Malaysia's important economic zones. Hence, risk control should be considered in this region to reduce the negative impacts as suggested in SDG11 and SDG13. © Published under licence by IOP Publishing Ltd.
publisher Institute of Physics
issn 17551307
language English
format Conference paper
accesstype All Open Access; Gold Open Access
record_format scopus
collection Scopus
_version_ 1809677891444670464