Analyzing Windstorm Pattern in Malaysia based on Extracted Twitter Data. Issue 1 (1st April 2022)

• Record Type: Journal Article
• Authors: Isa, N A; Salleh, S A; Chan, A; Zakaria, N H; Suif, Z; Abdul Halim, M
• Is Part Of: IOP conference series. Volume 1019:Issue 1(2022)
• Publication Date: 2022-04-01
• Subjects: Windstorm -- big data -- GIS -- R-programming -- SDG11 -- SDG13; Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/1019/1/012011
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: 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.