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A Data-Driven Approach to Flood Risk Assessment and Public Sentiment Analysis

Authors

Md. Shadman Zoha

Centre of Technology for Disaster Risk Reduction (CDR), Faculty of Information, Communication and Technology, University Technical Malaysia Melaka, Melaka, Malaysia (Malaysia)

Nor Aiza Moketar

Centre of Technology for Disaster Risk Reduction (CDR), Faculty of Information, Communication and Technology, University Technical Malaysia Melaka, Melaka, Malaysia (Malaysia)

Massila Kamalrudin

Centre of Technology for Disaster Risk Reduction (CDR), Faculty of Information, Communication and Technology, University Technical Malaysia Melaka, Melaka, Malaysia (Malaysia)

Suriati Akmal

Centre of Technology for Disaster Risk Reduction (CDR), Institute of Technology Management and Entrepreneurship, University Technical Malaysia Melaka, Melaka, Malaysia (Malaysia)

Noorrezam Yusop

Centre of Technology for Disaster Risk Reduction (CDR), Faculty of Information, Communication and Technology, University Technical Malaysia Melaka, Melaka, Malaysia (Malaysia)

Mohd Riduan Ahmad

Centre of Technology for Disaster Risk Reduction (CDR), Faculty of Electronics and Computer Technology and Engineering, University Technical Malaysia Melaka, Melaka, Malaysia (Malaysia)

Ariff Idris

Centre of Technology for Disaster Risk Reduction (CDR), Faculty of Electronics and Computer Technology and Engineering, University Technical Malaysia Melaka, Melaka, Malaysia (Malaysia)

Takeshi Morimoto

Faculty of Science and Engineering, Kindai University Kowakae, Higashiosaka City, Osaka, Japan (Japan)

Article Information

DOI: 10.47772/IJRISS.2025.910000779

Subject Category: Management

Volume/Issue: 9/10 | Page No: 9516-9529

Publication Timeline

Submitted: 2025-11-07

Accepted: 2025-11-14

Published: 2025-11-24

Abstract

Flooding remains as the most frequent and destructive natural disaster impacting Malaysia which causing significant disruptions across social, economic and environmental systems. This study addresses the need to integrate physical risk assessment with public sentiment analysis to strengthen disaster management. Historical flood records from 1967 to 2023 were analyzed together with flood-related news articles, utilizing geographical risk mapping and transformer-based as well as keyword-driven sentiment analysis. The results identified Kelantan and Terengganu as the highest-risk states and revealed dominant emotions of fear and frustration in media coverage, with 74.5% of articles emphasizing rescue operations and only 11.8% focusing on recovery. These findings highlight critical gaps in long-term flood resilience communication and planning. By integrating data-driven flood risk assessment with sentiment insights, the study offers a more comprehensive understanding of flood impacts, supporting more targeted disaster preparedness, communication strategies and policy development in Malaysia.

Keywords

Data-Driven, Decision-making, Flood, Risk Assessment

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References

1. Abd Majid, N., Rizal Razman, M., Zarina Syed Zakaria, S., Farid Ahmed, M., & Azwani Zulkafli, S. (2020). Flood disaster in Malaysia: approach review, causes and application of geographic information system (GIS) for Mapping of flood risk area. In Copyright@ EM International. [Google Scholar] [Crossref]

2. Aminah Shakirah, J., Sidek, L. M., Hidayah, B., Nazirul, M. Z., Jajarmizadeh, M., Ros, F. C., & Roseli, Z. A. (2016). A Review on Flood Events for Kelantan River Watershed in Malaysia for Last Decade (2001-2010). IOP Conference Series: Earth and Environmental Science, 32(1). https://doi.org/10.1088/1755-1315/32/1/012070 [Google Scholar] [Crossref]

3. Elsheikh, R. F. A., Ouerghi, S., & Elhag, A. R. (2015). Flood Risk Map Based on GIS, and Multi Criteria Techniques (Case Study Terengganu Malaysia). Journal of Geographic Information System, 07(04), 348–357. https://doi.org/10.4236/jgis.2015.74027 [Google Scholar] [Crossref]

4. Hair Zaki, U. H., Ibrahim, R., Abd Halim, S., & Yokoi, T. (2017). A review on service oriented architecture approach in flood disaster management framework for sentiment analysis: Malaysia context. Frontiers in Artificial Intelligence and Applications, 297, 362–377. https://doi.org/10.3233/978-1-61499-800-6-362 [Google Scholar] [Crossref]

5. Hatta, M. P., Pongmanda, S., Suprapti, A., Sari, K., Rijal, S., Samad, W., & Fadlin, F. (2025). The Spatial Model of Flood Risk in the Kanjiro River Basin, North Luwu District, Indonesia. Engineering, Technology and Applied Science Research, 15(3), 22848–22856. https://doi.org/10.48084/etasr.10235 [Google Scholar] [Crossref]

6. Kaklauskas, A., Rajib, S., Piaseckiene, G., Kaklauskiene, L., Sepliakovas, J., Lepkova, N., Abaravicius, Z., Milevicius, V., Kildiene, S., & Sapurov, M. (2024). Multiple criteria and statistical sentiment analysis on flooding. Scientific Reports, 14(1), 1–16. https://doi.org/10.1038/s41598-024-81562-0 [Google Scholar] [Crossref]

7. Kumar, N., & Jha, R. (2023). GIS-based Flood Risk Mapping: The Case Study of Kosi River Basin, Bihar, India. Engineering, Technology and Applied Science Research, 13(1). https://doi.org/10.48084/etasr.5377 [Google Scholar] [Crossref]

8. Romali, N. S., & Yusop, Z. (2021). Flood damage and risk assessment for urban area in Malaysia. Hydrology Research, 52(1), 142–159. https://doi.org/10.2166/NH.2020.121 [Google Scholar] [Crossref]

9. Rosmadi, H. S., Ahmed, M. F., Mokhtar, M. Bin, & Lim, C. K. (2023). Reviewing Challenges of Flood Risk Management in Malaysia. In Water (Switzerland) (Vol. 15, Issue 13). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/w15132390 [Google Scholar] [Crossref]

10. Rosmadi, H. S., Ahmed, M. F., Radwan, N., Mokhtar, M. Bin, Lim, C. K., Halder, B., Scholz, M., Alshehri, F., & Pande, C. B. (2025). Flood Management Framework for Local Government at Shah Alam, Malaysia. Water (Switzerland), 17(4). https://doi.org/10.3390/w17040513 [Google Scholar] [Crossref]

11. Saad, M. S. H., Ali, M. I., Razi, P. Z., & Ramli, N. I. (2024). Flood Risk Management in Development Projects: A Review of Malaysian Perspective within the Sendai Framework for Disaster Risk Reduction 2015-2030. CONSTRUCTION, 4(2), 103–117. https://doi.org/10.15282/construction.v4i2.10592 [Google Scholar] [Crossref]

12. Saddam, M. A., Dewantara, E. K., & Solichin, A. (2023). Sentiment Analysis of Flood Disaster Management in Jakarta on Twitter Using Support Vector Machines. Sinkron, 8(1), 470–479. https://doi.org/10.33395/sinkron.v8i1.12063 [Google Scholar] [Crossref]

13. Saharudin, M. A. I. Bin, Rosli, M. A. N. Bin, Handayani, D. O. D., Basri, A. B. B., Attarbashi, Z. S., & Suryady, Z. (2023). Flood Forecasting Using Weather Parameters. 2023 IEEE 9th International Conference on Computing, Engineering and Design, ICCED 2023. https://doi.org/10.1109/ICCED60214.2023.10425318 [Google Scholar] [Crossref]

14. Usman Kaoje, I., Abdul Rahman, M. Z., Idris, N. H., Razak, K. A., Wan Mohd Rani, W. N. M., Tam, T. H., & Mohd Salleh, M. R. (2021). Physical flood vulnerability assessment using geospatial indicator‐based approach and participatory analytical hierarchy process: A case study in Kota Bharu, Malaysia. Water (Switzerland), 13(13). https://doi.org/10.3390/w13131786 [Google Scholar] [Crossref]

15. Zaki, U. H. H., Ibrahim, R., Halim, S. A., Khaidzir, K. A. M., & Yokoi, T. (2017). Sentiflood: Process model for flood disaster sentiment analysis. 2017 IEEE Conference on Big Data and Analytics, ICBDA 2017, 2018-January, 37–42. https://doi.org/10.1109/ICBDAA.2017.8284104 [Google Scholar] [Crossref]

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