KUALA LUMPUR'S ROAD NETWORK’S RISK TO FLASH FLOODS

Authors

  • Muhamad Faisharulfaizi Mohd Rofi Department of Civil Engineering, Faculty of Engineering, National Defence University of Malaysia, Sg. Besi Camp, 57000 Kuala Lumpur, Malaysia
  • Choy Peng Ng Department of Civil Engineering, Faculty of Engineering, National Defence University of Malaysia, Sg. Besi Camp, 57000 Kuala Lumpur, Malaysia
  • Teik Hua Law Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
  • Neza Ismail Department of Civil Engineering, Faculty of Engineering, National Defence University of Malaysia, Sg. Besi Camp, 57000 Kuala Lumpur, Malaysia

Keywords:

Flash floods, Road, Risk, Vulnerability, Urban

Abstract

Well-maintained roads are crucial to modern society, especially in urban areas. Flash floods can expose urban road networks' vulnerabilities and degrade their performance. Inadequate or poorly maintained drainage systems make roads the alternative flooding routes, causing delays and traffic disruptions. Thus, identifying susceptible road network segments disrupted by prior floods is vital for investigating and analysing the risks associated with such incidents. Vulnerability analysis contributes to risk management by providing information for better mitigation strategies and decisions. The objective of this research is to evaluate Kuala Lumpur's road network vulnerability based on flash flood frequency and ten road geometric factors. This study covered 349 flash flood incidents that occurred from 2015 to 2022. The vulnerability analysis revealed that Kuala Lumpur's central region is most vulnerable to flash floods. Jalan Gurney Kiri, Jalan Tun Razak, Jalan Raja Chulan, and Jalan Cheras have the highest vulnerability index scores due to frequent flash flood events and road conditions that exacerbate their effects. The study's results were used to create a risk map for flash floods, which may be used by local authorities to develop action plans and strategies for managing flash floods in the city.

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References

[1] Bisht, D. S., Chatterjee, C., Kalakoti, S., Upadhyay, P., Sahoo, M., & Panda, A. (2016). Modeling urban floods and drainage using SWMM and MIKE URBAN: A case study. Natural Hazards, 84, 749–776.

[2] Sandink, D. (2016). Urban flooding and ground-related homes in Canada: An overview. Flood Risk Management, 9(3), 208–223.

[3] Gholami, V., Saravi, M. M., & Ahmadi, H. (2010). Effects of impervious surfaces and urban development on runoff generation and flood hazard in the Hajighoshan watershed. Caspian Journal of Environmental Sciences, 8(1), 1–12.

[4] Sohn, W., Kim, J. H., Li, M. H., Brown, R. D., & Jaber, F. H. (2020). How does increasing impervious surfaces affect urban flooding in response to climate variability? Ecological Indicators, 118, 106774.

[5] Floodlist. (2015). Malaysia floods – Kelantan flooding worst recorded as costs rise to RM1 billion. Retrieved April 24, 2024, from http://floodlist.com/asia/malaysia-floods-kelantan-worst-recorded-costs

[6] Samsuri, N., Abu Bakar, R., & Unjah, T. (2018). Flash flood impact in Kuala Lumpur – Approach review and way forward. International Journal of the Malay World and Civilisation, 6(1), 69–76.

[7] Papilloud, T., & Keiler, M. (2021). Vulnerability patterns of road network to extreme floods based on accessibility measures. Transportation Research Part D: Transport and Environment, 100, 103045.

[8] Jamaluddin, M. (1985). Flash flood problems and human responses to the flash flood hazard in Kuala Lumpur area. Akademika, 26, 45–62.

[9] Hong, J. L., & Hong, K. (2016). Flood forecasting for Klang River at Kuala Lumpur using artificial neural networks. International Journal of Hybrid Information Technology, 9(3), 39–60.

[10] Doré, G., Drouin, P., Pierre, P., & Desrochers, P. (2005). Estimation of the relationships of road deterioration to traffic and weather in Canada (Final Report, BPR Reference: M61-04-07 (60ET), TC Reference: T8080-04-0242).

[11] Gaitan, S., van de Giesen, N. C., & ten Veldhuis, J. A. E. (2016). Can urban pluvial flooding be predicted by open spatial data and weather data? Environmental Modelling & Software, 85, 156–171.

[12] Yao, L., Wei, W., & Chen, L. (2016). How does imperviousness impact the urban rainfall runoff process under various storm cases? Ecological Indicators, 60, 893–905.

[13] Abdullah, J., & Julien, P. Y. (2014). Distributed flood simulations on a small tropical watershed with the TREX model. Journal of Flood Engineering, 5(1-2), 17-37.

[14] Coulthard, T., Frostick, L., Hardcastle, H., Jones, K., Rogers, D., & Scott, M. (2007). The June 2007 floods in Hull: Interim report by the independent review body.

[15] Suparta, W., Rahman, R., & Singh, M. S. J. (2014). Monitoring the variability of precipitable water vapor over the Klang Valley, Malaysia during flash flood. IOP Conference Series: Earth and Environmental Science, 20, 012057.

[16] Zibulewsky, J. (2001). Defining disaster: The emergency department perspective. Baylor University Medical Center Proceedings, 14(2), 144–149.

[17] UNISDR. (2009). Terminology on Disaster Risk Reduction. United Nations International Strategy for Disaster Risk Reduction (UNISDR). Retrieved from http://www.unisdr.org/files/7817_UNISDRTerminologyEnglish.pdf

[18] Chamorro, A., Serrano-Estrada, L., & Otero, J. (2022). The impact of natural disasters on road networks: A review of recent studies and future research directions. Transportation Research Part A: Policy and Practice, 162, 184–202.

[19] Nelson, A., Perez, J., & Gassman, P. (2019). Landslide and flood vulnerability assessment of road networks in mountainous regions: A case study in Nepal. Natural Hazards, 95(1), 349–366.

[20] Zhu, W., Tang, J., & Liu, X. (2020). Cyclone risk assessment for road networks: A case study of Southeast Asia. International Journal of Disaster Risk Reduction, 42, 101339.

[21] Dong, X., & Ranzi, R. (2022). Assessment of road network vulnerability to flooding in urban areas: A case study in northern Italy. Natural Hazards, 114(3), 1759–1780.

[22] Tomaszewski, B. (2014). Geographic Information Systems (GIS) for Disaster Management. CRC Press.

[23] Huang, C., & Inoue, H. (2007). Soft risk maps of natural disasters and their applications to decision-making. Information Sciences, 177(7), 1583–1592.

[24] Department of Statistics Malaysia Official Portal. (2020). Kawasanku. Retrieved from https://open.dosm.gov.my/dashboard/kawasanku/W.P.%20Kuala%20Lumpur

[25] Kuala Lumpur City Hall. (2023). Kuala Lumpur Structure Plan 2040. Retrieved from https://ppkl.dbkl.gov.my/en/pskl2040/

[26] Department of Statistics Malaysia Official Portal. (2011). Federal Territory of Kuala Lumpur. Retrieved from https://www.dosm.gov.my/v1/index.php?r=column/cone&menu_id=bjRlZXVG

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Published

23-05-2026

How to Cite

Mohd Rofi, M. F., Ng, C. P., Law, T. H., & Neza Ismail. (2026). KUALA LUMPUR’S ROAD NETWORK’S RISK TO FLASH FLOODS. Zulfaqar Journal of Defence Science, Engineering & Technology, 9(1). Retrieved from https://zulfaqarjdset.upnm.edu.my/index.php/zjdset/article/view/160

Issue

Vol. 9 No. 1 (2026)

Section

Articles

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