An assessment framework for evaluating urban flood vulnerabilities using Night-Time Light data

Nuwani Kangana; Nayomi Kankanamge; Nuwani Kangana; Nayomi Kankanamge

doi:10.3934/urs.2025003

Urban Resilience and Sustainability

2025, Volume 3, Issue 1: 57-85. doi: 10.3934/urs.2025003

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Research article

An assessment framework for evaluating urban flood vulnerabilities using Night-Time Light data

Nuwani Kangana ¹,
Nayomi Kankanamge ^{2
,
,}

1.
Department of Town and Country Planning, University of Moratuwa, Sri Lanka
2.
Urban Design and Town Planning, University of the Sunshine Coast, Moreton Bay, QLD 4502, Australia

Received: 28 March 2025 Revised: 22 May 2025 Accepted: 04 June 2025 Published: 11 June 2025

Urban floods pose significant socio-economic and environmental challenges, particularly in rapidly urbanizing regions. We utilized Night-Time Light (NTL) data as a dynamic proxy for human activity and urban density to enhance the assessment of urban flood vulnerability. Unlike traditional methods that rely on static datasets or post-disaster surveys, this approach incorporates real-time NTL data to better capture the evolving patterns of urban exposure. Focusing on the Kelani River watershed, the most flood-prone and densely populated region in Sri Lanka, we integrated nine conditioning factors, including slope, precipitation, and soil type, with NTL intensity to generate comprehensive vulnerability maps. To ensure the accessibility and practical application of the results, a web application was developed using Google Earth Engine (GEE), offering an interactive platform for real-time visualization of urban flood risks. The findings underlined the transformative potential of NTL data in flood vulnerability mapping and demonstrated its applicability as a cost-effective, scalable, and open-access decision-support tool adaptable to various urban contexts.
- Night-Time Light data,
- urban flooding,
- urban resilience,
- vulnerability assessment,
- web application
Citation: Nuwani Kangana, Nayomi Kankanamge. An assessment framework for evaluating urban flood vulnerabilities using Night-Time Light data[J]. Urban Resilience and Sustainability, 2025, 3(1): 57-85. doi: 10.3934/urs.2025003

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Abstract

Urban floods pose significant socio-economic and environmental challenges, particularly in rapidly urbanizing regions. We utilized Night-Time Light (NTL) data as a dynamic proxy for human activity and urban density to enhance the assessment of urban flood vulnerability. Unlike traditional methods that rely on static datasets or post-disaster surveys, this approach incorporates real-time NTL data to better capture the evolving patterns of urban exposure. Focusing on the Kelani River watershed, the most flood-prone and densely populated region in Sri Lanka, we integrated nine conditioning factors, including slope, precipitation, and soil type, with NTL intensity to generate comprehensive vulnerability maps. To ensure the accessibility and practical application of the results, a web application was developed using Google Earth Engine (GEE), offering an interactive platform for real-time visualization of urban flood risks. The findings underlined the transformative potential of NTL data in flood vulnerability mapping and demonstrated its applicability as a cost-effective, scalable, and open-access decision-support tool adaptable to various urban contexts.

References

[1]	Yao L, Chen L, Wei W (2017) Exploring the linkage between urban flood risk and spatial patterns in small urbanized catchments of Beijing, China. Int J Environ Res Public Health 14: 239. https://doi.org/10.3390/ijerph14030239 doi: 10.3390/ijerph14030239
[2]	CRED, UNDRR (2020) Human cost of disasters: An overview of the last 20 years (2000–2019). Available from: https://www.undrr.org/publication/human-cost-disasters-overview-last-20-years-2000-2019.
[3]	Kankanamge N, Yigitcanlar T, Goonetilleke A, et al. (2020) Determining disaster severity through social media analysis: Testing the methodology with South East Queensland Flood tweets. Int J Disaster Risk Reduct 42: 101360. https://doi.org/10.1016/j.ijdrr.2019.101360 doi: 10.1016/j.ijdrr.2019.101360
[4]	Alahacoon N, Matheswaran K, Pani P, et al. (2018) A decadal historical satellite data and rainfall trend analysis (2001–2016) for flood hazard mapping in Sri Lanka. Remote Sens 10: 448. https://doi.org/10.3390/rs10030448 doi: 10.3390/rs10030448
[5]	Kankanamge N, Yigitcanlar T, Goonetilleke A, et al. (2019) Can volunteer crowdsourcing reduce disaster risk? A systematic review of literature. Int J Disaster Risk Reduct 35: 101197. https://doi.org/10.1016/j.ijdrr.2019.101097 doi: 10.1016/j.ijdrr.2019.101097
[6]	Agonafir C, Lakhankar T, Khanbilvardi R, et al. (2023) A review of recent advances in urban flood research. Water Secur 19: 100141. https://doi.org/10.1016/j.wasec.2023.100141 doi: 10.1016/j.wasec.2023.100141
[7]	Tate E, Rahman MA, Emrich C T, et al. (2021) Flood exposure and social vulnerability in the United States. Nat Hazards 106: 435–457. https://doi.org/10.1007/s11069-020-04470-2 doi: 10.1007/s11069-020-04470-2
[8]	Smith A, Bates PD, Wing O, et al. (2019) New estimates of flood exposure in developing countries using high-resolution population data. Nat Commun 10: 1814. https://doi.org/10.1038/s41467-019-09282-y doi: 10.1038/s41467-019-09282-y
[9]	Weerasinghe KM, Gehrels H, Arambepola N, et al. (2018) Qualitative Flood Risk assessment for the Western Province of Sri Lanka. Procedia Eng 212: 503–510. https://doi.org/10.1016/j.proeng.2018.01.065 doi: 10.1016/j.proeng.2018.01.065
[10]	Skoufias E, Strobl E, Tveit T (2021) Can we rely on VIIRS nightlights to estimate the short-term impacts of natural hazards? Evidence from five South East Asian countries. Geomat Nat Haz Risk 12: 381–404. https://doi.org/10.1080/19475705.2021.1879943 doi: 10.1080/19475705.2021.1879943
[11]	Chen Y, Zhou H, Zhang H, et al. (2015) Urban flood risk warning under rapid urbanization. Environ Res 139: 3–10. https://doi.org/10.1016/j.envres.2015.02.028 doi: 10.1016/j.envres.2015.02.028
[12]	Hsu FC, Baugh KE, Ghosh T, et al. (2015) DMSP-OLS radiance calibrated nighttime lights time series with intercalibration. Remote Sens 7: 1855–1876. https://doi.org/10.3390/rs70201855 doi: 10.3390/rs70201855
[13]	Lin CY, Ackerman A, Johnston D, et al. (2022) LiDAR operation and digital modeling visualization to communicate stormwater management at green spaces in developing regions. Eurographics Association. https://doi.org/10.2312/envirvis.20221056 doi: 10.2312/envirvis.20221056
[14]	Rahman MR, Thakur PK (2018) Detecting, mapping and analysing of flood water propagation using synthetic aperture radar (SAR) satellite data and GIS: A case study from the Kendrapara District of Orissa State of India. Egypt J Remote Sens Space Sci 21: S37–S41. https://doi.org/10.1016/j.ejrs.2017.10.002 doi: 10.1016/j.ejrs.2017.10.002
[15]	Cao R, Li F, Feng P (2020) Exploring the hydrologic response to the urban building coverage ratio by model simulation. Theor Appl Climatol 140: 1005–1015. https://doi.org/10.1007/s00704-020-03139-x doi: 10.1007/s00704-020-03139-x
[16]	Kankanamge N, Yigitcanlar T, Goonetilleke A (2021) Public perceptions on artificial intelligence driven disaster management: Evidence from Sydney, Melbourne and Brisbane. Telemat Inform 65: 101729. https://doi.org/10.1016/j.tele.2021.101729 doi: 10.1016/j.tele.2021.101729
[17]	Marolla C (2024) Urban flood resilience: Risk and business continuity management systems strategic approach. J Clin Epidemiol Public Health 2: 1–13. https://doi.org/10.33774/coe-2024-rjg0b doi: 10.33774/coe-2024-rjg0b
[18]	Paudel S, Benavides JC (2023) Flood Ecology, Oxford: Oxford University Press. https://www.sci-hub.ru/10.1093/obo/9780199830060-0244
[19]	Kumar D, Bhattacharjya RK (2020) Review of different methods and techniques used for flood vulnerability analysis. Nat Hazards Earth Syst Sci Discuss 1: 1–15, https://doi.org/10.5194/nhess-2020-297, 2020
[20]	Sy HM, Luu C, Bui QD, et al. (2023) Urban flood risk assessment using Sentinel-1 on the Google Earth Engine: A case study in Thai Nguyen city, Vietnam. Remote Sens Appl: Soc Environ 31: 1100987. https://doi.org/10.1016/j.rsase.2023.100987 doi: 10.1016/j.rsase.2023.100987
[21]	Shinde S, Pande CB, Barai VN, et al. (2023) Flood impact and damage assessment based on the Sentinel-1 SAR data using Google Earth Engine, In: Climate Change Impacts on Natural Resources, Ecosystems and Agricultural Systems, 483–502. https://doi.org/10.1007/978-3-031-19059-9_20
[22]	Gemitzi A, Kopsidas O, Stefani F, et al. (2024) A constantly updated flood hazard assessment tool using satellite-based high-resolution land cover dataset within Google Earth Engine. Land 13: 1919. https://doi.org/10.3390/land13111929 doi: 10.3390/land13111929
[23]	Prasertsoong N, Puttanapong N (2025) An integrated framework for satellite-based flood mapping and socioeconomic risk analysis: A case of Thailand. Prog Disaster Sci 25: 100393. https://doi.org/10.1016/j.pdisas.2024.100393 doi: 10.1016/j.pdisas.2024.100393
[24]	Asian Disaster Reduction Center (2021) Natural Disaster Databook 2021: An Analytical Overview. Available from: https://www.preventionweb.net/publication/natural-disasters-data-book-2021-analytical-overview.
[25]	Bagheri A, Liu GJ (2025) Climate change and urban flooding: assessing remote sensing data and flood modeling techniques: A comprehensive review. Environ Rev 33: 1–14. https://doi.org/10.1139/er-2024-0065 doi: 10.1139/er-2024-0065
[26]	Vojtek M, Vojteková J (2019) Flood susceptibility mapping on a national scale in Slovakia using the analytical hierarchy process. Water 11: 364. https://doi.org/10.3390/w11020364 doi: 10.3390/w11020364
[27]	Samanta S, Pal DK, Palsamanta B (2018) Flood susceptibility analysis through remote sensing, GIS and frequency ratio model. Appl Water Sci 8: 66. https://doi.org/10.1007/s13201-018-0710-1 doi: 10.1007/s13201-018-0710-1
[28]	Pathak S, Panta HK, Bhandari T, et al. (2020) Flood vulnerability and its influencing factors. Natl Hazards 104: 2175–2196. https://doi.org/10.1007/s11069-020-04267-3 doi: 10.1007/s11069-020-04267-3
[29]	Kates RW (1971) Natural hazard in human ecological perspective: Hypotheses and models. Econ Geogr 47: 438–451. https://doi.org/10.2307/142820 doi: 10.2307/142820
[30]	Birkmann J, Schüttrumpf H, Handmer J, et al. (2023) Strengthening resilience in reconstruction after extreme events—Insights from flood affected communities in Germany. Int J Disaster Risk Reduct 96: 103965. https://doi.org/10.1016/j.ijdrr.2023.103965 doi: 10.1016/j.ijdrr.2023.103965
[31]	Balica SF, Wright NG, van der Meulen F (2012) A flood vulnerability index for coastal cities and its use in assessing climate change impacts. Nat Hazard 64: 73–105. https://doi.org/10.1007/s11069-012-0234-1 doi: 10.1007/s11069-012-0234-1
[32]	Blistanova M, Zelenakova M, Blistan P, et al. (2016) Assessment of flood vulnerability in Bodva river basin, Slovakia. Acta Montan Slovaca 21: 19–28. https://doi.org/10.3390/ams21010019 doi: 10.3390/ams21010019
[33]	Skougaard Kaspersen P, Høegh Ravn N, Arnbjerg-Nielsen K, et al. (2017) Comparison of the impacts of urban development and climate change on exposing European cities to pluvial flooding. Hydrol Earth Syst Sci 21: 4131–4147. https://doi.org/10.5194/hess-21-4131-2017 doi: 10.5194/hess-21-4131-2017
[34]	Seybatou DIEYE, Mapathé NDIAYE, Diogoye DIOUF, et al. (2024) Flood risk modelling using the hierarchical process analysis (HPA) method: Example of the city of Thies, Senegal. World J Adv Res Rev 24: 2440–2657. https://doi.org/10.30574/wjarr.2024.24.3.3989 doi: 10.30574/wjarr.2024.24.3.3989
[35]	Kangana N, Kankanamge N, De Silva C, et al. (2025) Harnessing mobile technology for flood disaster readiness and response: A comprehensive review of mobile applications on the Google Play Store. Urban Sci 9: 106. https://doi.org/10.3390/urbansci9040106 doi: 10.3390/urbansci9040106
[36]	Chapi K, Singh VP, Shirzadi A, et al. (2017) A novel hybrid artificial intelligence approach for flood susceptibility assessment. Environ Model Softw 95: 229–245. https://doi.org/10.1016/j.envsoft.2017.06.012 doi: 10.1016/j.envsoft.2017.06.012
[37]	Band SS, Janizadeh S, Pal SC, et al. (2020) Flash flood susceptibility modeling using new approaches of hybrid and ensemble tree-based machine learning algorithms. Remote Sens 12: 3568. https://doi.org/10.3390/rs12213568 doi: 10.3390/rs12213568
[38]	Khosravi K, Shahabi H, Pham BT, et al. (2019) A comparative assessment of flood susceptibility modeling using multi-criteria decision-making analysis and machine learning methods. J Hydrol 573: 311–323. https://doi.org/10.1016/j.jhydrol.2019.03.073 doi: 10.1016/j.jhydrol.2019.03.073
[39]	Shafizadeh-Moghadam H, Valavi R, Shahabi H, et al. (2018) Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping. J Environ Manage 217: 1–11. https://doi.org/10.1016/j.jenvman.2018.03.089 doi: 10.1016/j.jenvman.2018.03.089
[40]	Darabi H, Choubin B, Rahmati O, et al. (2019) Urban flood risk mapping using the GARP and QUEST models: A comparative study of machine learning techniques. J Hydrol 569: 142–154. https://doi.org/10.1016/j.jhydrol.2018.12.002 doi: 10.1016/j.jhydrol.2018.12.002
[41]	Kangana N, Kankanamge N, De Silva C, et al. (2024) Bridging community engagement and technological innovation for creating smart and resilient cities: A systematic literature review. Smart Cities 7: 3823–3852. https://doi.org/10.3390/smartcities7060147 doi: 10.3390/smartcities7060147
[42]	Dodangeh E, Choubin B, Eigdir AN, et al. (2020) Integrated machine learning methods with resampling algorithms for flood susceptibility prediction. Sci Total Environ 705: 135983. https://doi.org/10.1016/j.scitotenv.2019.135983 doi: 10.1016/j.scitotenv.2019.135983
[43]	Swain KC, Singha C, Nayak L, (2020) Flood susceptibility mapping through the GIS-AHP technique using the cloud. ISPRS Int J Geoinf 9: 720. https://doi.org/10.3390/ijgi9120720 doi: 10.3390/ijgi9120720
[44]	Mahmoud SH, Gan TY (2018) Multi-criteria approach to develop flood susceptibility maps in arid regions of Middle East. J Clean Prod 196: 216–229. https://doi.org/10.1016/j.jclepro.2018.06.047 doi: 10.1016/j.jclepro.2018.06.047
[45]	Das S (2019) Geospatial mapping of flood susceptibility and hydro-geomorphic response to the floods in Ulhas basin, India. Remote Sens Appl Soc Environ 14: 60–74. https://doi.org/10.1016/j.rsase.2019.02.006 doi: 10.1016/j.rsase.2019.02.006
[46]	Park K, Lee MH (2019) The development and application of the urban flood risk assessment model for reflecting upon urban planning elements. Water 8: 920. https://doi.org/10.3390/w11050920 doi: 10.3390/w11050920
[47]	Piyumi MMM, Abenayake C, Jayasinghe A, et al. (2021) Urban flood modeling application: Assess the effectiveness of building regulation in coping with urban flooding under precipitation uncertainty. Sustain Cities Soc 75: 103294. https://doi.org/10.1016/j.scs.2021.103294 doi: 10.1016/j.scs.2021.103294
[48]	de Silva MMGT, Weerakoon SB, Herath S (2014) Modeling of event and continuous flow hydrographs with HEC–HMS: case study in the Kelani River Basin, Sri Lanka. J Hydrol Eng 19: 800–806. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000846 doi: 10.1061/(ASCE)HE.1943-5584.0000846
[49]	Wickramaarachchi TN, Ishidaira H, Wijayaratna TMN (2012) An application of distributed hydrological model, YHyM/BTOPMC to gin ganga watershed, Sri Lanka. Eng J Inst Eng Sri Lanka 45: 31–40. https://doi.org/10.4038/engineer.v45i2.7018 doi: 10.4038/engineer.v45i2.7018
[50]	Dushyantha C, Ptuhina I (2020) Flood risk assessment of river "Kelani Ganga" exceeding its threshold water level. E3S Web Conf 157: 02006. https://doi.org/10.1051/e3sconf/202015702006 doi: 10.1051/e3sconf/202015702006
[51]	Balica SF, Douben N, Wright NG (2009) Flood vulnerability indices at varying spatial scales. Water Sci Technol 60: 2571–2580. https://doi.org/10.2166/wst.2009.183 doi: 10.2166/wst.2009.183
[52]	Khosravi K, Pham BT, Chapi K, et al. (2018) A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Sci Total Environ 627: 744–755. https://doi.org/10.1016/j.scitotenv.2018.01.266 doi: 10.1016/j.scitotenv.2018.01.266
[53]	Tehrany MS, Pradhan B, Jebur MN (2014) Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. J Hydrol 512: 332–343. https://doi.org/10.1016/j.jhydrol.2014.03.008 doi: 10.1016/j.jhydrol.2014.03.008
[54]	Meybeck M, Green P, Vörösmarty C (2001) A new typology for mountains and other relief classes. Mt Res Dev 21: 34–45. https://doi.org/10.1659/0276-4741(2001)021[0034:ANTFMA]2.0.CO;2 doi: 10.1659/0276-4741(2001)021[0034:ANTFMA]2.0.CO;2
[55]	Rahmati O, Pourghasemi HR, Zeinivand H (2016) Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran. Geocarto Int 31: 42–70. https://doi.org/10.1080/10106049.2015.1041559 doi: 10.1080/10106049.2015.1041559
[56]	Mitra R, Das J (2022) A comparative assessment of flood susceptibility modelling of GIS-based TOPSIS, VIKOR, and EDAS techniques in the Sub-Himalayan foothills region of Eastern India. Environ Sci Pollut Res 29: 28999–29013. https://doi.org/10.1007/s11356-022-20839-2 doi: 10.1007/s11356-022-20839-2
[57]	Moreira LL, de Brito MM, Kobiyama M (2021) A systematic review and future prospects of flood vulnerability indices. Nat Hazards Earth Syst Sci 21: 1–23. https://doi.org/10.5194/nhess-21-1513-2021 doi: 10.5194/nhess-21-1513-2021
[58]	Liu X, Hu G, Ai B, et al. (2015) A Normalized Urban Areas Composite Index (NUACI) based on combination of DMSP-OLS and MODIS for mapping impervious surface area. Remote Sens 7: 17168–17189. https://doi.org/10.3390/rs71215863 doi: 10.3390/rs71215863
[59]	Tong L, Hu S, Frazier AE (2018) Mixed accuracy of nighttime lights (NTL)-based urban land identification using thresholds: Evidence from a hierarchical analysis in Wuhan Metropolis, China. Appl Geogr 98: 201–214. https://doi.org/10.1016/j.apgeog.2018.07.017 doi: 10.1016/j.apgeog.2018.07.017
[60]	Ranaweera DKDA, Ratnayake RMK (2016) Urban Landuse Changes in Sri Lanka with Special Reference to Kaduwela Town from 1975 to 2016. Int J Innov Res Dev 6: 1–9. https://doi.org/10.24940/ijird/2017/v6/i6/JUN17014 doi: 10.24940/ijird/2017/v6/i6/JUN17014

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