Urban expansion trends and their relationship with flood susceptibility during the period 2014–2024 in Hanoi City

Huu Duy Nguyen; Thanh Binh Doan; Si Dung Pham; Duc Dung Tran

doi:10.15625/2615-9783/24214

Author affiliations

Authors

Huu Duy Nguyen Faculty of Geography, University of Science, Vietnam National University, Hanoi, Vietnam
Thanh Binh Doan Faculty of Architecture and Planning, Hanoi University of Civil Engineering, Hanoi, Vietnam
Si Dung Pham Faculty of Architecture and Planning, Hanoi University of Civil Engineering, Hanoi, Vietnam
Duc Dung Tran 1-National Institute of Education (NIE), Earth Observatory of Singapore (EOS) and Asian School of the Environment (ASE), Nanyang Technological University of Singapore, Singapore; 2-Center of Water Management and Climate Change (WACC), Institute for Environment and Resources (IER), Vietnam National University Ho Chi Minh City (VNU-HCM), Vietnam

DOI:

https://doi.org/10.15625/2615-9783/24214

Keywords:

Urban expansion, flood susceptibility, Hanoi, machine learning

Abstract

Over the past few decades, urban expansion has accelerated worldwide. This process can increase future flood risks due to local changes in hydrological conditions and the increased exposure and vulnerability of communities in flood-prone areas. Therefore, assessing the impact of urban expansion on flood susceptibility is an important task that can support local authorities in urban planning and in mitigating flood impacts. The objective of this study was to assess the impact of urban expansion on flood susceptibility in Hanoi using machine learning models: Deep Neural Networks (DNN), Adaptive Boosting (ADB), Extreme Gradient Boosting (XGB), and Random Forest (RF). A total of 1058 flood points and 14 conditioning factors corresponding to 2014 and 2024 were used as input to the models. Statistical indices, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Area Under the Curve (AUC), and Coefficient of Determination (R²) were used to evaluate the performance of the proposed model. The results showed that the DNN model achieved the highest performance in assessing the impact of urban expansion on flood susceptibility (AUC=0.92), followed by XGB (0.91), ADB (0.86), and RF (0.82). During 2014–2024, urban expansion combined with the impacts of climate change has significantly increased the areas susceptible to flooding. In Hanoi, areas in the "high" and "very high" flood-susceptibility categories have been expanding continuously, accounting for about 25% of the total study area.

In contrast, the "medium" group has a slight decreasing trend, while the "low" and "very low" areas have narrowed. This shows that urban expansion is increasing the area prone to flooding. The results of this study provide a solid scientific basis, supporting planners and policymakers in identifying limitations in current flood risk adaptation measures and in developing more appropriate spatial and temporal strategies to minimize flood impacts.

Downloads

Download data is not yet available.

References

Ahamed A., Bolten J.D., 2017. A MODIS-based automated flood monitoring system for Southeast Asia. International Journal of Applied Earth Observation and Geoinformation, 61, 104−117. https://doi.org/10.1016/j.jag.2017.05.006.

Ahmed K.R., Akter S., 2017. Analysis of landcover change in southwest Bengal delta due to floods by NDVI, NDWI and K-means cluster with landsat multi-spectral surface reflectance satellite data. Remote Sensing Applications: Society and Environment, 8, 168−181. https://doi.org/10.1016/j.rsase.2017.08.010.

Al-Areeq A.M., Abba S.I., Yassin M.A., Benaafi M., Ghaleb M., Aljundi I.H., 2022. Computational machine learning approach for flood susceptibility assessment integrated with remote sensing and GIS techniques from Jeddah, Saudi Arabia. Remote Sensing, 14, 5515. https://doi.org/10.3390/rs14215515.

Al-Juaidi A.E., 2023. The interaction of topographic slope with various geo-environmental flood-causing factors on flood prediction and susceptibility mapping. Environmental Science and Pollution Research, 30, 59327−59348. https://doi.org/10.1007/s11356-023-26616-y.

Al-Kindi K.M., Alabri Z., 2024. Investigating the role of the key conditioning factors in flood susceptibility mapping through machine learning approaches. Earth Systems and Environment, 8, 63−81. https://doi.org/10.1007/s41748-023-00369-7.

Albano R, Adamowski J (2025) Use of digital elevation models for flood susceptibility assessment via a hydrogeomorphic approach: A case study of the Basento River in Italy. Natural Hazards, 121, 9021−9042. https://doi.org/10.1007/s11069-025-07144-z.

Alfieri L., Bisselink B., Dottori F., Naumann G., de Roo A., Salamon P., Wyser K., Feyen L., 2017. Global projections of river flood risk in a warmer world. Earth's Future, 5, 171−182. Doi: 10.1002/2016EF000485.

Anbarasan M., Muthu B., Sivaparthipan C., Sundarasekar R., Kadry S., Krishnamoorthy S., Dasel A.A., 2020. Detection of flood disaster system based on IoT, big data and convolutional deep neural network. Computer Communications, 150, 150−157. https://doi.org/10.1016/j.comcom.2019.11.022.

Asfaw W., Rientjes T., Bekele T.W., Haile A.T., 2025. Estimating elements susceptible to urban flooding using multisource data and machine learning. International Journal of Disaster Risk Reduction, 116, 105169. https://doi.org/10.1016/j.ijdrr.2024.105169.

Azi A., Saleh R.F., Ardana W.M., Kusrini K., 2024. Flood Prediction Using Support Vector Regression (Case Study of Floodgates in Jakarta). Journal of Computer Networks, Architecture and High Performance Computing, 6, 1333−1344. https://doi.org/10.47709/cnahpc.v6i3.4360.

Baig A., Atif S., Tahir A., 2024. Urban development and the loss of natural streams leads to increased flooding. Discover Cities, 1, 9. https://doi.org/10.1007/s44327-024-00010-w.

Bhunia A., Chatterjee U., Gupta J., Gupta K., 2025. Urbanisation and Quality of Life: A Comparative Assessment between Developed and Developing Countries. Sustainability and Urban Quality of Life. Routledge India, 3−21. Doi: 10.4324/9781003604358-2.

Bui Q.D., Luu C., Mai S.H., Ha H.T., Ta H.T., Pham B.T., 2023. Flood risk mapping and analysis using an integrated framework of machine learning models and analytic hierarchy process. Risk Analysis, 43, 1478−1495. https://doi.org/10.1111/risa.14018.

Chen W., Li Y., Xue W., Shahabi H., Li S., Hong H., Wang X., Bian H., Zhang S., Pradhan B., 2020. Modeling flood susceptibility using data-driven approaches of naïve bayes tree, alternating decision tree, and random forest methods. Science of The Total Environment, 701, 134979. https://doi.org/10.1016/j.scitotenv.2019.134979.

Coltin B., McMichael S., Smith T., Fong T., 2016. Automatic boosted flood mapping from satellite data. International Journal of Remote Sensing, 37, 993−1015. Doi: 10.1080/01431161.2016.1145366.

Damayanti H.N., Wikan P.A., Annurhutami F., 2024. GIS-based flood susceptibility mapping in Wawar watershed, Purworejo Regency.

IOP Conference Series: Earth and Environmental Science. IOP Publishing, 012051. Doi: 10.1088/1755-1315/1314/1/012051.

Das A., Sahoo S.N., 2025 Impact of land use and climate change on urban flooding: a case study of Bhubaneswar city in India. Natural Hazards, 121, 8655−8674. https://doi.org/10.1007/s11069-025-07130-5.

Demissie Z., Rimal P., Seyoum W.M., Dutta A., Rimmington G., 2024. Flood susceptibility mapping: Integrating machine learning and GIS for enhanced risk assessment. Applied Computing and Geosciences, 23, 100183. https://doi.org/10.1016/j.acags.2024.100183.

Estoque R.C., Murayama Y., 2017 Trends and spatial patterns of urbanization in Asia and Africa: a comparative analysis. Urban Development in Asia and Africa: Geospatial Analysis of Metropolises. Springer, 393−414. https://doi.org/10.1007/978-981-10-3241-7_19.

Freund Y., Schapire R.E., 1997. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55, 119−139. https://doi.org/10.1006/jcss.1997.1504.

Güneralp B., Güneralp İ., Liu Y., 2015. Changing global patterns of urban exposure to flood and drought hazards. Global Environmental Change, 31, 217−225. https://doi.org/10.1016/j.gloenvcha.2015.01.002.

Hawamdeh A., Tarawneh A., Sharrab Y., Al-Fraihat D., 2025. Deep neural networks hydrologic and hydraulic modeling in flood hazard analysis. Water Resources Management, 1−17. https://doi.org/10.1007/s11269-025-04243-1.

Hoang D.-V., Liou Y.-A., 2024. Assessing the influence of human activities on flash flood susceptibility in mountainous regions of Vietnam. Ecological Indicators, 158, 111417. https://doi.org/10.1016/j.ecolind.2023.111417.

Jahanbani M., Vahidnia M.H., Aghamohammadi H., Azizi Z., 2024. Flood susceptibility mapping through geoinformatics and ensemble learning methods, with an emphasis on the AdaBoost-Decision Tree algorithm, in Mazandaran, Iran. Earth Science Informatics, 17, 1433−1457. https://doi.org/10.1007/s12145-023-01213-2.

Johri S., Pratap S., Narayan J., Dwivedy S.K., 2024. Sensory data classification for gait assessment using deep neural networks: A comparative study with SGD and Adam optimizer. 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI). IEEE, 1−6. Doi: 10.1109/IATMSI60426.2024.10503416.

Khosravi K., Shahabi H., Pham B.T., Adamowski J., Shirzadi A., Pradhan B., Dou J., Ly H.-B., Gróf G., Ho H.L., 2019. A comparative assessment of flood susceptibility modeling using multi-criteria decision-making analysis and machine learning methods. Journal of Hydrology, 573, 311−323. https://doi.org/10.1016/j.jhydrol.2019.03.073.

Kundu D., Pandey A.K., 2020. World urbanisation: trends and patterns. Developing national urban policies: Ways forward to green and smart cities. Springer, 13−49. https://doi.org/10.1007/978-981-15-3738-7.

Lan N.H., Tien N.N., 2009. GIS based loss estimation modeling for flooding in the case of long wave as tsunami. Vietnam Journal of Marine Science and Technology, 9. https://doi.org/10.15625/1859-3097/9/1/6250.

Li Z., 2025. Flood Hazard Prediction Model Based on K-means Clustering Analysis and AdaBoost Regression Model. 2025 International Conference on Electrical Drives, Power Electronics & Engineering (EDPEE). IEEE, 182−186. Doi: 10.1109/EDPEE65754.2025.00036.

Linh N.T.T., Pandey M., Janizadeh S., Bhunia G.S., Norouzi A., Ali S., Pham Q.B., Anh D.T., Ahmadi K., 2022. Flood susceptibility modeling based on new hybrid intelligence model: Optimization of XGboost model using GA metaheuristic algorithm. Advances in Space Research, 69, 3301−3318. https://doi.org/10.1016/j.asr.2022.02.027.

Liu X., Sahli H., Meng Y., Huang Q., Lin L., 2017. Flood inundation mapping from optical satellite images using spatiotemporal context learning and modest AdaBoost. Remote Sensing, 9, 617. https://doi.org/10.3390/rs9060617.

Liu Y., Yang Y., Chin R.J., Wang C., Wang C., 2023. Long short-term memory (LSTM) based model for Flood forecasting in Xiangjiang River. KSCE Journal of Civil Engineering, 27, 5030−5040. https://doi.org/10.1007/s12205-023-2469-7.

Liuzzo L., Sammartano V., Freni G., 2019. Comparison between different distributed methods for flood susceptibility mapping. Water Resources Management, 33, 3155−3173. https://doi.org/10.1007/s11269-019-02293-w.

Long G., Tantanee S., Nusit K., Sooraksa P., 2026. Aggregated flood susceptibility mapping in Upper Chao Phraya River Basin using Shannon’s Entropy, Machine Learning, and Stacking ensemble methods. Geomatics, Natural Hazards and Risk, 17, 2614731. https://doi.org/10.1080/19475705.2026.2614731.

Luo P., Mu D., Xue H., Ngo-Duc T., Dang-Dinh K., Takara K., Nover D., Schladow G., 2018. Flood inundation assessment for the Hanoi Central Area, Vietnam under historical and extreme rainfall conditions. Scientific reports, 8, 12623. https://doi.org/10.1038/s41598-018-30024-5.

Luu C., Nguyen D.D., Amiri M., Van P.T., Bui Q.D., Prakash I., Pham B.T., 2022. Flood susceptibility modeling using Radial Basis Function Classifier and Fisher’s linear discriminant function. Vietnam Journal of Earth Sciences, 44(1), 55−72. https://doi.org/10.1038/s41598-018-30024-5.

Ma M., Zhao G., He B., Li Q., Dong H., Wang S., Wang Z., 2021. XGBoost-based method for flash flood risk assessment. Journal of Hydrology, 598, 126382. https://doi.org/10.1016/j.jhydrol.2021.126382.

Madhuri R., Sistla S., Srinivasa Raju K., 2021. Application of machine learning algorithms for flood susceptibility assessment and risk management. Journal of Water and Climate Change, 12, 2608−2623. https://doi.org/10.2166/wcc.2021.051.

Mosavi A., Ozturk P., Chau K.-W., 2018. Flood prediction using machine learning models: Literature review. Water, 10, 1536. https://doi.org/10.3390/w10111536.

Nguyen H.D., Nguyen Q.-H., Dang D.K., Van C.P., Truong Q.H., Pham S.D., Bui Q.-T., Petrisor A.-I., 2024a. A novel flood risk management approach based on future climate and land use change scenarios. Science of The Total Environment, 921, 171204. https://doi.org/10.1016/j.scitotenv.2024.171204.

Nguyen N.T., Nhon N.T.T., Hai H.T.N., Chi N.D.T., Hien T.T., 2022. Characteristics of microplastics and their affiliated PAHs in surface water in Ho Chi Minh City, Vietnam. Polymers, 14, 2450. https://doi.org/10.3390/polym14122450.

Onchi-Ramos K., Rodríguez-Cuevas C., Couder-Castañeda C., Padilla-Pérez D.-A., 2024. Flood risk assessment of the Garita River in the urban zone of San Luis Potosí City, by hydrodynamic modeling. Scientific Reports, 14, 15891. https://doi.org/10.1038/s41598-024-66743-1.

Panahi M., Jaafari A., Shirzadi A., Shahabi H., Rahmati O., Omidvar E., Lee S., Bui D.T., 2021. Deep learning neural networks for spatially explicit prediction of flash flood probability. Geoscience Frontiers, 12, 101076. https://doi.org/10.1016/j.gsf.2020.09.007.

Petrişor A.-I., Hamma W., Nguyen H.D., Randazzo G., Muzirafuti A., Stan M.-I., Tran V.T., Aştefănoaiei R., Bui Q.-T., Vintilă D.-F., 2020. Degradation of coastlines under the pressure of urbanization and tourism: Evidence on the change of land systems from Europe, Asia and Africa. Land, 9, 275. https://doi.org/10.3390/land9080275.

Pham V.C., Pham T.-T.-H., Tong T.H.A., Nguyen T.T.H., Pham N.H., 2015. The conversion of agricultural land in the peri-urban areas of Hanoi (Vietnam): patterns in space and time. Journal of Land Use Science, 10, 224−242. https://doi.org/10.1080/1747423X.2014.884643.

Rahmati O., Darabi H., Panahi M., Kalantari Z., Naghibi S.A., Ferreira C.S.S., Kornejady A., Karimidastenaei Z., Mohammadi F., Stefanidis S., 2020. Development of novel hybridized models for urban flood susceptibility mapping. Scientific reports, 10, 12937. https://doi.org/10.1038/s41598-020-69703-7.

Ren H., Pang B., Bai P., Zhao G., Liu S., Liu Y., Li M., 2024. Flood susceptibility assessment with random sampling strategy in ensemble learning (RF and XGBoost). Remote Sensing, 16, 320. https://doi.org/10.3390/rs16020320.

Shao Y., Chen J., Zhang T., Yu T., Chu S., 2024. Advancing rapid urban flood prediction: a spatiotemporal deep learning approach with uneven rainfall and attention mechanism. Journal of Hydroinformatics, 26, 1409−1424. https://doi.org/10.2166/hydro.2024.024.

Smith D.W., Scarpaci J.L., 2000b. Urbanization in transitional societies: An overview of Vietnam and Hanoi. Urban Geography, 21, 745−757. Doi: 10.2747/0272-3638.21.8.745.

Taubenböck H., Weigand M., Esch T., Staab J., Wurm M., Mast J., Dech S., 2019. A new ranking of the world's largest cities - Do administrative units obscure morphological realities? Remote Sensing of Environment, 232, 111353. https://doi.org/10.1016/j.rse.2019.111353.

Tien N.N., Thanh N.T., Dang V.H., Vinh V.D., 2016. The initial results of studying the suspended sediment dynamics during the flood season in the Hau River mouth area. Vietnam Journal of Marine Science and Technology, 16, 122−128. https://doi.org/10.15625/1859-3097/16/2/6480.

Van Pham T., Bui D.X., Do T.A.T., Do A.N.T., 2025. Assessing flood susceptibility in Hanoi using machine learning and remote sensing: implications for urban health and resilience. Natural Hazards, 1−22. https://doi.org/10.1007/s11069-025-07211-5.

Versini P.-A., Gaume E., Andrieu H., 2010. Assessment of the susceptibility of roads to flooding based on geographical information-test in a flash flood prone area (the Gard region, France). Natural Hazards and Earth System Sciences, 10, 793−803. https://doi.org/10.5194/nhess-10-793-2010.

Viet H.P., An B.N., Viet H.P., Ngoc A.N., Phuong T.G.T., Cao H.N., Tien B.D., 2025. Enhancing spatial prediction of flash floods with Balancing Composite Motion Optimized Random Forest: A case study in High-Frequency Torrential rainfall area. Vietnam Journal of Earth Sciences, 47(1), 118−135. https://doi.org/10.15625/2615-9783/22189.

Wang Z., Lai C., Chen X., Yang B., Zhao S., Bai X., 2015. Flood hazard risk assessment model based on random forest. Journal of Hydrology, 527, 1130−1141. https://doi.org/10.1016/j.jhydrol.2015.06.008.

Wu Y., She D., Xia J., Song J., Xiao T., Zhou Y., 2023. The quantitative assessment of impact of pumping capacity and LID on urban flood susceptibility based on machine learning. Journal of Hydrology, 617, 129116. https://doi.org/10.1016/j.jhydrol.2023.129116.

Yuwono B.D., Sabri L., Wijaya A., Awaluddin M., 2024. Assessment of Flood Risk Induced by Land Subsidence Using Machine Learning. Indonesian Journal of Geography, 56. https://doi.org/10.22146/ijg.94726.