Flood susceptibility assessment using deep neural networks and open-source spatial datasets in transboundary river basin
Author affiliations
DOI:
https://doi.org/10.15625/2615-9783/22711Keywords:
Flood susceptibility, Mekong basin, deep learning, machine learning, AUC validation, climate change, hydrological modelingAbstract
The Mekong Basin is the most critical transboundary river basin in Asia. This basin provides an abundant source of fresh water essential for the development of agriculture, domestic consumption, and industry, as well as for the production of hydroelectricity, and it also contributes to ensuring food security worldwide. This region is often subject to floods that cause significant damage to human life, society, and the economy. However, flood risk management challenges in this region are increasingly substantial due to conflicting objectives between several countries and data sharing. This study integrates deep learning with optimization algorithms, namely Grasshopper Optimisation Algorithm (GOA), Adam and Stochastic Gradient Descent (SGD), and open-source datasets to identify the region of probably occurring floods in the Mekong basin, covering Vietnam and Cambodia. Various statistical indices, namely Area Under the Curve (AUC), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²), were used to evaluate flood susceptibility models. The results show that the proposed models performed well with AUC values above 0.8, specifying that the DNN-Adam model achieved an AUC of 0.98, outperforming DNN-GOA (AUC = 0.89), DNN-SGD (AUC = 0.87), and XGB (AUC = 0.82. Regions with very high flood susceptibility are concentrated in the Mekong Delta of Vietnam and along the Mekong River in Cambodia. The findings of this study are significant in supporting decision-makers or planners in proposing appropriate flood mitigation strategies, planning policies, and strategies, particularly in the Mekong River watershed.
Downloads
References
Abedi R., Costache R., Shafizadeh-Moghadam H., Pham Q.B., 2022. Flash-flood susceptibility mapping based on XGBoost, random forest and boosted regression trees. Geocarto International, 37(19), 5479–5496. DOI: https://doi.org/10.1080/10106049.2021.1920636
Ahmed N., Hoque M.A.-A., Arabameri A., Pal S.C., Chakrabortty R., Jui J.,2022. Flood susceptibility mapping in Brahmaputra floodplain of Bangladesh using deep boost, deep learning neural network, and artificial neural network. Geocarto International, 37(25), 8770–8791. DOI: https://doi.org/10.1080/10106049.2021.2005698
Al-Areeq A.M., Saleh R.A., Ghaleb M., Abba S.I., Yaseen Z.M., 2024. Implication of novel hybrid machine learning model for flood subsidence susceptibility mapping: A representative case study in Saudi Arabia. Journal of hydrology, 630, 130692. DOI: https://doi.org/10.1016/j.jhydrol.2024.130692
Al-Qadhi A.K., Latip R., Chiong R., Athauda R., Hussin M., 2025. Independent task scheduling algorithms in fog environments from users' and service providers' perspectives: a systematic review. Cluster Computing, 28(3), 209. DOI: https://doi.org/10.1007/s10586-024-04771-2
Alirezapour H., Mansouri N., Mohammad Hasani Zade, B., 2024. A comprehensive survey on feature selection with grasshopper optimization algorithm. Neural Processing Letters, 56(1), 28. DOI: https://doi.org/10.1007/s11063-024-11514-2
Amiri A., Soltani K., Ebtehaj I., Bonakdari H., 2024. A novel machine learning tool for current and future flood susceptibility mapping by integrating remote sensing and geographic information systems. Journal of Hydrology, 632, 130936. DOI: https://doi.org/10.1016/j.jhydrol.2024.130936
Askar S., Zeraat Peyma S., Yousef M.M., Prodanova N.A., Muda I., Elsahabi M., Hatamiafkoueieh J., 2022. Flood susceptibility mapping using remote sensing and integration of decision table classifier and metaheuristic algorithms. Water, 14(19), 3062. DOI: https://doi.org/10.3390/w14193062
Aydin H.E., Iban M.C., 2023. Predicting and analyzing flood susceptibility using boosting-based ensemble machine learning algorithms with SHapley Additive exPlanations. Natural Hazards, 116(3), 2957–2991. DOI: https://doi.org/10.1007/s11069-022-05793-y
Band S.S., Janizadeh S., Chandra Pal S., Saha A., Chakrabortty R., Melesse A.M., Mosavi A., 2020. Flash flood susceptibility modeling using new approaches of hybrid and ensemble tree-based machine learning algorithms. Remote Sensing, 12(21), 3568. DOI: https://doi.org/10.3390/rs12213568
Bhattarai Y., Duwal S., Sharma S., Talchabhadel R., 2024. Leveraging machine learning and open-source spatial datasets to enhance flood susceptibility mapping in transboundary river basin. International Journal of Digital Earth, 17(1), 2313857. DOI: https://doi.org/10.1080/17538947.2024.2313857
Bui D.T., et al., 2020. A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area. Science of the Total Environment, 701, 134413. DOI: https://doi.org/10.1016/j.scitotenv.2019.134413
Bui Q.-T., Nguyen Q.-H., Nguyen X.L., Pham V.D., Nguyen H.D., Pham V.-M., 2020. Verification of novel integrations of swarm intelligence algorithms into deep learning neural network for flood susceptibility mapping. Journal of Hydrology, 581, 124379. DOI: https://doi.org/10.1016/j.jhydrol.2019.124379
Cortiñas-Lorenzo B., Pérez-González F., 2020. Adam and the ants: on the influence of the optimization algorithm on the detectability of DNN watermarks. Entropy, 22(12), 1379. DOI: https://doi.org/10.3390/e22121379
Costache R., Pal S.C., Pande C.B., Islam A.R.M.T., Alshehri F., Abdo H.G., 2024. Flood mapping based on novel ensemble modeling involving the deep learning, Harris Hawk optimization algorithm and stacking based machine learning. Applied Water Science, 14(4), 78. DOI: https://doi.org/10.1007/s13201-024-02131-4
Dagal I., Tanriöven K., Nayir A., Akın B., 2025. Adaptive Stochastic Gradient Descent (SGD) for erratic datasets. Future Generation Computer Systems, 166, 107682. DOI: https://doi.org/10.1016/j.future.2024.107682
Dinh K.D., Anh T.N., Nguyen N.Y., Bui D.D., Srinivasan R., 2020. Evaluation of grid-based rainfall products and water balances over the Mekong river Basin. Remote Sensing, 12(11), 1858. DOI: https://doi.org/10.3390/rs12111858
Dinh Q., Balica S., Popescu I., Jonoski A., 2012. Climate change impact on flood hazard, vulnerability and risk of the Long Xuyen Quadrangle in the Mekong Delta. International Journal of River Basin Management, 10(1), 103–120. DOI: https://doi.org/10.1080/15715124.2012.663383
Do D.T., Nguyen H.Q., Do X.S., 2024. Sustainable development in Mekong Delta, Vietnam: designing resilient cities based on landscape ecological principles. Journal of Asian Architecture and Building Engineering, 1–18. DOI: https://doi.org/10.1080/13467581.2024.2358210
Dodangeh E., Choubin B., Eigdir A.N., Nabipour N., Panahi M., Shamshirband S., Mosavi A., 2020. Integrated machine learning methods with resampling algorithms for flood susceptibility prediction. Science of The Total Environment, 705, 135983. DOI: https://doi.org/10.1016/j.scitotenv.2019.135983
Elghouat A., Algouti A., Algouti A., Baid S., Ezzahzi S., Kabili S., Agli S., 2024. Integrated approaches for flash flood susceptibility mapping: spatial modeling and comparative analysis of statistical and machine learning models. A case study of the Rheraya watershed, Morocco. Journal of Water and Climate Change, 15(8), 3624–3646. DOI: https://doi.org/10.2166/wcc.2024.726
Fang Z., Xu X., Li X., Yang H., Gong C., 2020. SPGD algorithm optimization based on Adam optimizer. Paper presented at the AOPC 2020: Optical Sensing and Imaging Technology. DOI: https://doi.org/10.1117/12.2579991
Ghosh S., Saha S., Bera B., 2022. Flood susceptibility zonation using advanced ensemble machine learning models within Himalayan foreland basin. Natural Hazards Research, 2(4), 363–374. DOI: https://doi.org/10.1016/j.nhres.2022.06.003
Hashem I.A., Alaba F.A., Jumare M.H., Ibrahim A.O., Abulfaraj A.W., 2024. Adaptive stochastic conjugate gradient optimization for backpropagation neural networks. IEEE Access, 12, 33757–33768. DOI: https://doi.org/10.1109/ACCESS.2024.3370859
Hicks F., Peacock T., 2005. Suitability of HEC-RAS for flood forecasting. Canadian Water Resources Journal, 30(2), 159–174. DOI: https://doi.org/10.4296/cwrj3002159
Hitouri S., et al., 2024. Flood susceptibility mapping using SAR data and machine learning algorithms in a small watershed in northwestern Morocco. Remote Sensing, 16(5), 858. DOI: https://doi.org/10.3390/rs16050858
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. DOI: https://doi.org/10.1016/j.ecolind.2023.111417
Hochrainer-Stigler S., Bachner G., Knittel N., Poledna S., Reiter K., Bosello F., 2024. Risk management against indirect risks from disasters: A multi-model and participatory governance framework applied to flood risk in Austria. International Journal of Disaster Risk Reduction, 106, 104425. https://doi.org/10.1016/j.ijdrr.2024.104425 DOI: https://doi.org/10.1016/j.ijdrr.2024.104425
Hoque R., Nakayama D., Matsuyama H., Matsumoto J., 2011. Flood monitoring, mapping and assessing capabilities using RADARSAT remote sensing, GIS and ground data for Bangladesh. Natural Hazards, 57, 525–548. DOI: https://doi.org/10.1007/s11069-010-9638-y
Huang J., Ling S., Wu X., Deng R., 2022. GIS-based comparative study of the Bayesian network, decision table, radial basis function network and stochastic gradient descent for the spatial prediction of landslide susceptibility. Land, 11(3), 436. DOI: https://doi.org/10.3390/land11030436
Im S.S., 2018. Floods in the Mekong River basin Literature reviews Summary Contents.
Islam R., Chowdhury P., 2024. Local-scale flash flood susceptibility assessment in northeastern Bangladesh using machine learning algorithms. Environmental Challenges, 14, 100833. DOI: https://doi.org/10.1016/j.envc.2023.100833
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(2), 1433–1457. DOI: https://doi.org/10.1007/s12145-023-01213-2
Kang J., Zhu X., Shen L., Li M., 2024. Fault diagnosis of a wave energy converter gearbox based on an Adam optimized CNN-LSTM algorithm. Renewable Energy, 231, 121022. DOI: https://doi.org/10.1016/j.renene.2024.121022
Kazemi M., Mohammadi F., Nafooti M.H., Behvar K., Kariminejad N., 2024. Flood susceptibility mapping using machine learning and remote sensing data in the Southern Karun Basin, Iran. Applied Geomatics, 16, 731–750. DOI: https://doi.org/10.1007/s12518-024-00582-7
Khosravi K., Pourghasemi H.R., Chapi K., Bahri M., 2016. Flash flood susceptibility analysis and its mapping using different bivariate models in Iran: a comparison between Shannon's entropy, statistical index, and weighting factor models. Environmental Monitoring and Assessment, 188, 1–21. DOI: https://doi.org/10.1007/s10661-016-5665-9
Kuenzer C., Guo H., Huth J., Leinenkugel P., Li X., Dech S., 2013. Flood mapping and flood dynamics of the Mekong Delta: ENVISAT-ASAR-WSM based time series analyses. Remote Sensing, 5(2), 687–715. DOI: https://doi.org/10.3390/rs5020687
Kurugama K.M., Kazama S., Hiraga Y., Samarasuriya C., 2024. A comparative spatial analysis of flood susceptibility mapping using boosting machine learning algorithms in Rathnapura, Sri Lanka. Journal of Flood Risk Management, 17(2), e12980. DOI: https://doi.org/10.1111/jfr3.12980
Linh N.T.T., Pandey M., Janizadeh S., Bhunia G.S., Norouzi A., Ali S., 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(9), 3301–3318. DOI: https://doi.org/10.1016/j.asr.2022.02.027
Luppichini M., Barsanti M., Giannecchini R., Bini M., 2022. Deep learning models to predict flood events in fast-flowing watersheds. Science of the Total Environment, 813, 151885. DOI: https://doi.org/10.1016/j.scitotenv.2021.151885
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(6), 2608–2623. DOI: https://doi.org/10.2166/wcc.2021.051
Maharjan M., Timilsina S., Ayer S., Singh B., Manandhar B., Sedhain A., 2024. Flood susceptibility assessment using machine learning approach in the Mohana-Khutiya River of Nepal. Natural Hazards Research, 4(1), 32–45. https://doi.org/10.1016/j.nhres.2024.01.001 DOI: https://doi.org/10.1016/j.nhres.2024.01.001
Mangkhaseum S., Bhattarai Y., Duwal S., Hanazawa A., 2024. Flood susceptibility mapping leveraging open-source remote-sensing data and machine learning approaches in Nam Ngum River Basin (NNRB), Lao PDR. Geomatics, Natural Hazards and Risk, 15(1), 2357650. DOI: https://doi.org/10.1080/19475705.2024.2357650
Minh H.V.T., Lien B.T.B., Hong Ngoc D.T., Ty T.V., Ngan N.V.C., Cong N.P., Kumar P., 2024. Understanding rainfall distribution characteristics over the Vietnamese Mekong Delta: a comparison between coastal and inland localities. Atmosphere, 15(2), 217. DOI: https://doi.org/10.3390/atmos15020217
Mrc., 2010. State of the basin report 2010. In: MRC Vientiane.
Narendra B., Setiawan O., Hasan R., Siregar C., Sari N., Sukmana A., Nandini R., 2024. Flood susceptibility mapping based on watershed geomorphometric characteristics and land use/land cover on a small island. Global Journal of Environmental Science and Management, 10(1), 301–320.
Nguyen H.D., 2022. GIS-based hybrid machine learning for flood susceptibility prediction in the Nhat Le-Kien Giang watershed, Vietnam. Earth Science Informatics, 15(4), 2369–2386. DOI: https://doi.org/10.1007/s12145-022-00825-4
Nguyen H.D., Dang D.K., Nguyen Q.-H., Bu Q.-T., Petrisor A.-I., 2022. Evaluating the effects of climate and land use change on the future flood susceptibility in the central region of Vietnam by integrating land change modeler, machine learning methods. Geocarto International, 37(26), 12810–12845. DOI: https://doi.org/10.1080/10106049.2022.2071477
Nguyen H.D., Nguyen Q.-H., Bui Q.-T., 2024. Solving the spatial extrapolation problem in flood susceptibility using hybrid machine learning, remote sensing, and GIS. Environmental Science and Pollution Research, 31(12), 18701–18722. DOI: https://doi.org/10.1007/s11356-024-32163-x
Nguyen H.D., Nguyen Q.-H., Du Q.V.V., Nguyen T.H.T., Nguyen T.G., Bui Q.-T., 2022. A novel combination of Deep Neural Network and Manta Ray Foraging Optimization for flood susceptibility mapping in Quang Ngai province, Vietnam. Geocarto International, 37(25), 7531–7555. DOI: https://doi.org/10.1080/10106049.2021.1975832
Nguyen H.D., Van C.P., Do A. D., 2023. Application of hybrid model-based deep learning and swarm‐based optimizers for flood susceptibility prediction in Binh Dinh province. Vietnam. Earth Science Informatics, 16(2), 1173–1193. DOI: https://doi.org/10.1007/s12145-023-00954-4
Nhu V.-H., Hoang N.-D., Nguyen H., Ngo P.T.T., Bui T.T., Hoa P.V., Bui D.T., 2020. Effectiveness assessment of Keras based deep learning with different robust optimization algorithms for shallow landslide susceptibility mapping at tropical area. Catena, 188, 104458.
Nhu V.-H., Hoang N.-D., Nguyen H., Ngo P.T.T., Thanh Bui T., Hoa P.V., Tien Bui D., 2020. Effectiveness assessment of Keras based deep learning with different robust optimization algorithms for shallow landslide susceptibility mapping at tropical area. Catena, 188, 104458. https://doi.org/10.1016/j.catena.2020.104458. DOI: https://doi.org/10.1016/j.catena.2020.104458
Park E., 2024. Sand mining in the Mekong Delta: Extent and compounded impacts. Science of the Total Environment, 171620. DOI: https://doi.org/10.1016/j.scitotenv.2024.171620
Patro S., Chatterjee C., Mohanty S., Singh R., Raghuwanshi N., 2009. Flood inundation modeling using MIKE FLOOD and remote sensing data. Journal of the Indian Society of Remote Sensing, 37, 107–118. DOI: https://doi.org/10.1007/s12524-009-0002-1
Pham B.T., Luu C., Van Dao D., Van Phong T., Nguyen H.D., Van Le H., Prakash I., 2021. Flood risk assessment using deep learning integrated with multicriteria decision analysis. Knowledge-Based Systems, 219, 106899. DOI: https://doi.org/10.1016/j.knosys.2021.106899
Pham B.T., Luu C., Van Phong T., Trinh P.T., Shirzadi A., Renoud S., Clague J.J., 2021. Can deep learning algorithms outperform benchmark machine learning algorithms in flood susceptibility modeling?. Journal of Hydrology, 592, 125615. DOI: https://doi.org/10.1016/j.jhydrol.2020.125615
Rashidiyan M., Rahimzadegan M., 2024. Investigation and Evaluation of Land Use-Land Cover Change Effects on Current and Future Flood Susceptibility. Natural Hazards Review, 25(1), 04023049. DOI: https://doi.org/10.1061/NHREFO.NHENG-1854
Reyad M., Sarhan A.M., Arafa M., 2023. A modified Adam algorithm for deep neural network optimization. Neural Computing and Applications, 35(23), 17095–17112. DOI: https://doi.org/10.1007/s00521-023-08568-z
Saleem Ashraf M.L., Iftikhar M., Ashraf I., Hassan Z. Y., 2017. Understanding flood risk management in Asia: concepts and challenges. Flood Risk Management, 177. DOI: https://doi.org/10.5772/intechopen.69139
Samanta S., Pal D.K., Palsamanta B., 2018. Flood susceptibility analysis through remote sensing, GIS and frequency ratio model. Applied Water Science, 8(2), 66. DOI: https://doi.org/10.1007/s13201-018-0710-1
Saremi S., Mirjalili S., Lewis A., 2017. Grasshopper optimisation algorithm: theory and application. Advances in Engineering Software, 105, 30–47. DOI: https://doi.org/10.1016/j.advengsoft.2017.01.004
Sun H., Zhou W., Yang J., Shao Y., Xing L., Zhao Q., Zhang L., 2024. An Improved Medical Image Classification Algorithm Based on Adam Optimizer. Mathematics, 12(16), 2509. DOI: https://doi.org/10.3390/math12162509
Tang Z., Zhang H., Yi S., Xiao Y., 2018. Assessment of flood susceptible areas using spatially explicit, probabilistic multicriteria decision analysis. Journal of Hydrology, 558, 144–158. DOI: https://doi.org/10.1016/j.jhydrol.2018.01.033
Tansar H., Babur M., Karnchanapaiboon S.L., 2020. Flood inundation modeling and hazard assessment in Lower Ping River Basin using MIKE FLOOD. Arabian Journal of Geosciences, 13(18), 934. DOI: https://doi.org/10.1007/s12517-020-05891-w
Tehrany M.S., Pradhan B., Jebur M.N., 2013. Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. Journal of Hydrology, 504, 69–79. DOI: https://doi.org/10.1016/j.jhydrol.2013.09.034
Tella A., Balogun A.-L., 2020. Ensemble fuzzy MCDM for spatial assessment of flood susceptibility in Ibadan, Nigeria. Natural Hazards, 104(3), DOI: https://doi.org/10.1007/s11069-020-04272-6
2277–2306.
Triet N.V.K., Dung N.V., Hoang L.P., Duy N.L., Tran D.D., Anh T.T., Apel H., 2020. Future projections of flood dynamics in the Vietnamese Mekong Delta. Science of the Total Environment, 742, 140596. https://doi.org/10.1016/j.scitotenv.2020.140596. DOI: https://doi.org/10.1016/j.scitotenv.2020.140596
Van P., Popescu I., Van Griensven A., Solomatine D., Trung N., Green A., 2012. A study of the climate change impacts on fluvial flood propagation in the Vietnamese Mekong Delta. Hydrology and Earth System Sciences, 16(12), 4637–4649. DOI: https://doi.org/10.5194/hess-16-4637-2012
Vincent A.M., Parthasarathy K., Jidesh P., 2023. Flood susceptibility mapping using AutoML and a deep learning framework with evolutionary algorithms for hyperparameter optimization. Applied Soft Computing, 148, 110846. DOI: https://doi.org/10.1016/j.asoc.2023.110846
Wahba M., Sharaan M., Elsadek W.M., Kanae S., Hassan H.S., 2024. Examination of the efficacy of machine learning approaches in the generation of flood susceptibility maps. Environmental Earth Sciences, 83(14), 429. DOI: https://doi.org/10.1007/s12665-024-11696-x
Wang Y., Fang Z., Hon, H., Peng L., 2020. Flood susceptibility mapping using convolutional neural network frameworks. Journal of Hydrology, 582, 124482. DOI: https://doi.org/10.1016/j.jhydrol.2019.124482
Widya L.K., Rezaie F., Lee W., Lee C.-W., Nurwatik N., Lee S., 2024. Flood susceptibility mapping of Cheongju, South Korea based on the integration of environmental factors using various machine learning approaches. Journal of Environmental Management, 364, 121291. DOI: https://doi.org/10.1016/j.jenvman.2024.121291
Wood M., Haigh I.D., Le Q.Q., Nguyen H.N., Tran H.B., Darby S.E., Hirschi J.J.-M., 2024. Risk of compound flooding substantially increases in the future Mekong River delta. Natural Hazards and Earth System Sciences, 24(10), 3627–3649. DOI: https://doi.org/10.5194/nhess-24-3627-2024
Yaseen Z.M., 2024. Flood hazards and susceptibility detection for Ganga river, Bihar state, India: Employment of remote sensing and statistical approaches. Results in Engineering, 21, 101665. DOI: https://doi.org/10.1016/j.rineng.2023.101665
Youssef A.M., Pradhan B., Sefry S.A., 2016. Flash flood susceptibility assessment in Jeddah city (Kingdom of Saudi Arabia) using bivariate and multivariate statistical models. Environmental earth sciences, 75(1), 12. DOI: https://doi.org/10.1007/s12665-015-4830-8
Zhao G., Pang B., Xu Z., Peng D., Xu L., 2019. Assessment of urban flood susceptibility using semi-supervised machine learning model. Science of the Total Environment, 659, 940–949. DOI: https://doi.org/10.1016/j.scitotenv.2018.12.217
Zhao G., Pang B., Xu Z., Peng D., Zuo D., 2020. Urban flood susceptibility assessment based on convolutional neural networks. Journal of Hydrology, 590, 125235. DOI: https://doi.org/10.1016/j.jhydrol.2020.125235
Zhu K., Lai C., Wang Z., Zeng Z., Mao Z., Chen X., 2024. A novel framework for feature simplification and selection in flood susceptibility assessment based on machine learning. Journal of Hydrology: Regional Studies, 52, 101739. DOI: https://doi.org/10.1016/j.ejrh.2024.101739
Zhu X., Guo H., Huang J.J., 2024. Urban flood susceptibility mapping using remote sensing, social sensing and an ensemble machine learning model. Sustainable Cities and Society, 108, 105508. https://doi.org/10.1016/j.scs.2024.105508. DOI: https://doi.org/10.1016/j.scs.2024.105508
Downloads
Published
How to Cite
Issue
Section
