Abstract
Vietnam faces on significant human and property losses from floods almost every year. Therefore, the aim of this study is to provide timely and highly accurate flood prediction information using a developed hybrid model by combination of the rainfall-runoff model and AI-based model. We used Tank model as a rainfall-runoff model for peak flood discharge and parameter calibration was conducted by GA (genetic algorithm) and PS (pattern search). The sum of squared residuals (SSR) and weighted sum of squared residuals (WSSR) as objective functions were used for peak flood discharge evaluation. The simulated flood discharge was converted as flood water level by rating curve and Monte Carlo simulation was applied to estimate the confidence bounds for 95% confidence level. These estimates were then utilized as input data for the AI-based models to identify the strengths and weaknesses of each model and develop an optimal flood water level prediction model. Two AI-based models, deep neural network (DNN) and long short-term memory (LSTM), were used for flood water level prediction. The LSTM model demonstrated the best performance with a correlation coefficient (CC) of 0.98, normalized root mean square error (NRMSE) of 0.04, and Nash-Sutcliffe efficiency (NSE) of 0.98.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abdolabadi H, Amaya M, Little JC (2023) Intimate coupling of a hydrologic model with an economic input–output model using system dynamics. Applied Water Science 13(3):75, DOI: https://doi.org/10.1007/s13201-023-01872-y
Blöschl G (2016) Recent advances in flood hydrology–contributions to implementing the Flood Directive. Acta Hydrotechnica 29(50):13–22
Choi C, Kim J, Han H, Han D, Kim H S (2019) Development of water level prediction models using machine learning in wetlands: A case study of Upo wetland in South Korea. Water 12(1):93, DOI: https://doi.org/10.3390/w12010093
Choi C, Kim J, Kim J, Kim D, Bae Y, Kim HS (2018) Development of heavy rain damage prediction model using machine learning based on big data. Advances in Meteorology, 2018, DOI: https://doi.org/10.1155/2018/5024930
Civicioglu P (2012) Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm. Computers & Geosciences 46:229–247, DOI: https://doi.org/10.1016/j.cageo.2011.12.011
Costache R (2019) Flood susceptibility assessment by using bivariate statistics and machine learning models-a useful tool for flood risk management. Water Resources Management 33(9):3239–3256, DOI: https://doi.org/10.1007/s11269-019-02301-z
Dhondia JF, Stelling GS (2004) Sobek one dimensional–two dimensional integrated hydraulic model for flood simulation–its capabilities and features explained. In Hydroinformatics: (In 2 Volumes, with CD-ROM) 1867–1874, DOI: https://doi.org/10.1142/9789812702838_0230
Di Baldassarre G Schumann G, Bates PD, Freer J E, Beven K J (2010) Flood-plain mapping: A critical discussion of deterministic and probabilistic approaches. Hydrological Sciences Journal, DOI: https://doi.org/10.1080/02626661003683389
Dolan ED, Lewis RM, Torczon V (2003) On the local convergence of pattern search. SIAM Journal on Optimization 14(2):567–583, DOI: https://doi.org/10.1137/S1052623400374495
Dreyfus HL (1990) Being-in-the-world: A commentary on Heidegger’s being in time, Division I. Mit Press
Ferraris L, Rudari R, Siccardi F (2002) The uncertainty in the prediction of flash floods in the northern Mediterranean environment. Journal of Hydrometeorology 3(6):714–727, DOI: https://doi.org/10.1175/1525-7541(2002)003<0714:TUITPO>2.0.CO;2
Georgakakos KP (1992) Advances in forecasting flash floods. Proceedings of the CCNAA-AIT Joint Seminar on Prediction and Damage Mitigation of Meteorologically Induced Natural Disasters 21–24
Georgakakos KP, Hudlow MD (1984) Quantitative precipitation forecast techniques for use in hydrologic forecasting. Bulletin of the American Meteorological Society 65(11);1186–1200, DOI: https://doi.org/10.1175/1520-0477(1984)065<1186:QPFTFU>2.0.CO;2
Graves A, Liwicki M, Fernández S, Bertolami R, Bunke H, Schmidhuber J (2008) A novel connectionist system for unconstrained handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(5):855–868, DOI: https://doi.org/10.1109/TPAMI.2008.137
Ha H, Luu C, Bui QD, Pham DH, Hoang T, Nguyen VP, Vu MT, Pham BT (2021) Flash flood susceptibility prediction mapping for a road network using hybrid machine learning models. Natural Hazards 109(1):1247–1270, DOI: https://doi.org/10.1007/s11069-021-04877-5
Han D, Chan L, Zhu N (2007) Flood forecasting using support vector machines. Journal of Hydroinformatics 9(4):267–276, DOI: https://doi.org/10.2166/hydro.2007.027
Han H, Choi C, Jung J, Kim HS (2021) Application of sequence to sequence learning based LSTM model (LSTM-s2s) for forecasting dam inflow. Journal of Korea Water Resources Association 54(3):157–166, DOI: https://doi.org/10.3741/JKWRA.2021.54.3.157
Herath HMVV, Chadalawada J, Babovic V (2021) Genetic programming for hydrological applications: To model or to forecast that is the question. Journal of Hydroinformatics 23(4):740–763, DOI: https://doi.org/10.2166/hydro.2021.179
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Computation 9(8):1735–1780, DOI: https://doi.org/10.1162/neco.1997.9.8.1735
Holland JH (1984) Genetic algorithms and adaptation. Adaptive Control of Ill-Defined Systems, 317–333, DOI: https://doi.org/10.1007/978-1-4684-8941-5_21
Jahandideh-Tehrani M, Helfer F, Zhang H, Jenkins G, Yu Y (2020) Hydrodynamic modelling of a flood-prone tidal river using the 1D model MIKE HYDRO River: Calibration and sensitivity analysis. Environmental Monitoring and Assessment 192:1–18, https://link.springer.com/article/10.1007/s10661-019-8049-0
James F (1980) Monte carlo theory and practice. Reports on Progress in Physics 43(9):1145, DOI: https://doi.org/10.1088/0034-4885/43/9/002
Kim D, Choi C, Kim J, Lee J, Bae Y, Kim HS (2018) Analysis of heavy rain damage considering regional characteristics. Journal of the Korean Society of Hazard Mitigation 18(4):311–320, DOI: https://doi.org/10.9798/KOSHAM.2018.18.4.311
Kim D, Kim J, Choi C, Wang W, You Y, Kim HS (2019) Estimations of hazard-triggering rainfall and breach discharge of aging reservoir. Journal of the Korean Society of Hazard Mitigation 19(7):421–432, DOI: https://doi.org/10.9798/KOSHAM.2019.19.7.421
Kim D, Kim J, Kwak J, Necesito IV, Kim J, Kim HS (2020) Development of water level prediction models using deep neural network in mountain wetlands. Journal of Wetlands Research 22(2):106–112, DOI: https://doi.org/10.17663/JWR.2020.22.2.106
Kim D, Han H, Wang W, Kang Y, Lee H, Kim HS (2022a) Application of deep learning models and network method for comprehensive air-quality index prediction. Applied Sciences 12(13):6699, DOI: https://doi.org/10.3390/app12136699
Kim D, Han H, Wang W, Kim HS (2022b) Improvement of deep learning models for river water level prediction using complex network method. Water 14(3):466, DOI: https://doi.org/10.3390/w14030466
Kim D, Lee K, Hwang-Bo J, Kim HS, Kim S (2022c) Development of the method for flood water level forecasting and flood damage warning using an ai-based model. Journal of the Korean Society of Hazard Mitigation 22(4):145–156, DOI: https://doi.org/10.9798/KOSHAM.2022.22.4.145
Kim D, Lee J, Kim J, Lee M, Wang W, Kim HS (2022d) Comparative analysis of long short-term memory and storage function model for flood water level forecasting of bokha stream in namhan river, Korea. Journal of Hydrology, 127415, DOI: https://doi.org/10.1016/j.jhydrol.2021.127415
Kim J, Kim D, Joo H, Noh H, Lee J, Kim HS (2018) Case study: On objective functions for the peak flow calibration and for the representative parameter estimation of the basin. Water 10(5): 614, DOI: https://doi.org/10.3390/w10050614
Kim J, Kim D, Wang W, Lee H, Lee M, Kim HS (2021) Comparative analysis of linear model and deep learning algorithm for water usage prediction. Journal of Korea Water Resources Association 54(spc1): 1083–1093, DOI: https://doi.org/10.3741/JKWRA.2021.54.S-1.1083
Kroese DP, Brereton T, Taimre T, Botev ZI (2014) Why the Monte Carlo method is so important today. Wiley Interdisciplinary Reviews: Computational Statistics 6(6):386–392, DOI: https://doi.org/10.1002/wics.1314
Krzysztofowicz R (1999) Bayesian theory of probabilistic forecasting via deterministic hydrologic model. Water Resources Research 35(9):2739–2750, DOI: https://doi.org/10.1029/1999WR900099
Kwak J, Kim S, Jung J, Singh VP, Lee DR, Kim HS (2016) Assessment of meteorological drought in Korea under climate change. Advances in Meteorology, 2016, DOI: https://doi.org/10.1155/2016/1879024
Lee K, Choi C, Shin DH, Kim HS (2020) Prediction of heavy rain damage using deep learning. Water 12(7):1942, DOI: https://doi.org/10.3390/w12071942
Lee H, Kim HS, Kim S, Kim D, Kim J (2021) Development of a method for urban flooding detection using unstructured data and deep learing. Journal of Korea Water Resources Association 54(12):1233–1242, DOI: https://doi.org/10.3741/JKWRA.2021.54.12.1233
Lekkas DF, Onof C, Lee MJ, Baltas EA (2004) Application of artificial neural networks for flood forecasting. Global Nest Journal 6(3): 205–211, DOI: https://doi.org/10.30955/gnj.000305
Liong SY, Sivapragasam C (2002) Flood stage forecasting with support vector machines 1. JAWRA Journal of the American Water Resources Association 38(1):173–186, DOI: https://doi.org/10.1111/j.1752-1688.2002.tb01544.x
Mason DC, Schumann GP, Neal JC, Garcia-Pintado J, Bates PD (2012) Automatic near real-time selection of flood water levels from high resolution Synthetic Aperture Radar images for assimilation into hydraulic models: A case study. Remote Sensing of Environment 124:705–716, DOI: https://doi.org/10.1016/j.rse.2012.06.017
Matgen P, Schumann G, Henry JB, Hoffmann L, Pfister L (2007) Integration of SAR-derived river inundation areas, high-precision topographic data and a river flow model toward near real-time flood management. International Journal of Applied Earth Observation and Geoinformation, DOI: https://doi.org/10.1016/j.jag.2006.03.003
McCullock WS, Pitts WV (1956) Automata studies. Edited by CE Shannon and J. McCarthy
McKinnon KI (1998) Convergence of the nelder—mead simplex method to a nonstationary point. SIAM Journal on Optimization 9(1):148–158, DOI: https://doi.org/10.1137/S1052623496303482
Mojaddadi H, Pradhan B, Nampak H, Ahmad N, Ghazali AHB (2017) Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS. Geomatics, Natural Hazards and Risk 8(2):1080–1102, DOI: https://doi.org/10.1080/19475705.2017.1294113
Mosavi A, Ozturk P, Chau KW (2018) Flood prediction using machine learning models: Literature review. Water 10(11):1536, DOI: https://doi.org/10.3390/w10111536
Nachappa TG, Piralilou ST, Gholamnia K, Ghorbanzadeh O, Rahmati O, Blaschke T (2020) Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory. Journal of hydrology 590:125275, DOI: https://doi.org/10.1016/j.jhydrol.2020.125275
Nhu OL, Thuy NTT, Wilderspin I, Coulier M (2011) A preliminary analysis of flood and storm disaster data in Vietnam. Ha Noi
Noh H, Lee J, Kang N, Lee D, Kim HS, Kim S (2016) Long-term simulation of daily streamflow using radar rainfall and the SWAT model: A case study of the Gamcheon basin of the Nakdong River, Korea. Advances in Meteorology, 2016, DOI: https://doi.org/10.1155/2016/2485251
Patrascu M, Stancu AF, Pop F (2014) HELGA: A heterogeneous encoding lifelike genetic algorithm for population evolution modeling and simulation. Soft Computing 18:2565–2576, DOI: https://doi.org/10.1007/s00500-014-1401-y
Pham BT, Luu C, Phong TV, Nguyen HD, Le HV, Tran TQ, Ta HT, Prakash I (2021) Flood risk assessment using hybrid artificial intelligence models integrated with multi-criteria decision analysis in Quang Nam Province. Vietnam. Journal of Hydrology 592: 125815, DOI: https://doi.org/10.1016/j.jhydrol.2020.125815
Pilarczyk KW, Nuoi NS (2005) Experience and practices on flood control in Vietnam. Water International 30(1):114–122, DOI: https://doi.org/10.1080/02508060508691843
Rahman M, Ningsheng C, Islam MM, Dewan A, Iqbal J, Washakh RM A, Shufeng T (2019) Fslood susceptibility assessment in Bangladesh using machine learning and multi-criteria decision analysis. Earth Systems and Environment 3:585–601, DOI: https://doi.org/10.1007/s41748-019-00123-y
Saha A, Pal SC, Arabameri A, Blaschke T, Panahi S, Chowdhuri I, Chakrabortty R, Costache R, Arora A (2021a) Optimization modelling to establish false measures implemented with ex-situ plant species to control gully erosion in a monsoon-dominated region with novel in-situ measurements. Journal of Environmental Management 287: 112284, DOI: https://doi.org/10.1016/j.jenvman.2021.112284
Saha A, Pal SC, Arabameri A, Chowdhuri I, Rezaie F, Chakrabortty R, Roy P, Shit M (2021b) Optimization modelling to establish false measures implemented with ex-situ plant species to control gully erosion in a monsoon-dominated region with novel in-situ measurements. Journal of Environmental Management 287:112284, DOI: https://doi.org/10.1016/j.jenvman.2021.112284
Schumann G, Bates PD, Horritt MS, Matgen P, Pappenberger F (2009) Progress in integration of remote sensing-derived flood extent and stage data and hydraulic models. Reviews of Geophysics 47(4), DOI: https://doi.org/10.1029/2008RG000274
Shafapour Tehrany M, Kumar L, Neamah Jebur M, Shabani F (2019) Evaluating the application of the statistical index method in flood susceptibility mapping and its comparison with frequency ratio and logistic regression methods. Geomatics, Natural Hazards and Risk 10(1):79–101, DOI: https://doi.org/10.1080/19475705.2018.1506509
Sugawara M (1984) Tank model with snow component. Study Report of National Research Center for Disaster Prevention, 293
Swiechowski M, Godlewski K, Sawicki B, Mandziuk J (2023) Monte Carlo tree search: A review of recent modifications and applications. Artificial Intelligence Review 56(3):2497–2562, DOI: https://doi.org/10.1007/s10462-022-10228-y
Van Anh Truong AQD, Bui NQ, Van Hiep Pham DD, Nguyen XQT, Tran TMA (2021) The advantage of using satellite data together with the hydraulic model in flood hazard assessment: A case study in Ca River downstream. Vietnam Journal of Hydrometeorology 8:28–43, DOI: https://doi.org/10.36335/VNJHM.2021(8).28-43
Wagenaar D, Curran A, Balbi M, Bhardwaj A, Soden R, Hartato E, Mestav Sarica G, Ruangpan L, Molinario G, Lallemant D (2020) Invited perspectives: How machine learning will change flood risk and impact assessment. Natural Hazards and Earth System Sciences 20(4):1149–1161, DOI: https://doi.org/10.5194/nhess-20-1149-2020
Acknowledgments
This work was supported by INHA UNIVERSITY Research Grant (Grant Number 68859-1).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Kim, D., Han, H., Lee, H. et al. Predicting Flood Water Level Using Combined Hybrid Model of Rainfall-Runoff and AI-Based Models. KSCE J Civ Eng 28, 1580–1593 (2024). https://doi.org/10.1007/s12205-023-1147-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12205-023-1147-0