Abstract
Owing to global climate change, the frequency of disasters has increased twelve-fold, with a corresponding approximately seventeen-fold increase in economic damages over the past six decades. Notably, severe flood damage has been occurring in Asia along the Pacific coast due to extreme weather events, including torrential rains and typhoons, which have been becoming increasingly frequent and prolonging the rainy season. In the Eastern Visayas region, the management and monitoring facilities for flood observation data, as well as the forecasting and warning systems suitable for the local area, are insufficient. The warning system introduced through overseas grants is limited in operation in some areas of the city. Furthermore, although an organization has jurisdiction over flood forecasting and warning, the system’s operation is not systematic and is limited. Additionally, there is a shortage of technical manpower. In this study, we utilized deep learning models to forecast flood water levels in the CarayCaray Basin on Biliran Island, located in Eastern Visayas, the Philippines. Additionally, a flood risk classification was applied to evaluate the degree of risk associated with the predicted water levels. The predicted water levels for each model were compared with the observed water level data. The evaluation of the predictive performance of each model resulted in an NRMSE value of 9.48. Moreover, the accuracy of the DNN model was found to be the best among the flood water level prediction models. To implement the flood risk classification, we utilized extreme gradient boosting, random forest, and decision tree models. The application of these models resulted in an F1-score of 0.92 for the extreme gradient boost model, which exhibited the highest accuracy. With an increasing need for disaster (flood) management, AI-based predictive models are anticipated to reduce the damage caused by natural disasters and enhance disaster mitigation systems. Real-time collection of rainfall and water level data enables continuous learning. Furthermore, if a clear flood warning based on learned flood level patterns is issued, preemptive measures can be taken before intense flood damage occurs.
Similar content being viewed by others
References
Abrahart R, Kneale PE, See LM (2004) Neural networks for hydrological modeling. CRC Press, DOI: https://doi.org/10.1201/9780203024119
Amit Y, Geman D, Wilder K (1997) Joint induction of shape features and tree classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(11):1300–1305, DOI: https://doi.org/10.1109/34.632990
Assem H, Ghariba S, Makrai G Johnston P, Gill L, Pilla F (2017) Urban water flow and water level prediction based on deep learning. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases 317–329, DOI: https://doi.org/10.1007/978-3-319-71273-4_26
Bicknell BR, Imhoff JC, Kittle Jr JL, Donigian Jr AS, Johanson RC (1997) Hydrological simulation program—FORTRAN user’s manual for version 11. Environmental Protection Agency Report No. EPA/600/R-97/080. US Environmental Protection Agency, Athens, Ga
Breiman L (2001) Random forests. Machine Learning 45(1):5–32, DOI: https://doi.org/10.1023/a:1010933404324
Breiman L, Ihaka R (1984) Nonlinear discriminant analysis via scaling and ACE. Davis One Shields Avenue Davis, CA, USA: Department of Statistics, University of California
Chen PA, Chang LC, Chang FJ (2013) Reinforced recurrent neural networks for multi-step-ahead flood forecasts. Journal of Hydrology 497:71–79, DOI: https://doi.org/10.1016/j.jhydrol.2013.05.038
Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting system. In Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining 785–794, DOI: https://doi.org/10.1145/2939672.2939785
Choi C, Kim J, Han H, Han D, Kim HS (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
De Leon EG, Pittock J (2017) Integrating climate change adaptation and climate-related disaster risk-reduction policy in developing countries: A case study in the Philippines. Climate and Development 9(5):471–478, DOI: https://doi.org/10.1080/17565529.2016.1174659
Devia GK, Ganasri BP, Dwarakish GS (2015) A review on hydrological models. Aquatic Procedia 4:1001–1007, DOI: https://doi.org/10.1016/j.aqpro.2015.02.126
Dreyfus HL (1990) Being-in-the-world: A commentary on Heidegger’s being in time, Division I. Mit Press
Eslamitabar V, Ahmadi F, Sharafati A, Rezaverdinejad V (2022) Bivariate simulation of river flow using hybrid intelligent models in subbasins of Lake Urmia, Iran. Acta Geophysica 1–20, DOI: 10.1007/s11600-022-00933-1
Germanwatch, Global Climate Risk Index (2021) Who suffers most from extreme weather events? Weather-related Loss Events in 2019 and 2000 to 2019
Ghumman AR, Ghazaw YM, Sohail AR, Watanabe K (2011) Runoff forecasting by artificial neural network and conventional model. Alexandria Engineering Journal 50(4):345–350, DOI: https://doi.org/10.1016/j.aej.2012.01.005
Granata F, Gargano R, De Marinis G (2016) Support vector regression for rainfall-runoff modeling in urban drainage: A comparison with the EPA’s storm water management model. Water 8(3):69, DOI: https://doi.org/10.3390/w8030069
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
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
Ho TK, Baird HS (1998) Pattern classification with compact distribution maps. Computer Vision and Image Understanding 70(1):101–110, DOI: https://doi.org/10.1006/cviu.1998.0624
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
Kamiński B, Jakubczyk M, Szufel P (2018) A framework for sensitivity analysis of decision trees. Central European Journal of Operations Research 26(1):135–159, DOI: https://doi.org/10.1007/s10100-017-0479-6
Kang NR, Noh HS, Lee JS, Lim SH, Kim HS (2013) Runoff simulation of an urban drainage system using radar rainfall data. Journal of Wetlands Research 15(3):413–422, DOI: https://doi.org/10.17663/JWR.2013.15.3.413
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, 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, 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 BK, Kim SD, Lee ET, Kim HS (2007) Methodology for estimating ranges of SWAT model parameters: Application to Imha Lake inflow and suspended sediments. KSCE Journal of Civil and Environmental Engineering Research 27(6B):661–668
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
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
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
McCullock WS, Pitts WV (1956) Automata studies. edited by CE Shannon and J. McCarthy
Merufinia E, Sharafati A, Abghari H, Hassanzadeh Y (2023) On the simulation of streamflow using hybrid tree-based machine learning models: A case study of Kurkursar basin, Iran. Arabian Journal of Geosciences 16(1):1–23, DOI: https://doi.org/10.1007/s12517-022-11045-x
Ministry of the Interior and Safety (2022) The 2021 annual natural disaster report, MOIS, Korea
Montanari A, Rosso R, Taqqu MS (1997) Fractionally differenced ARIMA models applied to hydrologic time series: Identification, estimation, and simulation. Water Resources Research 33(5):1035–1044, DOI: https://doi.org/10.1029/97WR00043
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
Neitsch SL, Arnold JG, Kiniry JR Williams JR (2011) Soil and water assessment tool theoretical documentation version 2009. Texas Water Resources Institute
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
Park MK, Yoon YS, Lee HH, Kim JH (2018) Application of recurrent neural network for inflow prediction into multi-purpose dam basin. Journal of Korea Water Resources Association 51(12):1217–1227, DOI: https://doi.org/10.3741/JKWRA.2018.51.12.1217
Riad S, Mania J, Bouchaou L, Najjar Y (2004) Predicting catchment flow in a semi-arid region via an artificial neural network technique. Hydrological Processes 18(13):2387–2393, DOI: https://doi.org/10.1002/hyp.1469
Sharafati A, Zahabiyoun B (2014) Rainfall threshold curves extraction by considering rainfall-runoff model uncertainty. Arabian Journal for Science and Engineering 39:6835–6849, DOI: https://doi.org/10.1007/s13369-014-1246-9
Shoaib M, Shamseldin AY, Melville BW, Khan MM (2016) A comparison between wavelet based static and dynamic neural network approaches for runoff prediction. Journal of Hydrology 535:211–225, DOI: https://doi.org/10.1016/j.jhydrol.2016.01.076
Yan J, Jin J, Chen F, Yu G Yn H, Wang W (2018) Urban flash flood forecast using support vector machine and numerical simulation. Journal of Hydroinformatics 20(1):221–231, DOI: https://doi.org/10.2166/hydro.2017.175
Acknowledgments
This research was conducted with the support of the “National R&D Project for Smart Construction Technology (No. 22SMIP-A156365-03)” funded by the Korea Agency for Infrastructure Technology/Advancement under the Ministry of Land, Infrastructure and Transport and managed by the Korea Expressway Corporation.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Kim, D., Park, J., Han, H. et al. Application of AI-Based Models for Flood Water Level Forecasting and Flood Risk Classification. KSCE J Civ Eng 27, 3163–3174 (2023). https://doi.org/10.1007/s12205-023-2175-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12205-023-2175-5