Improving Long-Term Flood Forecasting Accuracy Using Ensemble Deep Learning Models and an Attention Mechanism
Publication: Journal of Hydrologic Engineering
Volume 29, Issue 6
Abstract
Floods, as major natural disasters, cause massive property destruction and death. Understanding the occurrence time of this event by advance notice helps consider operational flood prevention systems and platforms. Precise flood forecasting provides a suitable time for policymakers and the public to consider helpful responses to this event. This study introduces an innovative methodology to enhance the precision of long-term flood predictions by employing a multistep forecasting approach. Our approach leverages historical time-series data on precipitation and streamflow to train an autoencoder algorithm. The primary objective is to develop advanced forecasting models to predict 12-step-weekly ahead flood occurrences during the critical April to July period from 2019 to 2021 within the DuPage River basin, Illinois, USA. In order to achieve this goal, we explore three deep learning techniques: bidirectional long short-term memory (BI-LSTM), ensemble long short-term memory (E-LSTM), and ensemble long short-term memory-gated recurrent unit (E-LSTM-GRU). Then, we integrate an attention mechanism (AM) that utilizes dynamic fusion techniques to emphasize the salient features of ensemble models. A dedicated fusion model is developed for each forecasting stage, effectively consolidating the predictions from various deep-learning models. Additionally, two traditional machine learning techniques, namely MLP and SVM models, are used to compare and justify the efficiency of applied deep learning models. The performance evaluation of our approach using statistical error metrics, including coefficient of determination (), normalized root mean square error, normalized mean absolute error, mean absolute percentage error, Nash–Sutcliffe efficiency, Kling–Gupta efficiency, and percent bias, for the 12th step prediction reveals impressive results, with average values of 0.976, 2.393, 1.892, 20.956, 0.967, 0.923, and 2.307, respectively. These findings underscore the capability of our proposed models to significantly reduce uncertainty in flood forecasting, thus enhancing the reliability and accuracy of future predictions.
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Data Availability Statement
All data and models that support the findings of this study are available from the corresponding author upon reasonable request.
References
Alizadeh, B., A. G. Bafti, H. Kamangir, Y. Zhang, D. B. Wright, and K. J. Franz. 2021. “A novel attention-based LSTM cell post-processor coupled with Bayesian optimization for streamflow prediction.” J. Hydrol. 601 (Oct): 126526. https://doi.org/10.1016/j.jhydrol.2021.126526.
Apaydin, H., M. T. Sattari, K. Falsafian, and R. Prasad. 2021. “Artificial intelligence modelling integrated with Singular Spectral analysis and Seasonal-Trend decomposition using Loess approaches for streamflow predictions.” J. Hydrol. 600 (Sep): 126506. https://doi.org/10.1016/j.jhydrol.2021.126506.
Awol, F. S., P. Coulibaly, and I. Tsanis. 2021. “Identification of combined hydrological models and numerical weather predictions for enhanced flood forecasting in a semiurban watershed.” J. Hydrol. Eng. 26 (1): 04020057. https://doi.org/10.1061/(ASCE)HE.1943-5584.0002018.
Aydin, H. E., and M. C. Iban. 2023. “Predicting and analyzing flood susceptibility using boosting-based ensemble machine learning algorithms with SHapley Additive exPlanations.” Nat. Hazards 116 (3): 2957–2991. https://doi.org/10.1007/s11069-022-05793-y.
Bahdanau, D., K. Cho, and Y. Bengio. 2014. “Neural machine translation by jointly learning to align and translate.” Preprint, submitted September 1, 2014. https://arxiv.org/abs/1409.0473.
Bank, D., N. Koenigstein, and R. Giryes. 2023. “Autoencoders.” In Machine learning for data science handbook: Data mining and knowledge discovery handbook, 353–374. Cham, Switzerland: Springer.
Bengio, Y., A. Courville, and P. Vincent. 2013. “Representation learning: A review and new perspectives.” IEEE Trans. Pattern Anal. Mach. Intell. 35 (8): 1798–1828. https://doi.org/10.1109/TPAMI.2013.50.
Bennett, J. C., Q. J. Wang, M. Li, D. E. Robertson, and A. Schepen. 2016. “Reliable long-range ensemble streamflow forecasts: Combining calibrated climate forecasts with a conceptual runoff model and a staged error model.” Water Resour. Res. 52 (10): 8238–8259. https://doi.org/10.1002/2016WR019193.
Berkhahn, S., L. Fuchs, and I. Neuweiler. 2019. “An ensemble neural network model for real-time prediction of urban floods.” J. Hydrol. 575 (Aug): 743–754. https://doi.org/10.1016/j.jhydrol.2019.05.066.
Bi, H., L. Lu, and Y. Meng. 2023. “Hierarchical attention network for multivariate time series long-term forecasting.” Appl. Intell. 53 (5): 5060–5071. https://doi.org/10.1007/s10489-022-03825-5.
Cai, L., Y. Qi, W. Wei, J. Wu, and J. Li. 2019. “mrMoulder: A recommendation-based adaptive parameter tuning approach for big data processing platform.” Future Gener. Comput. Syst. 93 (Apr): 570–582. https://doi.org/10.1016/j.future.2018.05.080.
Chen, C., J. Jiang, Z. Liao, Y. Zhou, H. Wang, and Q. Pei. 2022. “A short-term flood prediction based on spatial deep learning network: A case study for Xi County, China.” J. Hydrol. 607 (Apr): 127535. https://doi.org/10.1016/j.jhydrol.2022.127535.
Chen, M., K. Papadikis, C. Jun, and N. Macdonald. 2023. “Linear, nonlinear, parametric and nonparametric regression models for nonstationary flood frequency analysis.” J. Hydrol. 616 (Jan): 128772. https://doi.org/10.1016/j.jhydrol.2022.128772.
Chen, S., and W. Guo. 2023. “Auto-encoders in deep learning—A review with new perspectives.” Mathematics 11 (8): 1777. https://doi.org/10.3390/math11081777.
Cheng, M., F. Fang, T. Kinouchi, I. M. Navon, and C. C. Pain. 2020. “Long lead-time daily and monthly streamflow forecasting using machine learning methods.” J. Hydrol. 590 (Nov): 125376. https://doi.org/10.1016/j.jhydrol.2020.125376.
Chung, J., C. Gulcehre, K. Cho, and Y. Bengio. 2014. “Empirical evaluation of gated recurrent neural networks on sequence modeling.” Preprint, submitted December 11, 2014. https://arxiv.org/abs/1412.3555.
County of DuPage, Illinois. 2022. “DuPage county countywide stormwater & floodplain ordinance.” Accessed September 1, 2022. https://cms5.revize.com/revize/dupage/Community%20Services/Documents/Community%20Development%20Block%20Grant/Amendments/CSFO%20Effective%209-13-22.pdf.
Cui, Z., S. Guo, Y. Zhou, and J. Wang. 2023. “Exploration of dual-attention mechanism-based deep learning for multi-step-ahead flood probabilistic forecasting.” J. Hydrol. 622 (Jul): 129688. https://doi.org/10.1016/j.jhydrol.2023.129688.
Cui, Z., Y. Zhou, S. Guo, J. Wang, and C.-Y. Xu. 2022. “Effective improvement of multi-step-ahead flood forecasting accuracy through encoder-decoder with an exogenous input structure.” J. Hydrol. 609 (Jun): 127764. https://doi.org/10.1016/j.jhydrol.2022.127764.
da Costa, T. A. G., R. I. Meneguette, and J. Ueyama. 2022. “Providing a greater precision of Situational Awareness of urban floods through Multimodal Fusion.” Expert Syst. Appl. 188 (Feb): 115923. https://doi.org/10.1016/j.eswa.2021.115923.
Dehghani, A., H. M. Z. H. Moazam, F. Mortazavizadeh, V. Ranjbar, M. Mirzaei, S. Mortezavi, J. L. Ng, and A. Dehghani. 2023. “Comparative evaluation of LSTM, CNN, and ConvLSTM for hourly short-term streamflow forecasting using deep learning approaches.” Ecol. Inf. 75 (Jul): 102119. https://doi.org/10.1016/j.ecoinf.2023.102119.
Dembele, M., M. Hrachowitz, H. H. Savenije, G. Mariéthoz, and B. Schaefli. 2020. “Improving the predictive skill of a distributed hydrological model by calibration on spatial patterns with multiple satellite data sets.” Water Resour. Res. 56 (1): e2019WR026085. https://doi.org/10.1029/2019WR026085.
Dtissibe, F. Y., A. A. A. Ari, H. Abboubakar, A. N. Njoya, A. Mohamadou, and O. Thiare. 2024. “A comparative study of Machine Learning and Deep Learning methods for flood forecasting in the Far-North region, Cameroon.” Sci. Afr. 23 (Mar): e02053. https://doi.org/10.1016/j.sciaf.2023.e02053.
Dtissibe, F. Y., A. A. A. Ari, C. Titouna, O. Thiare, and A. M. Gueroui. 2020. “Flood forecasting based on an artificial neural network scheme.” Nat. Hazards 104 (Nov): 1211–1237. https://doi.org/10.1007/s11069-020-04211-5.
Dyer, C., M. Ballesteros, W. Ling, A. Matthews, and N. A. Smith. 2015. “Transition-based dependency parsing with stack long short-term memory.” Preprint, submitted May 29, 2015. https://arxiv.org/abs/1505.08075.
Fahad, M. G. R., R. Nazari, M. H. Motamedi, and M. Karimi. 2022. “A decision-making framework integrating fluid and solid systems to assess resilience of coastal communities experiencing extreme storm events.” Reliab. Eng. Syst. Saf. 221 (May): 108388. https://doi.org/10.1016/j.ress.2022.108388.
Fooladi, M., M. H. Golmohammadi, I. Rahimi, H. R. Safavi, and M. R. Nikoo. 2023. “Assessing the changeability of precipitation patterns using multiple remote sensing data and an efficient uncertainty method over different climate regions of Iran.” Expert Syst. Appl. 221 (Jul): 119788. https://doi.org/10.1016/j.eswa.2023.119788.
Fooladi, M., M. H. Golmohammadi, H. R. Safavi, and V. P. Singh. 2021. “Fusion-based framework for meteorological drought modeling using remotely sensed datasets under climate change scenarios: Resilience, vulnerability, and frequency analysis.” J. Environ. Manage. 297 (Nov): 113283. https://doi.org/10.1016/j.jenvman.2021.113283.
Fooladi, M., M. R. Nikoo, R. Mirghafari, C. A. Madramootoo, G. Al-Rawas, and R. Nazari. 2024. “Robust clustering-based hybrid technique enabling reliable reservoir water quality prediction with uncertainty quantification and spatial analysis.” J. Environ. Manage. 362 (Jun): 121259. https://doi.org/10.1016/j.jenvman.2024.121259.
Fu, Y., X. Zhou, B. Li, and Y. Zhang. 2023. “Daily water level time series prediction using ECRBM-based ensemble optimized neural network model.” J. Hydrol. Eng. 28 (1): 04022036. https://doi.org/10.1061/(ASCE)HE.1943-5584.0002219.
Gamboa, J. C. B. 2017. “Deep learning for time-series analysis.” Preprint, submitted January 7, 2017. https://arxiv.org/abs/1701.01887.
Gao, S., Y. Huang, S. Zhang, J. Han, G. Wang, M. Zhang, and Q. Lin. 2020. “Short-term runoff prediction with GRU and LSTM networks without requiring time step optimization during sample generation.” J. Hydrol. 589 (Oct): 125188. https://doi.org/10.1016/j.jhydrol.2020.125188.
Giglou, A. N., R. Nazari, F. Jazaei, M. Karimi, M. L. Museru, K. N. Opare, and M. R. Nikoo. 2024. “Future eco-hydrological dynamics: Urbanization and climate change effects in a changing landscape: A case study of Birmingham’s river basin.” J. Cleaner Prod. 447 (Apr): 141320. https://doi.org/10.1016/j.jclepro.2024.141320.
Girihagama, L., M. Naveed Khaliq, P. Lamontagne, J. Perdikaris, R. Roy, L. Sushama, and A. Elshorbagy. 2022. “Streamflow modelling and forecasting for Canadian watersheds using LSTM networks with attention mechanism.” Neural Comput. Appl. 34 (22): 19995–20015. https://doi.org/10.1007/s00521-022-07523-8.
Goodarzi, L., M. E. Banihabib, A. Roozbahani, and J. Dietrich. 2019. “Bayesian network model for flood forecasting based on atmospheric ensemble forecasts.” Nat. Hazards Earth Syst. Sci. 19 (11): 2513–2524. https://doi.org/10.5194/nhess-19-2513-2019.
Graves, A., and J. Schmidhuber. 2005. “Framewise phoneme classification with bidirectional LSTM and other neural network architectures.” Neural Networks 18 (5–6): 602–610. https://doi.org/10.1016/j.neunet.2005.06.042.
He, K., X. Zhang, S. Ren, and J. Sun. 2015. “Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification.” In Proc., IEEE Int. Conf. on Computer Vision, 1026–1034. New York: IEEE.
Hinton, G. E., and R. R. Salakhutdinov. 2006. “Reducing the dimensionality of data with neural networks.” Science 313 (5786): 504–507. https://doi.org/10.1126/science.1127647.
Hochreiter, S., and J. Schmidhuber. 1997. “Long short-term memory.” Neural Comput. 9 (8): 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735.
Hu, C., Q. Wu, H. Li, S. Jian, N. Li, and Z. Lou. 2018. “Deep learning with a long short-term memory networks approach for rainfall-runoff simulation.” Water 10 (11): 1543. https://doi.org/10.3390/w10111543.
Huang, P., P. Gu, Y. Kang, Y. Zhang, B. Duan, and C. Zhang. 2022. “The state of health estimation of lithium-ion batteries based on data-driven and model fusion method.” J. Cleaner Prod. 366 (Sep): 132742. https://doi.org/10.1016/j.jclepro.2022.132742.
Illinois Department of Natural Resources. 2001. “The DuPage River basin an inventory of the region’s resources.” Accessed February 1, 2001. https://dnr.illinois.gov/content/dam/soi/en/web/dnr/publications/documents/00000532.pdf.
Indra, G., and N. Duraipandian. 2023. “An improved flood forecasting system with cluster based visualization and analyzing using GK-ANFIS and CGDNN.” Expert Syst. Appl. 212 (Feb): 118747. https://doi.org/10.1016/j.eswa.2022.118747.
Isaaks, E. H., and R. M. Srivastava. 1989. Applied geostatistics. Oxford, UK: Oxford University Press.
Jain, S. K., P. Mani, S. K. Jain, P. Prakash, V. P. Singh, D. Tullos, S. Kumar, S. P. Agarwal, and A. P. Dimri. 2018. “A brief review of flood forecasting techniques and their applications.” Int. J. River Basin Manage. 16 (3): 329–344. https://doi.org/10.1080/15715124.2017.1411920.
Jain, Y. K., and S. K. Bhandare. 2013. “Min max normalization based data perturbation method for privacy protection.” Int. J. Comput. Commun. Technol. 233–238. https://doi.org/10.47893/IJCCT.2013.1201.
Jin, A., Q. Wang, H. Zhan, and R. Zhou. 2024. “Comparative performance assessment of physical-based and data-driven machine-learning models for simulating streamflow: A case study in three catchments across the US.” J. Hydrol. Eng. 29 (2): 05024004. https://doi.org/10.1061/JHYEFF.HEENG-6118.
Kao, I.-F., J.-Y. Liou, M.-H. Lee, and F.-J. Chang. 2021. “Fusing stacked autoencoder and long short-term memory for regional multistep-ahead flood inundation forecasts.” J. Hydrol. 598 (Jul): 126371. https://doi.org/10.1016/j.jhydrol.2021.126371.
Khajehali, M., H. R. Safavi, M. R. Nikoo, and M. Fooladi. 2024. “A fusion-based framework for daily flood forecasting in multiple-step-ahead and near-future under climate change scenarios: A case study of the Kan River, Iran.” Nat. Hazards 120: 8483–8504. https://doi.org/10.1007/s11069-024-06528-x.
Khanbilvardi, R., T. Lakhankar, N. Krakauer, R. Nazari, and A. Powell. 2013. “Remote sensing data and information for hydrological monitoring and modeling.” In Handbook of engineering hydrology, modeling climate changes and variability. Boca Raton, FL: CRC Press.
Konapala, G., S. C. Kao, S. L. Painter, and D. Lu. 2020. “Machine learning assisted hybrid models can improve streamflow simulation in diverse catchments across the conterminous US.” Environ. Res. Lett. 15 (10): 104022. https://doi.org/10.1088/1748-9326/aba927.
Kratzert, F., D. Klotz, G. Shalev, G. Klambauer, S. Hochreiter, and G. Nearing. 2019. “Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets.” Hydrol. Earth Syst. Sci. 23 (12): 5089–5110. https://doi.org/10.5194/hess-23-5089-2019.
Le, X. H., H. V. Ho, G. Lee, and S. Jung. 2019. “Application of long short-term memory (LSTM) neural network for flood forecasting.” Water 11 (7): 1387. https://doi.org/10.3390/w11071387.
Li, Y., and H. Hong. 2023. “Modelling flood susceptibility based on deep learning coupling with ensemble learning models.” J. Environ. Manage. 325 (Jan): 116450. https://doi.org/10.1016/j.jenvman.2022.116450.
Lin, K., H. Chen, Y. Zhou, S. Sheng, Y. Luo, S. Guo, and C.-Y. Xu. 2023. “Exploring a similarity search-based data-driven framework for multi-step-ahead flood forecasting.” Sci. Total Environ. 891 (Sep): 164494. https://doi.org/10.1016/j.scitotenv.2023.164494.
Lin, Y., K. Chen, X. Zhang, B. Tan, and Q. Lu. 2022. “Forecasting crude oil futures prices using BiLSTM-Attention-CNN model with Wavelet transform.” Appl. Soft Comput. 130 (Nov): 109723. https://doi.org/10.1016/j.asoc.2022.109723.
Luong, M. T., H. Pham, and C. D. Manning. 2015. “Effective approaches to attention-based neural machine translation.” Preprint, submitted August 17, 2015. https://arxiv.org/abs/1508.04025.
Luu, C., Q. D. Bui, R. Costache, L. T. Nguyen, T. T. Nguyen, T. Van Phong, H. Van Le, and B. T. Pham. 2021. “Flood-prone area mapping using machine learning techniques: A case study of Quang Binh province, Vietnam.” Nat. Hazards 108 (3): 3229–3251. https://doi.org/10.1007/s11069-021-04821-7.
Ma, K., D. He, S. Liu, X. Ji, Y. Li, and H. Jiang. 2024. “Novel time-lag informed deep learning framework for enhanced streamflow prediction and flood early warning in large-scale catchments.” J. Hydrol. 631 (Mar): 130841. https://doi.org/10.1016/j.jhydrol.2024.130841.
Majnooni, S., M. Fooladi, M. R. Nikoo, G. Al-Rawas, A. T. Haghighi, R. Nazari, M. Al-Wardy, and A. H. Gandomi. 2024. “Smarter water quality monitoring in reservoirs using interpretable deep learning models and feature importance analysis.” J. Water Process Eng. 60 (Apr): 105187. https://doi.org/10.1016/j.jwpe.2024.105187.
Majnooni, S., M. R. Nikoo, B. Nematollahi, M. Fooladi, N. Alamdari, G. Al-Rawas, and A. H. Gandomi. 2023. “Long-term precipitation prediction in different climate divisions of California using remotely sensed data and machine learning.” Hydrol. Sci. J. 68 (14): 1984–2008. https://doi.org/10.1080/02626667.2023.2248112.
Mohammed, K., A. S. Islam, G. T. Islam, L. Alfieri, M. J. U. Khan, S. K. Bala, and M. K. Das. 2018. “Future floods in Bangladesh under 1.5°C, 2°C, and 4°C global warming scenarios.” J. Hydrol. Eng. 23 (12): 04018050. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001705.
Motamedi, M. H., A. Iranmanesh, and R. Nazari. 2018. “Quantitative assessment of resilience for earthen structures using coupled plasticity-damage model.” J. Eng. Struct. 172 (Oct): 700–711. https://doi.org/10.1016/j.engstruct.2018.06.050.
Murphy, E. A., and J. B. Sharpe. 2013. Flood-inundation maps for the DuPage River from Plainfield to Shorewood, Illinois. Reston, VA: USGS.
Museru, M. L., R. Nazari, A. N. Giglou, K. Opare, and M. Karimi. 2024. “Advancing flood damage modeling for coastal Alabama residential properties: A multivariable machine learning approach.” Sci. Total Environ. 907 (Jan): 167872. https://doi.org/10.1016/j.scitotenv.2023.167872.
National Weather Service. 2023. “Timely provision of reliable weather, water, and climate.” Accessed January 25, 2023. https://www.weather.gov.
Nazari, R., M. G. R. Fahad, M. Karimi, and S. Eslamian. 2022a. “Continuous large-scale simulation models in flood studies.” In Flood handbook: Analysis and modeling, edited by S. Eslamian and F. Eslamian. 1st ed., 16. Boca Raton, FL: CRC Press.
Nazari, R., H. Vasiliadis, M. Karimi, M. G. R. Fahad, S. Simon, T. Zhang, Q. Sun, and R. Peters. 2022b. “Hydrodynamic study of the impact of extreme flooding events on wastewater treatment plants considering total water level.” Nat. Hazard. Rev. 23 (1): 04021056. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000531.
Nguyen, D.-M. T., T.-N. Do, S. Van Nghiem, J. Ghimire, K.-B. Dang, V.-T. Giang, K.-C. Vu, and V.-M. Pham. 2024. “Flood inundation assessment of UNESCO World Heritage Sites using remote sensing and spatial metrics in Hoi An City, Vietnam.” Ecol. Inf. 79 (Mar): 102427. https://doi.org/10.1016/j.ecoinf.2023.102427.
Patro, S. G. O. P. A. L., and K. K. Sahu. 2015. “Normalization: A preprocessing stage.” Preprint, submitted Mar 19, 2015. https://arxiv.org/abs/1503.06462.
Pontes, F. J., G. F. Amorim, P. P. Balestrassi, A. P. Paiva, and J. R. Ferreira. 2016. “Design of experiments and focused grid search for neural network parameter optimization.” Neurocomputing 186 (Apr): 22–34. https://doi.org/10.1016/j.neucom.2015.12.061.
Rahman, M., N. Chen, M. M. Islam, G. I. Mahmud, H. R. Pourghasemi, M. Alam, M. A. Rahim, M. A. Baig, A. Bhattacharjee, and A. Dewan. 2021. “Development of flood hazard map and emergency relief operation system using hydrodynamic modeling and machine learning algorithm.” J. Cleaner Prod. 311 (Aug): 127594. https://doi.org/10.1016/j.jclepro.2021.127594.
Rana, R. 2016. “Gated recurrent unit (GRU) for emotion classification from noisy speech.” Preprint, submitted December 13, 2016. https://arxiv.org/abs/1612.07778.
Schuster, M., and K. K. Paliwal. 1997. “Bidirectional recurrent neural networks.” IEEE Trans. Signal Process. 45 (11): 2673–2681. https://doi.org/10.1109/78.650093.
USACE. 2019. DuPage River, Illinois feasibility report and integrated environmental assessment. Chicago: USACE.
Vogel, R. M., C. Yaindl, and M. Walter. 2011. “Nonstationarity: Flood magnification and recurrence reduction factors in the United States.” JAWRA J. Am. Water Resour. Assoc. 47 (3): 464–474. https://doi.org/10.1111/j.1752-1688.2011.00541.x.
Wunsch, A., T. Liesch, and S. Broda. 2021. “Groundwater level forecasting with artificial neural networks: A comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX).” Hydrol. Earth Syst. Sci. 25 (3): 1671–1687. https://doi.org/10.5194/hess-25-1671-2021.
Yang, T., F. Sun, P. Gentine, W. Liu, H. Wang, J. Yin, M. Du, and C. Liu. 2019. “Evaluation and machine learning improvement of global hydrological model-based flood simulations.” Environ. Res. Lett. 14 (11): 114027. https://doi.org/10.1088/1748-9326/ab4d5e.
Yildirim, O. 2018. “A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification.” Comput. Biol. Med. 96 (May): 189–202. https://doi.org/10.1016/j.compbiomed.2018.03.016.
Young, C. C., W. C. Liu, and M. C. Wu. 2017. “A physically based and machine learning hybrid approach for accurate rainfall-runoff modeling during extreme typhoon events.” Appl. Soft Comput. 53 (Apr): 205–216. https://doi.org/10.1016/j.asoc.2016.12.052.
Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. “Understanding deep learning (still) requires rethinking generalization.” Commun. ACM 64 (3): 107–115. https://doi.org/10.1145/3446776.
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Journal of Hydrologic Engineering
Volume 29 • Issue 6 • December 2024
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© 2024 American Society of Civil Engineers.
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Received: Jan 27, 2024
Accepted: Jun 26, 2024
Published online: Sep 14, 2024
Published in print: Dec 1, 2024
Discussion open until: Feb 14, 2025
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