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Research Article

A novel smoothing-based long short-term memory framework for short-to medium-range flood forecasting

ORCID Icon, , & ORCID Icon
Pages 488-506 | Received 30 Jun 2022, Accepted 13 Dec 2022, Published online: 24 Feb 2023

References

  • Adikari, K.E., et al., 2021. Evaluation of artificial intelligence models for flood and drought forecasting in arid and tropical regions. Environmental Modelling and Software, 144, 105136. doi:10.1016/j.envsoft.2021.105136.
  • Adikari, Y. and Yoshitani, J., 2009. Global trends in water-related disasters: an insight for policymakers. World Water Assessment Programme Side Publication Series, Insights. The United Nations, UNESCO. International Centre for Water Hazard and Risk Management (ICHARM).
  • Akhtar, M.K., et al., 2009. River flow forecasting with artificial neural networks using satellite observed precipitation pre-processed with flow length and travel time information: case study of the Ganges River basin. Hydrology and Earth System Sciences, 13 (9), 1607–1618. doi:10.5194/hess-13-1607-2009.
  • An, Y., et al., 2018. Discrete space reinforcement learning algorithm based on support vector machine classification. Pattern Recognition Letters, 111, 30–35. doi:10.1016/j.patrec.2018.04.012.
  • ASCE Task Committee on Definition of Criteria for Evaluation of Watershed Models of the Watershed Management Committee, Irrigation and Drainage Division, 1993. Criteria for evaluation of watershed models. Journal of Irrigation and Drainage Engineering, 119 (3), 429–442. doi:10.1061/(ASCE)0733-9437(1993)119:3(429).
  • Assem, H., et al., 2017. Urban water flow and water level prediction based on deep learning. In: Joint European conference on machine learning and knowledge discovery in databases, Cham: Springer, 317–329.
  • Azadeh, A., Asadzadeh, S.M., and Ghanbari, A., 2010. An adaptive network-based fuzzy inference system for short-term natural gas demand estimation: uncertain and complex environments. Energy Policy, 38 (3), 1529–1536. doi:10.1016/j.enpol.2009.11.036.
  • Bai, Y., et al., 2019. Short-term streamflow forecasting using the feature-enhanced regression model. Water Resources Management, 33 (14), 4783–4797. doi:10.1007/s11269-019-02399-1.
  • Bhowmik, S.R. and Durai, V.R., 2012. Development of multimodel ensemble based district level medium range rainfall forecast system for Indian region. Journal of Earth System Science, 121 (2), 273–285. doi:10.1007/s12040-012-0158-x.
  • Bisht, D.S., et al., 2020. Impact of climate change on streamflow regime of a large Indian river basin using a novel monthly hybrid bias correction technique and a conceptual modeling framework. Journal of Hydrology, 590, 125448. doi:10.1016/j.jhydrol.2020.125448
  • Brakenridge, G.R., 2018. Global active archive of large flood events [online]. Dartmouth Flood Observatory, University of Colorado. Available from: https://floodobservatory.colorado.edu/Archives/index.html [ Accessed 2 January 2022].
  • Central Water Commission, 2014. Mahanadi Basin. CWC and NRSC, p. 110.
  • Chandra, S., 2019. State wise flood damage statistics. Government of India, Central Water Commission, Technical Report Number, 3/38/2012-FFM/1067-1164.
  • Chen, X., et al., 2020. The importance of short lag-time in the runoff forecasting model based on long short-term memory. Journal of Hydrology, 589, 125359. doi:10.1016/j.jhydrol.2020.125359
  • Costabile, P. and Macchione, F., 2015. Enhancing river model set-up for 2-D dynamic flood modelling. Environmental Modelling and Software, 67, 89–107. doi:10.1016/j.envsoft.2015.01.009
  • Cui, Z., et al., 2022. Effective improvement of multi-step-ahead flood forecasting accuracy through encoder-decoder with an exogenous input structure. Journal of Hydrology, 609, 127764. doi:10.1016/j.jhydrol.2022.127764
  • Damavandi, H.G., et al., 2019. Accurate prediction of streamflow using long short-term memory network: a case study in the Brazos River Basin in Texas. International Journal of Environmental Science and Development, 10 (10), 294–300. doi:10.18178/ijesd.2019.10.10.1190.
  • Das, A.K. and Kaur, S., 2013. Performance of IMD multi-model ensemble and WRF (ARW) based sub-basin wise rainfall forecast for Mahanadi basin during flood season 2009 and 2010. Mausam, 64 (4), 625–644. doi:10.54302/mausam.v64i4.745.
  • Das, A.K. and Kaur, S., 2016. Performance of IMD multi-model ensemble and WRF (ARW) model for sub-basin wise rainfall forecast during monsoon 2012. Mausam, 67 (2), 323–332. doi:10.54302/mausam.v67i2.1298.
  • de la Fuente, A., Meruane, V., and Meruane, C., 2019. Hydrological early warning system based on a deep learning runoff model coupled with a meteorological forecast. Water, 11 (9), 1808. doi:10.3390/w11091808.
  • Deng, H., Chen, W., and Huang, G., 2022. Deep insight into daily runoff forecasting based on a CNN-LSTM model. Natural Hazards, 113 (3), 1675–1696.
  • Ding, Y., et al., 2020. Interpretable spatio-temporal attention LSTM model for flood forecasting. Neurocomputing, 403, 348–359. doi:10.1016/j.neucom.2020.04.110
  • Durai, V.R. and Bhardwaj, R., 2014. Forecasting quantitative rainfall over India using multi-model ensemble technique. Meteorology and Atmospheric Physics, 126 (1), 31–48. doi:10.1007/s00703-014-0334-4.
  • Fan, H., et al., 2020. Comparison of long short term memory networks and the hydrological model in runoff simulation. Water, 12 (1), 175. doi:10.3390/w12010175.
  • Fang, K., et al., 2017. Prolongation of SMAP to spatiotemporally seamless coverage of continental US using a deep learning neural network. Geophysical Research Letters, 44 (21), 11030–11039. doi:10.1002/2017GL075619.
  • Farfán, J.F., et al., 2020. A hybrid neural network-based technique to improve the flow forecasting of physical and data-driven models: methodology and case studies in Andean watersheds. Journal of Hydrology: Regional Studies, 27, 100652.
  • Feng, Z.K., et al., 2020. Monthly runoff time series prediction by variational mode decomposition and support vector machine based on quantum-behaved particle swarm optimization. Journal of Hydrology, 583, 124627. doi:10.1016/j.jhydrol.2020.124627
  • Feng, R., et al., 2021. Enhanced long short-term memory model for runoff prediction. Journal of Hydrologic Engineering, 26 (2), 04020063. doi:10.1061/(ASCE)HE.1943-5584.0002035.
  • Fonseca Alves, L.G., et al., 2022. Modelling and assessment of sustainable urban drainage systems in dense precarious settlements subject to flash floods. LHB, 108 (1), 1–11. doi:10.1080/27678490.2021.2016024.
  • Frame, J., et al., 2021. Post-processing the national water model with long short-term memory networks for streamflow predictions and model diagnostics. Journal of the American Water Resources Association, 57 (5), 1–48. doi:10.1111/1752-1688.12937.
  • Gao, S., et al., 2020. Short-term runoff prediction with GRU and LSTM networks without requiring time step optimization during sample generation. Journal of Hydrology, 589, 125188. doi:10.1016/j.jhydrol.2020.125188
  • Ghaeini, R., et al., 2018. DR-BiLSTM: dependent reading bidirectional LSTM for natural language inference. arXiv preprint arXiv:1802.05577.
  • Ghimire, S., et al., 2021. Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks. Scientific Reports, 11 (1), 1–26. doi:10.1038/s41598-021-96751-4.
  • Gong, W., et al., 2013. Estimating epistemic and aleatory uncertainties during hydrologic modeling: an information theoretic approach. Water Resources Research, 49 (4), 2253–2273. doi:10.1002/wrcr.20161.
  • Gupta, H.V., et al., 2009. Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modelling. Journal of Hydrology, 377 (1–2), 80–91. doi:10.1016/j.jhydrol.2009.08.003.
  • Halgamuge, M.N. and Nirmalathas, A., 2017. Analysis of large flood events: based on flood data during 1985-2016 in Australia and India. International Journal of Disaster Risk Reduction, 24, 1–11. doi:10.1016/j.ijdrr.2017.05.011
  • Hargreaves, G.L., Hargreaves, G.H., and Riley, J.P., 1985. Irrigation water requirements for Senegal River basin. Journal of Irrigation and Drainage Engineering, 111 (3), 265–275. doi:10.1061/(ASCE)0733-9437(1985)111:3(265).
  • He, X., et al., 2019. Daily runoff forecasting using a hybrid model based on variational mode decomposition and deep neural networks. Water Resources Management, 33 (4), 1571–1590. doi:10.1007/s11269-019-2183-x.
  • Hochreiter, S. and Schmidhuber, J., 1997. Long short-term memory. Neural Computation, 9 (8), 1735–1780. doi:10.1162/neco.1997.9.8.1735.
  • Hu, C., et al., 2018. Deep learning with a long short-term memory networks approach for rainfall-runoff simulation. Water, 10 (11), 1543. doi:10.3390/w10111543.
  • Hu, R., et al., 2019. Rapid spatio-temporal flood prediction and uncertainty quantification using a deep learning method. Journal of Hydrology, 575, 911–920. doi:10.1016/j.jhydrol.2019.05.087.
  • Hu, Y., et al., 2020. Stream-flow forecasting of small rivers based on LSTM. arXiv preprint arXiv:2001.05681.
  • Huang, H., et al., 2019. Combination of multiple data-driven models for long-term monthly runoff predictions based on Bayesian model averaging. Water Resources Management, 33 (9), 3321–3338. doi:10.1007/s11269-019-02305-9.
  • Jena, P.P., et al., 2014. Are recent frequent high floods in Mahanadi basin in eastern India due to increase in extreme rainfalls? Journal of Hydrology, 517, 847–862. doi:10.1016/j.jhydrol.2014.06.021.
  • Ji, Y., et al., 2021. Application of the decomposition-prediction-reconstruction framework to medium-and long-term runoff forecasting. Water Supply, 21 (2), 696–709. doi:10.2166/ws.2020.337.
  • Jimeno-Sáez, P., et al., 2018. A comparison of SWAT and ANN models for daily runoff simulation in different climatic zones of peninsular Spain. Water, 10 (2), 192. doi:10.3390/w10020192.
  • Kandel, I. and Castelli, M., 2020. The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset. ICT express, 6 (4), 312–315. doi:10.1016/j.icte.2020.04.010.
  • Kao, I.F., et al., 2020. Exploring a long short-term memory based encoder-decoder framework for multi-step-ahead flood forecasting. Journal of Hydrology, 583, 124631. doi:10.1016/j.jhydrol.2020.124631.
  • Karimi, H.S., et al., 2019. Comparison of learning-based wastewater flow prediction methodologies for smart sewer management. Journal of Hydrology, 577, 123977. doi:10.1016/j.jhydrol.2019.123977.
  • Khatun, A., et al., 2022a. Understanding the impacts of predecessor rain events on flood hazard in a changing climate. Hydrological Processes, 36 (2), e14500. doi:10.1002/hyp.14500.
  • Khatun, A., Sahoo, B., and Chatterjee, C., 2022b. Assessment of enhanced Kohonen self-organizing map, quantile mapping and copula-based bias-correction approaches for constructing basin-scale rainfall forecasts. Hydrological Sciences Journal, 67 (12), 1860–1875.
  • Kingma, D.P. and Ba, J., 2014. Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980.
  • Koenker, R., 2005. Quantile regression Cambridge University Press, USA ISBN-13: 978-0521608275.
  • Kratzert, F., et al., 2018. Rainfall-runoff modelling using long short-term memory (LSTM) networks. Hydrology and Earth System Sciences, 22 (11), 6005–6022. doi:10.5194/hess-22-6005-2018.
  • Kratzert, F., et al., 2019. Benchmarking a catchment-aware long short-term memory network (LSTM) for large-scale hydrological modeling. Hydrology and Earth System Sciences Discussions, 1–32.
  • Le, X.H., et al., 2019. Application of long short-term memory (LSTM) neural network for flood forecasting. Water, 11 (7), 1387. doi:10.3390/w11071387.
  • Lechowska, E., 2018. What determines flood risk perception? A review of factors of flood risk perception and relations between its basic elements. Natural Hazards, 94 (3), 1341–1366. doi:10.1007/s11069-018-3480-z.
  • Lian, Y., et al., 2021. Climate-driven model based on long short-term memory and Bayesian optimization for multi-day-ahead daily streamflow forecasting. Water Resources Management, 36 (1), 21–37. doi:10.1007/s11269-021-03002-2.
  • Liang, C., et al., 2018. Dongting lake water level forecast and its relationship with the Three Gorges dam based on a long short-term memory network. Water, 10 (10), 1389. doi:10.3390/w10101389.
  • Livieris, I.E., et al., 2020. An advanced deep learning model for short-term forecasting US natural gas price and movement. In: IFIP International Conference on Artificial Intelligence Applications and Innovations, Cham: Springer, 165–176.
  • Livieris, I.E., et al., 2021. Smoothing and stationarity enforcement framework for deep learning time-series forecasting. Neural Computing & Applications, 33 (20), 14021–14035. doi:10.1007/s00521-021-06043-1.
  • Madsen, H., 2000. Automatic calibration of a conceptual rainfall-runoff model using multiple objectives. Journal of Hydrology, 235 (3–4), 276–288. doi:10.1016/S0022-1694(00)00279-1.
  • Madsen, H., Wilson, G., and Ammentorp, H.C., 2002. Comparison of different automated strategies for calibration of rainfall-runoff models. Journal of Hydrology, 261 (1–4), 48–59. doi:10.1016/S0022-1694(01)00619-9.
  • Moghadam, S.V., et al., 2021. An efficient strategy for predicting river dissolved oxygen concentration: application of deep recurrent neural network model. Environmental Monitoring and Assessment, 193 (12), 1–18. doi:10.1007/s10661-021-09586-x.
  • Mosavi, A., Ozturk, P., and Chau, K.W., 2018. Flood prediction using machine learning models: literature review. Water, 10 (11), 1536. doi:10.3390/w10111536.
  • Najibi, N. and Devineni, N., 2018. Recent trends in the frequency and duration of global floods. Earth System Dynamics, 9 (2), 757–783. doi:10.5194/esd-9-757-2018.
  • Nanda, T., et al., 2016. A wavelet-based non-linear autoregressive with exogenous inputs (WNARX) dynamic neural network model for real-time flood forecasting using satellite-based rainfall products. Journal of Hydrology, 539, 57–73. doi:10.1016/j.jhydrol.2016.05.014.
  • Nanda, T., Sahoo, B., and Chatterjee, C., 2019. Enhancing real-time streamflow forecasts with wavelet-neural network based error-updating schemes and ECMWF meteorological predictions in variable infiltration capacity model. Journal of Hydrology, 575, 890–910. doi:10.1016/j.jhydrol.2019.05.051.
  • NERC, 1975. Flood studies report. Hydrological studies. Vol. 1. London: Natural Environment Research Council.
  • Ni, L., et al., 2020. Streamflow and rainfall forecasting by two long short-term memory-based models. Journal of Hydrology, 583, 124296. doi:10.1016/j.jhydrol.2019.124296.
  • Nourani, V., et al., 2014. Applications of hybrid wavelet-artificial intelligence models in hydrology: a review. Journal of Hydrology, 514, 358–377. doi:10.1016/j.jhydrol.2014.03.057.
  • Nourani, V., 2017. An emotional ANN (EANN) approach to modeling rainfall-runoff process. Journal of Hydrology, 544, 267–277. doi:10.1016/j.jhydrol.2016.11.033.
  • Nourani, V., et al., 2019. Emotional artificial neural networks (EANNs) for multi-step ahead prediction of monthly precipitation; case study: northern Cyprus. Theoretical and Applied Climatology, 138 (3), 1419–1434. doi:10.1007/s00704-019-02904-x.
  • Nourani, V., et al., 2022. Multi-step-ahead solar irradiance modeling employing multi-frequency deep learning models and climatic data. Applied Energy, 315, 119069. doi:10.1016/j.apenergy.2022.119069.
  • Nourani, V., Elkiran, G., and Abdullahi, J., 2020. Multi-step ahead modeling of reference evapotranspiration using a multi-model approach. Journal of Hydrology, 581, 124434. doi:10.1016/j.jhydrol.2019.124434.
  • Nuttall, A., 1981. Some windows with very good sidelobe behavior. IEEE Transactions on Acoustics, Speech, and Signal Processing, 29 (1), 84–91. doi:10.1109/TASSP.1981.1163506.
  • Oppenheim, A.V. and Schafer, R.W., 1999. Discrete-time signal processing. Pearson: Prentice-Hall, 494–495.
  • Pai, D.S., et al., 2014. Development of a new high spatial resolution (0.25°×0.25°) Long period (1901-2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region. Mausam, 65 (1), 1–18. doi:10.54302/mausam.v65i1.851.
  • Parisouj, P., Mohebzadeh, H., and Lee, T., 2020. Employing machine learning algorithms for streamflow prediction: a case study of four river basins with different climatic zones in the United States. Water Resources Management, 34 (13), 4113–4131. doi:10.1007/s11269-020-02659-5.
  • Peng, A., et al., 2022. Effects of training data on the learning performance of LSTM network for runoff simulation. Water Resources Management, 36, 2381–2394.
  • Qin, J., et al., 2019. Simulating and predicting of hydrological time series based on TensorFlow deep learning. Polish Journal of Environmental Studies, 28, 2.
  • Rahimzad, M., et al., 2021. Performance comparison of an LSTM-based deep learning model versus conventional machine learning algorithms for streamflow forecasting. Water Resources Management, 35 (12), 4167–4187. doi:10.1007/s11269-021-02937-w.
  • Ren, Y., et al., 2022. Mid-to long-term runoff prediction based on deep learning at different time scales in the upper Yangtze river basin. Water, 14 (11), 1692. doi:10.3390/w14111692.
  • Sahoo, B.B., et al., 2019. Long short-term memory (LSTM) recurrent neural network for low-flow hydrological time series forecasting. Acta Geophysica, 67 (5), 1471–1481. doi:10.1007/s11600-019-00330-1.
  • Saon, G. and Picheny, M., 2017. Recent advances in conversational speech recognition using convolutional and recurrent neural networks. IBM Journal of Research and Development, 61 (4/5), 1–10. doi:10.1147/JRD.2017.2701178.
  • Sehgal, V., Sahay, R.R., and Chatterjee, C., 2014. Effect of utilization of discrete wavelet components on flood forecasting performance of wavelet based ANFIS models. Water Resources Management, 28 (6), 1733–1749. doi:10.1007/s11269-014-0584-4.
  • Shafaei, M., et al., 2016. A wavelet-SARIMA-ANN hybrid model for precipitation forecasting. Journal of Water and Land Development, 28 (1), 27–36. doi:10.1515/jwld-2016-0003.
  • Sharma, S. and Mujumdar, P., 2017. Increasing frequency and spatial extent of concurrent meteorological droughts and heatwaves in India. Scientific Reports, 7 (1), 1–9. doi:10.1038/s41598-017-15896-3.
  • Shin, D., et al., 2017. 14.2 DNPU: an 8.1 TOPS/W reconfigurable CNN-RNN processor for general-purpose deep neural networks. In: 2017 IEEE International Solid-State Circuits Conference (ISSCC), IEEE, 240–241.
  • Shoaib, M., et al., 2018. A comparative study of various hybrid wavelet feedforward neural network models for runoff forecasting. Water Resources Management, 32 (1), 83–103. doi:10.1007/s11269-017-1796-1.
  • Shortridge, J.E., Guikema, S.D., and Zaitchik, B.F., 2016. Machine learning methods for empirical streamflow simulation: a comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds. Hydrology and Earth System Sciences, 20 (7), 2611–2628. doi:10.5194/hess-20-2611-2016.
  • Singh, J., et al., 2004. Hydrological modeling of the Iroquois river watershed using HSPF and SWAT. Journal of the American Water Resources Association, 41 (2), 343–360. doi:10.1111/j.1752-1688.2005.tb03740.x.
  • Srivastava, A.K., Rajeevan, M., and Kshirsagar, S.R., 2009. Development of a high resolution daily gridded temperature data set (1969-2005) for the Indian region. Atmospheric Science Letters, 10 (4), 249–254.
  • Sudheer, K.P., Gosain, A.K., and Ramasastri, K.S., 2002. A data-driven algorithm for constructing artificial neural network rainfall-runoff models. Hydrological Processes, 16 (6), 1325–1330. doi:10.1002/hyp.554.
  • Tan, Q.F., et al., 2018. An adaptive middle and long-term runoff forecast model using EEMD-ANN hybrid approach. Journal of Hydrology, 567, 767–780. doi:10.1016/j.jhydrol.2018.01.015.
  • Tao, Y., et al., 2016. A deep neural network modeling framework to reduce bias in satellite precipitation products. Journal of Hydrometeorology, 17 (3), 931–945. doi:10.1175/JHM-D-15-0075.1.
  • Taormina, R. and Chau, K.W., 2015. Data-driven input variable selection for rainfall-runoff modeling using binary-coded particle swarm optimization and extreme learning machines. Journal of Hydrology, 529, 1617–1632. doi:10.1016/j.jhydrol.2015.08.022.
  • Tennant, C., et al., 2020. The utility of information flow in formulating discharge forecast models: a case study from an arid snow-dominated catchment. Water Resources Research, 56 (8), e2019WR024908. doi:10.1029/2019WR024908.
  • Tian, Y., et al., 2018. LSTM-based traffic flow prediction with missing data. Neurocomputing, 318, 297–305. doi:10.1016/j.neucom.2018.08.067.
  • Tiwari, M.K. and Chatterjee, C., 2010a. Development of an accurate and reliable hourly flood forecasting model using wavelet-bootstrap-ANN (WBANN) hybrid approach. Journal of Hydrology, 394 (3–4), 458–470. doi:10.1016/j.jhydrol.2010.10.001.
  • Tiwari, M.K. and Chatterjee, C., 2010b. Uncertainty assessment and ensemble flood forecasting using bootstrap based artificial neural networks (BANNs). Journal of Hydrology, 382 (1–4), 20–33. doi:10.1016/j.jhydrol.2009.12.013.
  • Tiwari, M.K. and Chatterjee, C., 2011. A new wavelet-bootstrap-ANN hybrid model for daily discharge forecasting. Journal of Hydroinformatics, 13 (3), 500–519. doi:10.2166/hydro.2010.142.
  • Troin, M., et al., 2021. Generating ensemble streamflow forecasts: a review of methods and approaches over the past 40 years. Water Resources Research, 57 (7), e2020WR028392. doi:10.1029/2020WR028392.
  • Van, S.P., et al., 2020. Deep learning convolutional neural network in rainfall-runoff modelling. Journal of Hydroinformatics, 22 (3), 541–561. doi:10.2166/hydro.2020.095.
  • van Dijk, D., Teräsvirta, T., and Franses, P.H., 2002. Smooth transition autoregressive models-a survey of recent developments. Econometric Reviews, 21 (1), 1–47. doi:10.1081/ETC-120008723.
  • Vapnik, V., Golowich, S., and Smola, A., 1996. Support vector method for function approximation, regression estimation and signal processing. Advances in Neural Information Processing Systems, 9, 281–287.
  • Weerts, A.H., Winsemius, H.C., and Verkade, J.S., 2011. Estimation of predictive hydrological uncertainty using quantile regression: examples from the National Flood Forecasting System (England and Wales). Hydrology and Earth System Sciences, 15 (1), 255. doi:10.5194/hess-15-255-2011.
  • Wegayehu, E.B. and Muluneh, F.B., 2022. Short-term daily univariate streamflow forecasting using deep learning models. Advances in Meteorology, 2022, 1–22. doi:10.1155/2022/1860460.
  • Wu, H., et al., 2020. A spatiotemporal deep fusion model for merging satellite and gauge precipitation in China. Journal of Hydrology, 584, 124664. doi:10.1016/j.jhydrol.2020.124664.
  • Xiang, Z., Yan, J., and Demir, I., 2020. A rainfall-runoff model with LSTM-based sequence-to-sequence learning. Water Resources Research, 56 (1), e2019WR025326. doi:10.1029/2019WR025326.
  • Xie, Y., et al., 2019. Speech emotion classification using attention-based LSTM. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 27 (11), 1675–1685. doi:10.1109/TASLP.2019.2925934.
  • Xie, K., et al., 2021. Physics-guided deep learning for rainfall-runoff modeling by considering extreme events and monotonic relationships. Journal of Hydrology, 603, 127043. doi:10.1016/j.jhydrol.2021.127043.
  • Xu, W., et al., 2020. Using long short-term memory networks for river flow prediction. Hydrology Research, 51 (6), 1358–1376. doi:10.2166/nh.2020.026.
  • Xu, Y., et al., 2022. Research on particle swarm optimization in LSTM neural networks for rainfall-runoff simulation. Journal of Hydrology, 608, 127553. doi:10.1016/j.jhydrol.2022.127553.
  • Yin, H., et al., 2022. Rainfall-runoff modeling using long short-term memory based step-sequence framework. Journal of Hydrology, 610, 127901. doi:10.1016/j.jhydrol.2022.127901.
  • Young, C.C., Liu, W.C., and Wu, M.C., 2017. A physically based and machine learning hybrid approach for accurate rainfall-runoff modeling during extreme typhoon events. Applied Soft Computing, 53, 205–216. doi:10.1016/j.asoc.2016.12.052.
  • Yue, Z., et al., 2020. Mid-to long-term runoff prediction by combining the deep belief network and partial least-squares regression. Journal of Hydroinformatics, 22 (5), 1283–1305. doi:10.2166/hydro.2020.022.
  • Yunpeng, L., et al., 2017. Multi-step ahead time series forecasting for different data patterns based on LSTM recurrent neural network. In: 2017 14th Web Information Systems And Applications Conference (WISA), IEEE, 305–310.
  • Zhang, D., Lindholm, G., and Ratnaweera, H., 2018. Use long short-term memory to enhance Internet of things for combined sewer overflow monitoring. Journal of Hydrology, 556, 409–418. doi:10.1016/j.jhydrol.2017.11.018.
  • Zhou, Y., 2020. Real-time probabilistic forecasting of river water quality under data missing situation: deep learning plus post-processing techniques. Journal of Hydrology, 589, 125164. doi:10.1016/j.jhydrol.2020.125164.
  • Zhou, Y., Guo, S., and Chang, F.J., 2019. Explore an evolutionary recurrent ANFIS for modelling multi-step-ahead flood forecasts. Journal of Hydrology, 570, 343–355. doi:10.1016/j.jhydrol.2018.12.040.

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