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Engineering >> 2023, Volume 24, Issue 5 doi: 10.1016/j.eng.2021.12.022

Enhanced LSTM Model for Daily Runoff Prediction in the Upper Huai River Basin, China

a School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
b Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China
c School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
d State Key Laboratory of Hydrology–Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China
e Xinyang Hydrology and Water Resources Survey Bureau, Xinyang 450003, China

Received: 2021-06-08 Revised: 2021-12-10 Accepted: 2021-12-17 Available online: 2022-04-28

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Abstract

Runoff prediction is of great significance to flood defense. However, due to the complexity and randomness of the runoff process, it is hard to predict daily runoff accurately, especially for peak runoff. To address this issue, this study proposes an enhanced long short-term memory (LSTM) model for runoff prediction, where novel loss functions are introduced and feature extractors are integrated. Two loss functions (peak error tanh (PET), peak error swish (PES)) are designed to strengthen the importance of the peak runoff's prediction while weakening the weight of the normal runoff's prediction. The feature extractor consisting of three LSTM networks is established for each meteorological station, aiming to extract temporal features of the input data at each station. Taking the upper Huai River Basin in China as a case study, daily runoff from 1960–2016 is predicted using the enhanced LSTM model. Results indicate that the enhanced LSTM model performed well, achieving Nash–Sutcliffe efficiency (NSE) coefficient ranging from 0.917–0.924 during the validation period (November 2005–December 2016), outperforming the widely used lumped hydrological models (Australian Water Balance Model (AWBM), Sacramento, SimHyd and Tank Model) and the data-driven models (artificial neural network (ANN), support vector regression (SVR), and gated recurrent units (GRU)). The enhanced LSTM with PES as loss function performed best on extreme runoff prediction with a mean NSE for floods of 0.873. In addition, precipitation at a meteorological station with a higher altitude contributes more runoff prediction than the closest stations. This study provides an effective tool for daily runoff prediction, which will benefit the basin's flood defense and water security management.

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References

[ 1 ] Barendrecht MH, Sairam N, Cumiskey L, Metin AD, Holz F, Priest SJ, et al. Needed: a systems approach to improve flood risk mitigation through private precautionary measures. Water Secur 2020;11:100080. link1

[ 2 ] Moore RJ, Bell VA, Jones DA. Forecasting for flood warning. C R Geosci 2005;337(1‒2):203‒17.

[ 3 ] Williams BS, Das A, Johnston P, Luo B, Lindenschmidt KE. Measuring the skill of an operational ice jam flood forecasting system. Int J Disast Risk Re 2021;52:102001. link1

[ 4 ] Mizutori M, Guha-Sapir D. Economic losses, poverty and disasters 1998‒2017. Report. Brussels: the United Nations Office for Disaster Risk Reduction, Geneva & Center for Research on the Epidemiology of Disasters; 2018.

[ 5 ] Kundzewicz ZW, Takeuchi K. Flood protection and management: quo vadimus? Hydrol Sci J 1999;44(3):417‒32. link1

[ 6 ] Wang L, Li X, Ma C, Bai Y. Improving the prediction accuracy of monthly streamflow using a data-driven model based on a double-processing strategy. J Hydrol: X 2019;573:733‒45. link1

[ 7 ] Zhang H, Yang Q, Shao J, Wang G. Dynamic streamflow simulation via online gradient-boosted regression tree. J Hydrol Eng 2019;24(10):04019041. link1

[ 8 ] Clark MP, Bierkens MFP, Samaniego L, Woods RA, Uijlenhoet R, Bennett KE, et al. The evolution of process-based hydrologic models: historical challenges and the collective quest for physical realism. Hydrol Earth Syst Sci 2017;21:3427‒40. link1

[ 9 ] Beven KJ, Kirkby MJ, Freer JE, Lamb R. A history of TOPMODEL. Hydrol Earth Syst Sci 2021;25(2):527‒49. link1

[10] Wang J, Zhang J, Wang G, Song X, Yang X, Wang Y. Ensemble flood simulation for the typical catchment in humid climatic zone by using multiple hydrological models. Proc IAHS 2020;383:213‒22. link1

[11] Beven KJ, Kirkby MJ, Schofield N, Tagg AF. Testing a physically-based flood forecasting model (TOPMODEL) for three U.K. catchments. J Hydrol: X 1984;69(1‒4):119‒43.

[12] Arnold JG, Srinivasan R, Muttiah RS, Williams JR. Large area hydrologic modeling and assessment part I: model development1. JAWRA 1998;34(1):73‒89. link1

[13] Jayakrishnan R, Srinivasan R, Santhi C, Arnold JG. Advances in the application of the SWAT model for water resources management. Hydrol Processes 2005;19(3):749‒62. link1

[14] Kim NW, Chung IM, Won YS, Arnold JG. Development and application of the integrated SWAT‒MODFLOW model. J Hydrol: X 2008;356(1‒2):1‒16.

[15] Fontaine TA, Cruickshank TS, Arnold JG, Hotchkiss RH. Development of a snowfall-snowmelt routine for mountainous terrain for the soil water assessment tool (SWAT). J Hydrol: X 2002;262(1‒4):209‒23.

[16] Azimi S, Dariane AB, Modanesi S, Bauer-Marschallinger B, Bindlish R, Wagner W, et al. Assimilation of Sentinel 1 and SMAP-based satellite soil moisture retrievals into SWAT hydrological model: the impact of satellite revisit time and product spatial resolution on flood simulations in small basins. J Hydrol:X 2020;581:124367. link1

[17] Rajib A, Liu Z, Merwade V, Tavakoly AA, Follum ML. Towards a large-scale locally relevant flood inundation modeling framework using SWAT and LISFLOOD-FP. J Hydrol:X 2020;581:124406. link1

[18] Boughton W. The Australian water balance model. Environ Model Softw 2004;19(10):943‒56. link1

[19] Boughton W, Chiew F. Estimating runoff in ungauged catchments from rainfall, PET and the AWBM model. Environ Model Softw 2007;22(4):476‒87. link1

[20] Mosavi A, Ozturk P, Chau K. Flood prediction using machine learning models: literature review. Water 2018;10(11):1536. link1

[21] Wang W, PHAJMVGelder, Vrijling JK, Ma J. Forecasting daily streamflow using hybrid ANN models. J Hydrol: X 2006;324(1‒4):383‒99.

[22] Huang S, Chang J, Huang Q, Chen Y. Monthly streamflow prediction using modified EMD-based support vector machine. J Hydrol: X 2014;511:764‒75. link1

[23] Mukerji A, Chatterjee C, Raghuwanshi NS. Flood Forecasting Using ANN, neuro-fuzzy, and neuro-GA models. J Hydrol Eng 2009;14(6):647‒52. link1

[24] Arora A, Arabameri A, Pandey M, Siddiqui MA, Shukla UK, Bui DT, et al. Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India. Sci Total Environ 2021;750:141565. link1

[25] Kasiviswanathan KS, He J, Sudheer KP, Tay JH. Potential application of wavelet neural network ensemble to forecast streamflow for flood management. J Hydrol: X 2016;536:161‒73. link1

[26] Zeng C, Wang Q, Wang W, Li T, Shwartz L. Online inference for time-varying temporal dependency discovery from time series. In: JoshiJ, KarypisG, LiuL, HuX, AkR, XiaY, alet, editors. 2016 IEEE International Conference on Big Data (Big Data); 2016 Dec 5-8; Washington, DC, USA. Washington: Institute of Electrical and Electronics Engineers (IEEE); 2016. p. 1281‒90. link1

[27] Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput 1997;9(8):1735‒80. link1

[28] Kratzert F, Klotz D, Shalev G, Klambauer G, Hochreiter S, Nearing G. Benchmarking a catchment-aware long short-term memory network (LSTM) for large-scale hydrological modeling. Hydrol Earth Syst Sci 2019:1‒32. link1

[29] Le XH, Ho HV, Lee G, Jung S. Application of long short-term memory (LSTM) neural network for flood forecasting. Water 2019;11(7):1387. link1

[30] wu Y, Liu Z, Xu W, Feng J, Palaiahnakote S, Lu T. Context-aware attention LSTM network for flood prediction. In: 24th International Conference on Pattern Recognition (ICPR); 2018 Aug 20‒24; Beijing, China. Washington: Institute of Electrical and Electronics Engineers (IEEE); 2018. p. 1301‒6. link1

[31] Zhang J, Zhu Y, Zhang X, Ye M, Yang J. Developing a long short-term memory (LSTM) based model for predicting water table depth in agricultural areas. J Hydrol: X 2018;561:918‒29. link1

[32] Kao IF, Zhou Y, Chang LC, Chang FJ. Exploring a long short-term memory based encoder-decoder framework for multi-step-ahead flood forecasting. J Hydrol: X 2020;583:124631. link1

[33] Kratzert F, Klotz D, Brenner C, Schulz K, Herrnegger M. Rainfall‒runoff modelling using long short-term memory (LSTM) networks. Hydrol Earth Syst Sci 2018;22(11):6005‒22. link1

[34] Davis N, Raina G, Jagannathan K. LSTM-based anomaly detection: detection rules from extreme value theory. In: Oliveira PM, Novais P, Reis LP, editors. Proceedings, Part I of 19th EPIA Conference on Artificial Intelligence; 2019 Sep 3‒6; Vila Real, Portugal. Cham: Springer Nature Switzerland; 2019. p. 572‒83. link1

[35] Chen Z, Yu H, Geng Ya, Li Q, Zhang Y. EvaNet: an extreme value attention network for long-term air quality prediction. In: 2020 IEEE International Conference on Big Data (Big Data); 2020 Dec 10‒13; Taking Place Virtually. Washington: Institute of Electrical and Electronics Engineers (IEEE); 2020. p. 4545‒52. link1

[36] Ding D, Zhang M, Pan X, Yang M, He X. Modeling extreme events in time series prediction. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining; 2019 Aug 4‍‒‍8; Anchorage, AK, USA. New York: Association for Computing Machinery(ACM); 2019. p. 1114‒22. link1

[37] Zhang YL, You WJ. Social vulnerability to floods: a case study of Huaihe River Basin. Nat Hazards 2014;71(3):2113‒25. link1

[38] Liu Z, Martina MLV, Todini E. Flood forecasting using a fully distributed model: application of the TOPKAPI model to the Upper Xixian Catchment. Hydrol Earth Syst Sci 2005;9:347‒64. link1

[39] Lv N, Liang X, Chen C, Zhou Y, Li J, Wei H, et al. A long short-term memory cyclic model with mutual information for hydrology forecasting: a case study in the Xixian Basin. Adv Water Resour 2020;141:103622. link1

[40] Li M, Chu R, Islam ARMT, Shen S. Characteristics of surface evapotranspiration and its response to climate and land use and land cover in the Huai River Basin of Eastern China. Environ Sci Pollut R 2021;28(1):683‒99. link1

[41] Shi P, Ma X, Hou Y, Li Q, Zhang Z, Qu S, et al. Effects of land-use and climate change on hydrological processes in the Upstream of Huai River, China. Water Resour Manage 2013;27(5):1263‒78.

[42] Kendall MG. Rank correlation methods. Oxford: Griffin; 1948.

[43] Liu M, Huang Y, Li Z, Tong B, Liu Z, Sun M, et al. The applicability of LSTM-KNN model for real-time flood forecasting in different climate zones in China. Water 2020;12(2):440. link1

[44] Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature 1986;323(6088):533‒536. link1

[45] Emin Orhan A, Pitkow X. Skip connections eliminate singularities. 2017. arXiv:1701.09175.

[46] Wenling S, Kihyuk S, Diogo A, Honglak L. Understanding and improving convolutional neural networks via concatenated rectified linear units. In: BalcanMF, WeinbergerKQ, editors. Proceedings of the 33rd International Conference on International Conference on Machine Learning; 2016 June 19‒24; New York, NY, USA. Cambridge: MIT Press; 2016. p. 2217‒25.

[47] Ramachandran P, Zoph B, Le QV. Searching for activation functions. 2017. arXiv:1710.05941.

[48] Zhang G, Eddy Patuwo B. Hu MY. Forecasting with artificial neural networks: the state of the art. Int J Forecast 1998;14(1):35‒62. link1

[49] Chung J, Gulcehre C, Cho K, Bengio Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. 2014. arXiv:1412.3555.

[50] Burnash RJ, Ferral RL, McGuire RA. A generalized streamflow simulation system: conceptual modeling for digital computers. Sacramento: US Department of Commerce, National Weather Service, and State of California, Department of Water Resources; 1973.

[51] Chiew FHS, Peel MC, Western AW. Application and testing of the simple rainfall-runoff model SIMHYD. In: Singh VP, Frevert DK, editors. Mathematical Models of Small Watershed Hydrology and Applications. Colorado: Water Resources Publications, LLC; 2002. p. 335‒67.

[52] Sugawara M, Sentā KBKG. Tank model with snow component. Report. Tsukuba: National Research Center for Disaster Prevention, Science and Technology Agency; 1984.

[53] Song JH, Her Y, Park J, Lee KD, Kang MS. Simulink implementation of a hydrologic model: a tank model case study. Water 2017;9(9):639. link1

[54] Bennett ND, Croke BFW, Guariso G, Guillaume JHA, Hamilton SH, Jakeman AJ, et al. Characterising performance of environmental models. Environ Model Softw 2013;40:1‒20. link1

[55] Ding Y, Zhu Y, Feng J, Zhang P, Cheng Z. Interpretable spatio-temporal attention LSTM model for flood forecasting. Neurocomputing 2020;403:348‒59. link1

[56] China Meteorological Administration (CMA). Blue paper of climate change 2019 in China. Beijing: China Meteorological Administration (CMA); 2019.

[57] Han S, Xu D, Wang S. Decreasing potential evaporation trends in China from 1956 to 2005: accelerated in regions with significant agricultural influence? Agric Meteorol 2012;154‒155:44‒56.

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