Abstract
Deep learning has gained its prevalence in conducting spatiotemporal runoff simulations. For spatial pattern recognition, a majority of previous spatiotemporal models preferred to receive image-like inputs and to learn their hidden features by convolutional neural networks (CNN) in Euclidean space. However, dealing with geospatially non-Euclidean topology-like structures has not been received sufficient attention in hydrological deep learning modeling and needs a further exploration. The purpose of this paper is to propose an innovative spatiotemporal graph convolution-based model for daily runoff prediction in a river network with non-Euclidean topological structure, named hierarchical static-dynamic spatiotemporal prediction model (HSDSTM). The river network is regarded as a graph, and its runoff stations and topological relationships are represented as nodes and edges of the graph. Both static and dynamic graphs are generated to model the spatial confluence process and to capture fixed attributes and varied correlations of topological connectivity, respectively. Instead of focusing on a single station, the proposed model is applied to simultaneously predict the multi-step ahead daily runoff of the multiple stations of a river network in the Mississippi River Basin (MRB). The results show that the HSDSTM significantly outperforms the baseline models with a higher accuracy at a significance level of 1%, which is examined by Diebold-Mariano test. The separate effectiveness of the static and dynamic graphs is compared and it indicates that the dynamic correlations provide more valuable information to enhance the precision than the static one. Not only does the proposed model achieve a lower error, but also it strengthens the physical meaning, since the static graphs reproduce the static confluence properties and the dynamic graph quantitatively describes the varied contributions of the upstream inflow. In conclusion, the research proves the potential of the proposed spatiotemporal model to accuracy improvements in a topological river network with the physical background.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Adnan RM, Liang Z, Trajkovic S, Zounemat-Kermani M, Li B, Kisi O (2019) Daily streamflow prediction using optimally pruned extreme learning machine. J Hydrol 577:123981. https://doi.org/10.1016/j.jhydrol.2019.123981
Ahmed AAM, Deo RC, Feng Q, Ghahramani A, Raj N, Yin Z, Yang L (2022) Hybrid deep learning method for a week-ahead evapotranspiration forecasting. Stoch Env Res Risk 36(3):831–849. https://doi.org/10.1007/s00477-021-02078-x
Bai S, Kolter JZ, Koltun V (2018) An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271
Betterle A, Schirmer M, Botter G (2019) Flow dynamics at the continental scale: streamflow correlation and hydrological similarity. Hydrol Process 33(4):627–646. https://doi.org/10.1002/hyp.13350
Bian H, Wang Q, Xu G, Zhao X (2022) Research on short-term load forecasting based on accumulated temperature effect and improved temporal convolutional network. Energy Rep 8:1482–1491. https://doi.org/10.1016/j.egyr.2022.03.196
Chen Z, Zhu Z, Jiang H, Sun S (2020) Estimating daily reference evapotranspiration based on limited meteorological data using deep learning and classical machine learning methods. J Hydrol 591:125286. https://doi.org/10.1016/j.jhydrol.2020.125286
Chen Y, Ding F, Zhai L (2022) Multi-scale temporal features extraction based graph convolutional network with attention for multivariate time series prediction. Expert Syst Appl 200:117011. https://doi.org/10.1016/j.eswa.2022.117011
Choi J, Lee J, Kim S (2022) Utilization of the long short-term memory network for predicting streamflow in ungauged basins in Korea. Ecol Eng 182:106699. https://doi.org/10.1016/j.ecoleng.2022.106699
Dauphin YN, Fan A, Auli M, Grangier D. (2017). Language modeling with gated convolutional networks In: International conference on machine learning. PMLR, p 933–941
Diebold FX, Mariano RS (2002) Comparing predictive accuracy. J Bus Econ Stat 20(1):134–144
Dong L, Zhang J (2021) Predicting polycyclic aromatic hydrocarbons in surface water by a multiscale feature extraction-based deep learning approach. Sci Total Environ 799:149509. https://doi.org/10.1016/j.scitotenv.2021.149509
Du B, Huang S, Guo J, Tang H, Wang L, Zhou S (2022) Interval forecasting for urban water demand using PSO optimized KDE distribution and LSTM neural networks. Appl Soft Comput 122:108875. https://doi.org/10.1016/j.asoc.2022.108875
Fang K, Sivakumar B, Woldemeskel FM (2017) Complex networks, community structure, and catchment classification in a large-scale river basin. J Hydrol 545:478–493. https://doi.org/10.1016/j.jhydrol.2016.11.056
Granata F, Di Nunno F (2021) Forecasting evapotranspiration in different climates using ensembles of recurrent neural networks. Agr Water Manage 255:107040. https://doi.org/10.1016/j.agwat.2021.107040
Greff K, Srivastava RK, Koutnik J, Steunebrink BR, Schmidhuber J (2017) LSTM: a search space odyssey. IEEE T Neur Net Lear 28(10):2222–2232. https://doi.org/10.1109/TNNLS.2016.2582924
Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, Liu T, Wang X, Wang G, Cai J, Chen T (2018) Recent advances in convolutional neural networks. Pattern Recogn 77:354–377. https://doi.org/10.1016/j.patcog.2017.10.013
Gu H, Xu Y, Ma D, Xie J, Liu L, Bai Z (2020) A surrogate model for the variable infiltration capacity model using deep learning artificial neural network. J Hydrol 588:125019. https://doi.org/10.1016/j.jhydrol.2020.125019
Guo D, Johnson F, Marshall L (2018) Assessing the potential robustness of conceptual rainfall-runoff models under a changing climate. Water Resour Res 54(7):5030–5049. https://doi.org/10.1029/2018WR022636
Guo Y, Yu X, Xu Y, Chen H, Gu H, Xie J (2021) AI-based techniques for multi-step streamflow forecasts: application for multi-objective reservoir operation optimization and performance assessment. Hydrol Earth Syst Sc 25(11):5951–5979. https://doi.org/10.5194/hess-25-5951-2021
He R, Zhang L, Chew AWZ (2022a) Modeling and predicting rainfall time series using seasonal-trend decomposition and machine learning. Knowl-Based Syst 251:109125. https://doi.org/10.1016/j.knosys.2022.109125
He S, Guo S, Zhang J, Liu Z, Cui Z, Zhang Y, Zheng Y (2022b) Multi-objective operation of cascade reservoirs based on short-term ensemble streamflow prediction. J Hydrol 610:127936. https://doi.org/10.1016/j.jhydrol.2022.127936
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition In: Proceedings of the IEEE conference on computer vision and pattern recognition, p 770–778
Huang Y, Ying JJ, Tseng VS (2021) Spatio-attention embedded recurrent neural network for air quality prediction. Knowl-Based Syst 233:107416. https://doi.org/10.1016/j.knosys.2021.107416
Jiang S, Zheng Y, Babovic V, Tian Y, Han F (2018) A computer vision-based approach to fusing spatiotemporal data for hydrological modeling. J Hydrol 567:25–40. https://doi.org/10.1016/j.jhydrol.2018.09.064
Jiang S, Zheng Y, Solomatine D (2020) Improving AI system awareness of geoscience knowledge: symbiotic integration of physical approaches and deep learning. Geophys Res Lett. https://doi.org/10.1029/2020GL088229
Jiang P, Liu Z, Niu X, Zhang L (2021) A combined forecasting system based on statistical method, artificial neural networks, and deep learning methods for short-term wind speed forecasting. Energy 217:119361. https://doi.org/10.1016/j.energy.2020.119361
Khodayar M, Wang J (2019) Spatio-temporal graph deep neural network for short-term wind speed forecasting. IEEE T Sustain Energy 10(2):670–681. https://doi.org/10.1109/TSTE.2018.2844102
Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907
Kiranyaz S, Avci O, Abdeljaber O, Ince T, Gabbouj M, Inman DJ (2021) 1D convolutional neural networks and applications: a survey. Mech Syst Signal Pr 151:107398. https://doi.org/10.1016/j.ymssp.2020.107398
Kong Z, Tang B, Deng L, Liu W, Han Y (2020) Condition monitoring of wind turbines based on spatio-temporal fusion of SCADA data by convolutional neural networks and gated recurrent units. Renew Energy 146:760–768. https://doi.org/10.1016/j.renene.2019.07.033
Kumar A, Ramsankaran R, Brocca L, Muñoz-Arriola F (2021) A simple machine learning approach to model real-time streamflow using satellite inputs: demonstration in a data scarce catchment. J Hydrol 595:126046. https://doi.org/10.1016/j.jhydrol.2021.126046
Li Y, Yu R, Shahabi C, Liu Y (2017) Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926
Liu J, Yuan X, Zeng J, Jiao Y, Li Y, Zhong L, Yao L (2022a) Ensemble streamflow forecasting over a cascade reservoir catchment with integrated hydrometeorological modeling and machine learning. Hydrol Earth Syst Sci 26(2):265–278. https://doi.org/10.5194/hess-26-265-2022
Liu Y, Hou G, Huang F, Qin H, Wang B, Yi L (2022b) Directed graph deep neural network for multi-step daily streamflow forecasting. J Hydrol 607:127515. https://doi.org/10.1016/j.jhydrol.2022.127515
Liu Y, Racah E, Correa J, Khosrowshahi A, Lavers D, Kunkel K, Wehner M, Collins W (2016) Application of deep convolutional neural networks for detecting extreme weather in climate datasets. arXiv preprint arXiv:1605.01156
Mu L, Zheng F, Tao R, Zhang Q, Kapelan Z (2020) Hourly and daily urban water demand predictions using a long short-term memory based model. J Water Res Plan Manag 146(9):5020017. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001276
Ni L, Wang D, Singh VP, Wu J, Wang Y, Tao Y, Zhang J (2020) Streamflow and rainfall forecasting by two long short-term memory-based models. J Hydrol 583:124296. https://doi.org/10.1016/j.jhydrol.2019.124296
Nourani V, Hosseini Baghanam A, Adamowski J, Kisi O (2014) Applications of hybrid wavelet–artificial intelligence models in hydrology: a review. J Hydrol 514:358–377. https://doi.org/10.1016/j.jhydrol.2014.03.057
Pan B, Hsu K, AghaKouchak A, Sorooshian S (2019) Improving precipitation estimation using convolutional neural network. Water Resour Res 55(3):2301–2321. https://doi.org/10.1029/2018WR024090
Patil S, Stieglitz M (2011) Hydrologic similarity among catchments under variable flow conditions. Hydrol Earth Syst Sci 15(3):989–997. https://doi.org/10.5194/hess-15-989-2011
Qi T, Li G, Chen L, Xue Y (2022) ADGCN: an asynchronous dilation graph convolutional network for traffic flow prediction. IEEE Internet Things 9(5):4001–4014. https://doi.org/10.1109/JIOT.2021.3102238
Qiu R, Wang Y, Rhoads B, Wang D, Qiu W, Tao Y, Wu J (2021) River water temperature forecasting using a deep learning method. J Hydrol 595:126016. https://doi.org/10.1016/j.jhydrol.2021.126016
Rahmani F, Lawson K, Ouyang W, Appling A, Oliver S, Shen C (2021) Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data. Environ Res Lett. https://doi.org/10.1088/1748-9326/abd501
Rahmani F, Shen C, Oliver S, Lawson K, Appling A (2021) Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins. Hydrol Process. https://doi.org/10.1002/hyp.14400
Reis GB, Da Silva DD, Fernandes Filho EI, Moreira MC, Veloso GV, Fraga MDS, Pinheiro SAR (2021) Effect of environmental covariable selection in the hydrological modeling using machine learning models to predict daily streamflow. J Environ Manag 290:112625. https://doi.org/10.1016/j.jenvman.2021.112625
Sadeghi Tabas S, Samadi S (2022) Variational Bayesian dropout with a Gaussian prior for recurrent neural networks application in rainfall–runoff modeling. Environ Res Lett 17(6):65012. https://doi.org/10.1088/1748-9326/ac7247
Sang Y (2013) A review on the applications of wavelet transform in hydrology time series analysis. Atmos Res 122:8–15. https://doi.org/10.1016/j.atmosres.2012.11.003
Saraiva SV, Carvalho FDO, Santos CAG, Barreto LC, Freire PKDM (2021) Daily streamflow forecasting in Sobradinho Reservoir using machine learning models coupled with wavelet transform and bootstrapping. Appl Soft Comput 102:107081. https://doi.org/10.1016/j.asoc.2021.107081
Sarkar R, Dutta S, Dubey AK (2015) An insight into the runoff generation processes in wet sub-tropics: field evidences from a vegetated hillslope plot. Catena 128:31–43. https://doi.org/10.1016/j.catena.2015.01.006
Sen S, Srivastava P, Dane JH, Yoo KH, Shaw JN (2010) Spatial-temporal variability and hydrologic connectivity of runoff generation areas in a North Alabama pasture-implications for phosphorus transport. Hydrol Process 24(3):342–356. https://doi.org/10.1002/hyp.7502
Shang Z, Zhang B, Li W, Qian S, Zhang J (2022) Machine remaining life prediction based on multi-layer self-attention and temporal convolution network. Complex Intell Syst 8(2):1409–1424. https://doi.org/10.1007/s40747-021-00606-4
Shuman DI, Narang SK, Frossard P, Ortega A, Vandergheynst P (2013) The emerging field of signal processing on graphs: extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Proc Mag 30(3):83–98. https://doi.org/10.1109/MSP.2012.2235192
Skøien JO, Blöschl G (2007) Spatiotemporal topological kriging of runoff time series. Water Resour Res. https://doi.org/10.1029/2006WR005760
Song CM (2022) Data construction methodology for convolution neural network based daily runoff prediction and assessment of its applicability. J Hydrol 605:127324. https://doi.org/10.1016/j.jhydrol.2021.127324
Song YH, Chung E, Shahid S (2022) Differences in extremes and uncertainties in future runoff simulations using SWAT and LSTM for SSP scenarios. Sci Total Environ 838:156162. https://doi.org/10.1016/j.scitotenv.2022.156162
Srivastav RK, Sudheer KP, Chaubey I (2007) A simplified approach to quantifying predictive and parametric uncertainty in artificial neural network hydrologic models. Water Resour Res. https://doi.org/10.1029/2006WR005352
Suh YJ, Diefendorf AF, Bowen GJ, Cotton JM, Ju S (2019) Plant wax integration and transport from the Mississippi River Basin to the Gulf of Mexico inferred from GIS-enabled isoscapes and mixing models. Geochim Cosmochim Ac 257:131–149. https://doi.org/10.1016/j.gca.2019.04.022
Sun AY, Scanlon BR, Zhang Z, Walling D, Bhanja SN, Mukherjee A, Zhong Z (2019) Combining physically based modeling and deep learning for fusing GRACE satellite data: can we learn from mismatch? Water Resour Res 55(2):1179–1195. https://doi.org/10.1029/2018WR023333
Sun AY, Jiang P, Mudunuru MK, Chen X (2021) Explore spatio-temporal learning of large sample hydrology using graph neural networks. Water Resour Res. https://doi.org/10.1029/2021WR030394
Sun AY, Jiang P, Yang Z, Xie Y, Chen X (2022) A graph neural network (GNN) approach to basin-scale river network learning: the role of physics-based connectivity and data fusion. Hydrol Earth Syst Sci 26(19):5163–5184. https://doi.org/10.5194/hess-26-5163-2022
Tamaddun KA, Kalra A, Ahmad S (2019) Spatiotemporal variation in the continental US streamflow in association with large-scale climate signals across multiple spectral bands. Water Resour Manag 33(6):1947–1968. https://doi.org/10.1007/s11269-019-02217-8
Tao H, Gemmer M, Bai Y, Su B, Mao W (2011) Trends of streamflow in the Tarim river basin during the past 50years: human impact or climate change? J Hydrol 400(1–2):1–9. https://doi.org/10.1016/j.jhydrol.2011.01.016
Tongal H, Sivakumar B (2019) Entropy analysis for spatiotemporal variability of seasonal, low, and high streamflows. Stoch Env Res Risk Assess 33(1):303–320. https://doi.org/10.1007/s00477-018-1615-0
Veličković P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2017) Graph attention networks. arXiv preprint arXiv:1710.10903
Weyn JA, Durran DR, Caruana R (2019) Can machines learn to predict weather? Using deep learning to predict gridded 500-hPa geopotential height from historical weather data. J Adv Model Earth Syst 11(8):2680–2693. https://doi.org/10.1029/2019MS001705
Wu Z, Pan S, Long G, Jiang J, Zhang C (2019) Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121
Xiao X, Jin Z, Wang S, Xu J, Peng Z, Wang R, Shao W, Hui Y (2022) A dual-path dynamic directed graph convolutional network for air quality prediction. Sci Total Environ 827:154298. https://doi.org/10.1016/j.scitotenv.2022.154298
Xie K, Liu P, Zhang J, Han D, Wang G, Shen C (2021) Physics-guided deep learning for rainfall-runoff modeling by considering extreme events and monotonic relationships. J Hydrol 603:127043. https://doi.org/10.1016/j.jhydrol.2021.127043
Yang S, Yang D, Chen J, Zhao B (2019) Real-time reservoir operation using recurrent neural networks and inflow forecast from a distributed hydrological model. J Hydrol 579:124229. https://doi.org/10.1016/j.jhydrol.2019.124229
Yang S, Yang D, Chen J, Santisirisomboon J, Lu W, Zhao B (2020) A physical process and machine learning combined hydrological model for daily streamflow simulations of large watersheds with limited observation data. J Hydrol 590:125206. https://doi.org/10.1016/j.jhydrol.2020.125206
Yang HF, Yang SL, Li BC, Wang YP, Wang JZ, Zhang ZL, Xu KH, Huang YG, Shi BW, Zhang WX (2021) Different fates of the Yangtze and Mississippi deltaic wetlands under similar riverine sediment decline and sea-level rise. Geomorphology 381:107646. https://doi.org/10.1016/j.geomorph.2021.107646
Yaseen ZM, Sulaiman SO, Deo RC, Chau K (2019) An enhanced extreme learning machine model for river flow forecasting: state-of-the-art, practical applications in water resource engineering area and future research direction. J Hydrol 569:387–408. https://doi.org/10.1016/j.jhydrol.2018.11.069
Young C, Liu W, Wu M (2017) A physically based and machine learning hybrid approach for accurate rainfall-runoff modeling during extreme typhoon events. Appl Soft Comput 53:205–216. https://doi.org/10.1016/j.asoc.2016.12.052
Yu X, Shi S, Xu L (2021) A spatial–temporal graph attention network approach for air temperature forecasting. Appl Soft Comput 113:107888. https://doi.org/10.1016/j.asoc.2021.107888
Yu F, Koltun V (2015) Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122
Zanfei A, Brentan BM, Menapace A, Righetti M, Herrera M (2022) Graph convolutional recurrent neural networks for water demand forecasting. Water Resour Res. https://doi.org/10.1029/2022WR032299
Zhang X, Peng Y, Zhang C, Wang B (2015) Are hybrid models integrated with data preprocessing techniques suitable for monthly streamflow forecasting? Some experiment evidences. J Hydrol 530:137–152. https://doi.org/10.1016/j.jhydrol.2015.09.047
Zhang Z, Zhang Q, Singh VP, Shi P (2018) River flow modelling: comparison of performance and evaluation of uncertainty using data-driven models and conceptual hydrological model. Stoch Env Res Risk Assess 32(9):2667–2682. https://doi.org/10.1007/s00477-018-1536-y
Zhang C, Yu JJQ, Liu Y (2019) Spatial-temporal graph attention networks: a deep learning approach for traffic forecasting. IEEE Access 7:166246–166256. https://doi.org/10.1109/ACCESS.2019.2953888
Zhang X, Deng L, Wu B, Gao S, Xiao Y (2022a) Low-impact optimal operation of a cascade sluice-reservoir system for water-society-ecology trade-offs. Water Resour Manag. https://doi.org/10.1007/s11269-022-03345-4
Zhang Y, Li C, Jiang Y, Sun L, Zhao R, Yan K, Wang W (2022b) Accurate prediction of water quality in urban drainage network with integrated EMD-LSTM model. J Clean Prod 354:131724. https://doi.org/10.1016/j.jclepro.2022.131724
Zhao Q, Zhu Y, Shu K, Wan D, Yu Y, Zhou X, Liu H (2020) Joint spatial and temporal modeling for hydrological prediction. IEEE Access 8:78492–78503. https://doi.org/10.1109/ACCESS.2020.2990181
Zhao X, Lv H, Lv S, Sang Y, Wei Y, Zhu X (2021) Enhancing robustness of monthly streamflow forecasting model using gated recurrent unit based on improved grey wolf optimizer. J Hydrol 601:126607. https://doi.org/10.1016/j.jhydrol.2021.126607
Acknowledgements
We appreciate the editors and anonymous reviewers for enhancing our paper.
Funding
This work was financially supported by the National Natural Science Foundation of China, grant numbers: Nos.U21A2002 and 52009092.
Author information
Authors and Affiliations
Contributions
LD: Conceptualization, Methodology, Formal analysis, Writing–original draft; XZ: Conceptualization, Supervision, Writing–review & editing, Funding acquisition; ST: Writing–review & editing; YZ: Data curation, Formal analysis; KW: Investigation, Validation; JL: Investigation, Validation.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Conflict of interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Deng, L., Zhang, X., Tao, S. et al. A spatiotemporal graph convolution-based model for daily runoff prediction in a river network with non-Euclidean topological structure. Stoch Environ Res Risk Assess 37, 1457–1478 (2023). https://doi.org/10.1007/s00477-022-02352-6
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00477-022-02352-6