{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T20:23:58Z","timestamp":1776371038628,"version":"3.51.2"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T00:00:00Z","timestamp":1734048000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T00:00:00Z","timestamp":1734048000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42377046"],"award-info":[{"award-number":["42377046"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42377046"],"award-info":[{"award-number":["42377046"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51879088"],"award-info":[{"award-number":["51879088"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42277190"],"award-info":[{"award-number":["42277190"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["B200204002"],"award-info":[{"award-number":["B200204002"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK 20190023"],"award-info":[{"award-number":["BK 20190023"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Earth Sci Inform"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s12145-024-01569-z","type":"journal-article","created":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T04:32:24Z","timestamp":1734064344000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Variational mode decomposition coupled LSTM with encoder-decoder framework: an efficient method for daily streamflow forecasting"],"prefix":"10.1007","volume":"18","author":[{"given":"Jiadong","family":"Liu","sequence":"first","affiliation":[]},{"given":"Teng","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Chunhui","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Jie","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Yifan","family":"Xie","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,13]]},"reference":[{"key":"1569_CR1","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.jhydrol.2015.11.011","volume":"532","author":"Y Bai","year":"2016","unstructured":"Bai Y, Chen Z, Xie J et al (2016) Daily reservoir inflow forecasting using multiscale deep feature learning with hybrid models. J Hydrol 532:193\u2013206. https:\/\/doi.org\/10.1016\/j.jhydrol.2015.11.011","journal-title":"J Hydrol"},{"key":"1569_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2023.130421","volume":"628","author":"P Bhasme","year":"2024","unstructured":"Bhasme P, Bhatia U (2024) Improving the interpretability and predictive power of hydrological models: Applications for daily streamflow in managed and unmanaged catchments. J Hydrol 628:130421","journal-title":"J Hydrol"},{"key":"1569_CR3","doi-asserted-by":"publisher","unstructured":"Carvalho VR, Moraes MF, Braga AP et al (2020) Evaluating five different adaptive decomposition methods for eeg signal seizure detection and classification. Biomed Signal Process Control 62:102073. https:\/\/doi.org\/10.1016\/j.bspc.2020.102073, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1746809420302299","DOI":"10.1016\/j.bspc.2020.102073"},{"key":"1569_CR4","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1007\/s12145-013-0141-3","volume":"7","author":"A Danandeh Mehr","year":"2014","unstructured":"Danandeh Mehr A, Kahya E, Bagheri F et al (2014) Successive-station monthly streamflow prediction using neuro-wavelet technique. Earth Sci Inf 7:217\u2013229","journal-title":"Earth Sci Inf"},{"issue":"1","key":"1569_CR5","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1007\/s00704-022-03939-3","volume":"148","author":"A Danandeh Mehr","year":"2022","unstructured":"Danandeh Mehr A, Ghadimi S, Marttila H et al (2022) A new evolutionary time series model for streamflow forecasting in boreal lake-river systems. Theoretical Appl Climatol 148(1):255\u2013268","journal-title":"Theoretical Appl Climatol"},{"issue":"15","key":"1569_CR6","doi-asserted-by":"publisher","first-page":"2686","DOI":"10.3390\/w15152686","volume":"15","author":"A Danandeh Mehr","year":"2023","unstructured":"Danandeh Mehr A, Reihanifar M, Alee MM et al (2023) Vmd-gp: A new evolutionary explicit model for meteorological drought prediction at ungauged catchments. Water 15(15):2686","journal-title":"Water"},{"issue":"2","key":"1569_CR7","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1061\/(ASCE)0733-9496(1985)111:2(157)","volume":"111","author":"GN Day","year":"1985","unstructured":"Day GN (1985) Extended streamflow forecasting using nwsrfs. J Water Resour Plan Manag 111(2):157\u2013170","journal-title":"J Water Resour Plan Manag"},{"issue":"3","key":"1569_CR8","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1109\/tsp.2013.2288675","volume":"62","author":"K Dragomiretskiy","year":"2014","unstructured":"Dragomiretskiy K, Zosso D (2014) Variational mode decomposition. IEEE Trans Signal Process 62(3):531\u2013544. https:\/\/doi.org\/10.1109\/tsp.2013.2288675","journal-title":"IEEE Trans Signal Process"},{"key":"1569_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.jenvman.2020.111713","volume":"280","author":"RG Ferreira","year":"2021","unstructured":"Ferreira RG, da Silva DD, Elesbon AAA et al (2021) Machine learning models for streamflow regionalization in a tropical watershed. J Environ Manag 280:111713","journal-title":"J Environ Manag"},{"key":"1569_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.envsoft.2024.106029","volume":"176","author":"S Gao","year":"2024","unstructured":"Gao S, Zhang S, Huang Y et al (2024) A hydrological process-based neural network model for hourly runoff forecasting. Environ Modell Softw 176:106029","journal-title":"Environ Modell Softw"},{"key":"1569_CR11","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/s11269-015-1123-7","volume":"30","author":"O Gen\u00e7","year":"2016","unstructured":"Gen\u00e7 O, Da\u011f A (2016) A machine learning-based approach to predict the velocity profiles in small streams. Water Resour Manag 30:43\u201361","journal-title":"Water Resour Manag"},{"issue":"22","key":"1569_CR12","doi-asserted-by":"publisher","first-page":"19995","DOI":"10.1007\/s00521-022-07523-8","volume":"34","author":"L Girihagama","year":"2022","unstructured":"Girihagama L, Naveed Khaliq M, Lamontagne P et al (2022) Streamflow modelling and forecasting for canadian watersheds using lstm networks with attention mechanism. Neural Comput Appl 34(22):19995\u201320015. https:\/\/doi.org\/10.1007\/s00521-022-07523-8","journal-title":"Neural Comput Appl"},{"issue":"2","key":"1569_CR13","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1080\/02626667.2013.800944","volume":"59","author":"CA Guimar\u00e3es Santos","year":"2014","unstructured":"Guimar\u00e3es Santos CA, Silva GBLd (2014) Daily streamflow forecasting using a wavelet transform and artificial neural network hybrid models. Hydrological Sci J 59(2):312\u2013324","journal-title":"Hydrological Sci J"},{"key":"1569_CR14","doi-asserted-by":"publisher","unstructured":"He X, Luo J, Zuo G et al (2019) Daily runoff forecasting using a hybrid model based on variational mode decomposition and deep neural networks. Water Resour Manag 33(4):1571\u20131590. https:\/\/doi.org\/10.1007\/s11269-019-2183-x, https:\/\/doi.org\/10.1007\/s11269-019-2183-x","DOI":"10.1007\/s11269-019-2183-x"},{"issue":"8","key":"1569_CR15","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"key":"1569_CR16","doi-asserted-by":"publisher","first-page":"764","DOI":"10.1016\/j.jhydrol.2014.01.062","volume":"511","author":"S Huang","year":"2014","unstructured":"Huang S, Chang J, Huang Q et al (2014) Monthly streamflow prediction using modified emd-based support vector machine. J Hydrol 511:764\u2013775","journal-title":"J Hydrol"},{"issue":"3","key":"1569_CR17","doi-asserted-by":"publisher","first-page":"939","DOI":"10.1007\/s12145-020-00450-z","volume":"13","author":"D Hussain","year":"2020","unstructured":"Hussain D, Khan AA (2020) Machine learning techniques for monthly river flow forecasting of hunza river, pakistan. Earth Sci Inf 13(3):939\u2013949","journal-title":"Earth Sci Inf"},{"key":"1569_CR18","doi-asserted-by":"publisher","unstructured":"Jahangir MS, You J, Quilty J (2023) A quantile-based encoder-decoder framework for multi-step ahead runoff forecasting. J Hydrol 619:129269. https:\/\/doi.org\/10.1016\/j.jhydrol.2023.129269, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0022169423002111","DOI":"10.1016\/j.jhydrol.2023.129269"},{"key":"1569_CR19","doi-asserted-by":"crossref","unstructured":"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 47(13):e2020GL088229","DOI":"10.1029\/2020GL088229"},{"key":"1569_CR20","doi-asserted-by":"publisher","first-page":"102679","DOI":"10.1016\/j.scs.2020.102679","volume":"66","author":"EON Jnr","year":"2021","unstructured":"Jnr EON, Ziggah YY, Relvas S (2021) Hybrid ensemble intelligent model based on wavelet transform, swarm intelligence and artificial neural network for electricity demand forecasting. Sustain Cities and Soc 66:102679","journal-title":"Sustain Cities and Soc"},{"key":"1569_CR21","doi-asserted-by":"publisher","unstructured":"Kao IF, Zhou Y, Chang LC et al (2020) Exploring a long short-term memory based encoder-decoder framework for multi-step-ahead flood forecasting. J Hydrol 583. https:\/\/doi.org\/10.1016\/j.jhydrol.2020.124631","DOI":"10.1016\/j.jhydrol.2020.124631"},{"key":"1569_CR22","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.jhydrol.2013.08.030","volume":"502","author":"L Karthikeyan","year":"2013","unstructured":"Karthikeyan L, Kumar DN (2013) Predictability of nonstationary time series using wavelet and emd based arma models. J Hydrol 502:103\u2013119","journal-title":"J Hydrol"},{"issue":"2","key":"1569_CR23","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1061\/(ASCE)0887-3801(1994)8:2(201)","volume":"8","author":"N Karunanithi","year":"1994","unstructured":"Karunanithi N, Grenney WJ, Whitley D et al (1994) Neural networks for river flow prediction. J Comput Civil Eng 8(2):201\u2013220","journal-title":"J Comput Civil Eng"},{"key":"1569_CR24","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.jhydrol.2016.02.044","volume":"536","author":"KS Kasiviswanathan","year":"2016","unstructured":"Kasiviswanathan KS, He J, Sudheer KP et al (2016) Potential application of wavelet neural network ensemble to forecast streamflow for flood management. J Hydrol 536:161\u2013173. https:\/\/doi.org\/10.1016\/j.jhydrol.2016.02.044","journal-title":"J Hydrol"},{"issue":"2","key":"1569_CR25","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1007\/s11269-021-03051-7","volume":"36","author":"K Khosravi","year":"2022","unstructured":"Khosravi K, Golkarian A, Tiefenbacher JP (2022) Using optimized deep learning to predict daily streamflow: A comparison to common machine learning algorithms. Water Resour Manag 36(2):699\u2013716","journal-title":"Water Resour Manag"},{"issue":"1","key":"1569_CR26","first-page":"32","volume":"1","author":"EK Lafdani","year":"2013","unstructured":"Lafdani EK, Nia AM, Pahlavanravi A et al (2013) Research article daily rainfall-runoff prediction and simulation using ann, anfis and conceptual hydrological mike11\/nam models. Int J Eng Technol 1(1):32\u201350","journal-title":"Int J Eng Technol"},{"key":"1569_CR27","doi-asserted-by":"publisher","unstructured":"Li J, Yao X, Wang H et al (2019) Periodic impulses extraction based on improved adaptive vmd and sparse code shrinkage denoising and its application in rotating machinery fault diagnosis. Mech Syst Signal Process 126:568\u2013589. https:\/\/doi.org\/10.1016\/j.ymssp.2019.02.056, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0888327019301475","DOI":"10.1016\/j.ymssp.2019.02.056"},{"key":"1569_CR28","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1016\/j.jhydrol.2016.03.017","volume":"537","author":"AR Lima","year":"2016","unstructured":"Lima AR, Cannon AJ, Hsieh WW (2016) Forecasting daily streamflow using online sequential extreme learning machines. J Hydrol 537:431\u2013443","journal-title":"J Hydrol"},{"key":"1569_CR29","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.jhydrol.2013.11.021","volume":"509","author":"AK Lohani","year":"2014","unstructured":"Lohani AK, Goel NK, Bhatia KKS (2014) Improving real time flood forecasting using fuzzy inference system. J Hydrol 509:25\u201341. https:\/\/doi.org\/10.1016\/j.jhydrol.2013.11.021","journal-title":"J Hydrol"},{"key":"1569_CR30","doi-asserted-by":"crossref","unstructured":"Malik H, Feng J, Shao P, et\u00a0al (2024) Improving flood forecasting using time-distributed cnn-lstm model: a time-distributed spatiotemporal method. Earth Sci Inf 1\u201320","DOI":"10.1007\/s12145-024-01354-y"},{"key":"1569_CR31","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1016\/j.jhydrol.2018.11.015","volume":"568","author":"E Meng","year":"2019","unstructured":"Meng E, Huang S, Huang Q et al (2019) A robust method for non-stationary streamflow prediction based on improved emd-svm model. J Hydrol 568:462\u2013478. https:\/\/doi.org\/10.1016\/j.jhydrol.2018.11.015","journal-title":"J Hydrol"},{"key":"1569_CR32","doi-asserted-by":"publisher","first-page":"114726","DOI":"10.1016\/j.jenvman.2022.114726","volume":"309","author":"RL Miller","year":"2022","unstructured":"Miller RL (2022) Nonstationary streamflow effects on backwater flood management of the atchafalaya basin, usa. J Environ Manag 309:114726","journal-title":"J Environ Manag"},{"key":"1569_CR33","doi-asserted-by":"publisher","unstructured":"Milly PCD, Betancourt J, Falkenmark M et al (2008) Stationarity is dead: Whither water management? Science 319(5863):573\u2013574. https:\/\/doi.org\/10.1126\/science.1151915, https:\/\/www.science.org\/doi\/abs\/10.1126\/science.1151915, https:\/\/arxiv.org\/abs\/https:\/\/www.science.org\/doi\/pdf\/10.1126\/science.1151915, https:\/\/www.science.org\/doi\/pdf\/10.1126\/science.1151915","DOI":"10.1126\/science.1151915"},{"key":"1569_CR34","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1016\/j.renene.2017.10.111","volume":"118","author":"J Naik","year":"2018","unstructured":"Naik J, Dash S, Dash PK et al (2018) Short term wind power forecasting using hybrid variational mode decomposition and multi-kernel regularized pseudo inverse neural network. Renew Energy 118:180\u2013212. https:\/\/doi.org\/10.1016\/j.renene.2017.10.111","journal-title":"Renew Energy"},{"issue":"3\u20134","key":"1569_CR35","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.jhydrol.2011.06.015","volume":"406","author":"G Napolitano","year":"2011","unstructured":"Napolitano G, Serinaldi F, See L (2011) Impact of emd decomposition and random initialisation of weights in ann hindcasting of daily stream flow series: an empirical examination. J Hydrol 406(3\u20134):199\u2013214","journal-title":"J Hydrol"},{"issue":"5","key":"1569_CR36","doi-asserted-by":"publisher","first-page":"1947","DOI":"10.1088\/0305-4470\/37\/5\/B01","volume":"37","author":"J Ng","year":"2004","unstructured":"Ng J, Kingsbury NG (2004) The illustrated wavelet transform handbook: Introductory theory and applications in science, engineering, medicine and finance. J Phys A: Math General 37(5):1947. https:\/\/doi.org\/10.1088\/0305-4470\/37\/5\/B01","journal-title":"J Phys A: Math General"},{"key":"1569_CR37","doi-asserted-by":"publisher","unstructured":"Niu M, Hu Y, Sun S et al (2018) A novel hybrid decomposition-ensemble model based on vmd and hgwo for container throughput forecasting. Appl Math Modell 57:163\u2013178. https:\/\/doi.org\/10.1016\/j.apm.2018.01.014, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0307904X1830026X","DOI":"10.1016\/j.apm.2018.01.014"},{"key":"1569_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.optlastec.2024.111212","volume":"177","author":"Z Pan","year":"2024","unstructured":"Pan Z, Liu H, Wang L et al (2024) A novel method for improving salinity resolution of optical fiber sensor based on modified adaptive variational mode decomposition. Optics Laser Technol 177:111212","journal-title":"Optics Laser Technol"},{"issue":"13","key":"1569_CR39","doi-asserted-by":"publisher","first-page":"4113","DOI":"10.1007\/s11269-020-02659-5","volume":"34","author":"P Parisouj","year":"2020","unstructured":"Parisouj P, Mohebzadeh H, 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 Resour Manag 34(13):4113\u20134131","journal-title":"Water Resour Manag"},{"issue":"6","key":"1569_CR40","doi-asserted-by":"publisher","first-page":"2997","DOI":"10.5194\/hess-25-2997-2021","volume":"25","author":"LT Pham","year":"2021","unstructured":"Pham LT, Luo L, Finley A (2021) Evaluation of random forests for short-term daily streamflow forecasting in rainfall-and snowmelt-driven watersheds. Hydrol Earth Syst Sci 25(6):2997\u20133015","journal-title":"Hydrol Earth Syst Sci"},{"key":"1569_CR41","doi-asserted-by":"crossref","unstructured":"Sahoo BB, Jha R, Singh A et al (2019a) Application of support vector regression for modeling low flow time series. KSCE J Civil Eng 23:923\u2013934","DOI":"10.1007\/s12205-018-0128-1"},{"key":"1569_CR42","doi-asserted-by":"crossref","unstructured":"Sahoo BB, Jha R, Singh A et al (2019b) Long short-term memory (lstm) recurrent neural network for low-flow hydrological time series forecasting. Acta Geophysica 67(5):1471\u20131481","DOI":"10.1007\/s11600-019-00330-1"},{"key":"1569_CR43","doi-asserted-by":"publisher","DOI":"10.3389\/frwa.2021.652100","volume":"3","author":"A Sahraei","year":"2021","unstructured":"Sahraei A, Chamorro A, Kraft P et al (2021) Application of machine learning models to predict maximum event water fractions in streamflow. Front Water 3:652100","journal-title":"Front Water"},{"issue":"4","key":"1569_CR44","doi-asserted-by":"publisher","first-page":"3077","DOI":"10.1007\/s12145-023-01078-5","volume":"16","author":"C Sezen","year":"2023","unstructured":"Sezen C (2023) Pan evaporation forecasting using empirical and ensemble empirical mode decomposition (eemd) based data-driven models in the euphrates sub-basin, turkey. Earth Sci Inf 16(4):3077\u20133095","journal-title":"Earth Sci Inf"},{"key":"1569_CR45","doi-asserted-by":"publisher","unstructured":"Stojkovi\u0107 M, Kosti\u0107 S, Plav\u0161i\u0107 J et al (2017) A joint stochastic-deterministic approach for long-term and short-term modelling of monthly flow rates. J Hydrol 544:555\u2013566. https:\/\/doi.org\/10.1016\/j.jhydrol.2016.11.025, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0022169416307314","DOI":"10.1016\/j.jhydrol.2016.11.025"},{"key":"1569_CR46","doi-asserted-by":"crossref","unstructured":"Sun P, Wen Q, Zhang Q, et\u00a0al (2018) Nonstationarity-based evaluation of flood frequency and flood risk in the huai river basin, china. J Hydrol https:\/\/api.semanticscholar.org\/CorpusID:135222088","DOI":"10.1016\/j.jhydrol.2018.10.031"},{"key":"1569_CR47","unstructured":"Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. arXiv:1409.3215"},{"issue":"1","key":"1569_CR48","doi-asserted-by":"publisher","first-page":"139","DOI":"10.2166\/wcc.2023.487","volume":"15","author":"S Swagatika","year":"2024","unstructured":"Swagatika S, Paul JC, Sahoo BB et al (2024) Improving the forecasting accuracy of monthly runoff time series of the brahmani river in india using a hybrid deep learning model. J Water Climate Change 15(1):139\u2013156","journal-title":"J Water Climate Change"},{"key":"1569_CR49","doi-asserted-by":"crossref","unstructured":"Tao L, Cui Z, He Y, et\u00a0al (2024) An explainable multiscale lstm model with wavelet transform and layer-wise relevance propagation for daily streamflow forecasting. Sci Total Environ 172465","DOI":"10.1016\/j.scitotenv.2024.172465"},{"issue":"12","key":"1569_CR50","doi-asserted-by":"publisher","first-page":"4535","DOI":"10.1007\/s11269-022-03262-6","volume":"36","author":"L Wang","year":"2022","unstructured":"Wang L, Guo Y, Fan M (2022) Improving annual streamflow prediction by extracting information from high-frequency components of streamflow. Water Resour Manag 36(12):4535\u20134555","journal-title":"Water Resour Manag"},{"issue":"2","key":"1569_CR51","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1002\/hyp.8227","volume":"26","author":"S Wei","year":"2012","unstructured":"Wei S, Song J, Khan NI (2012) Simulating and predicting river discharge time series using a wavelet-neural network hybrid modelling approach. Hydrological Processes 26(2):281\u2013296","journal-title":"Hydrological Processes"},{"key":"1569_CR52","doi-asserted-by":"publisher","unstructured":"Xiang Z, Yan J, Demir I (2020) A rainfall-runoff model with lstm-based sequence-to-sequence learning. Water Resources Research 56(1):e2019WR025326. https:\/\/doi.org\/10.1029\/2019WR025326, https:\/\/agupubs.onlinelibrary.wiley.com\/doi\/abs\/10.1029\/2019WR025326, e2019WR025326 2019WR025326, https:\/\/arxiv.org\/abs\/https:\/\/agupubs.onlinelibrary.wiley.com\/doi\/pdf\/10.1029\/2019WR025326https:\/\/agupubs.onlinelibrary.wiley.com\/doi\/pdf\/10.1029\/2019WR025326","DOI":"10.1029\/2019WR025326"},{"issue":"1","key":"1569_CR53","doi-asserted-by":"publisher","first-page":"04021043","DOI":"10.1061\/(ASCE)HE.1943-5584.0002145","volume":"27","author":"P Xu","year":"2022","unstructured":"Xu P, Wang D, Wang Y et al (2022) A stepwise and dynamic c-vine copula-based approach for nonstationary monthly streamflow forecasts. J Hydrologic Eng 27(1):04021043","journal-title":"J Hydrologic Eng"},{"key":"1569_CR54","doi-asserted-by":"publisher","unstructured":"Yin H, Guo Z, Zhang X et al (2021a) Runoff predictions in ungauged basins using sequence-to-sequence models. Journal of Hydrology 603:126975. https:\/\/doi.org\/10.1016\/j.jhydrol.2021.126975, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0022169421010258","DOI":"10.1016\/j.jhydrol.2021.126975"},{"key":"1569_CR55","doi-asserted-by":"publisher","unstructured":"Yin H, Zhang X, Wang F et al (2021b) Rainfall-runoff modeling using lstm-based multi-state-vector sequence-to-sequence model. J Hydrol 598:126378. https:\/\/doi.org\/10.1016\/j.jhydrol.2021.126378, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S002216942100425X","DOI":"10.1016\/j.jhydrol.2021.126378"},{"key":"1569_CR56","doi-asserted-by":"crossref","unstructured":"Zhang L, Wang C, Hu W et al (2024) Dynamic real-time forecasting technique for reclaimed water volumes in urban river environmental management. Environ Res 248:118267","DOI":"10.1016\/j.envres.2024.118267"},{"key":"1569_CR57","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.jhydrol.2015.09.047","volume":"530","author":"X Zhang","year":"2015","unstructured":"Zhang X, Peng Y, Zhang C et al (2015) Are hybrid models integrated with data preprocessing techniques suitable for monthly streamflow forecasting? some experiment evidences. J Hydrol 530:137\u2013152. https:\/\/doi.org\/10.1016\/j.jhydrol.2015.09.047","journal-title":"J Hydrol"},{"key":"1569_CR58","doi-asserted-by":"publisher","DOI":"10.1016\/j.envres.2023.115259","volume":"221","author":"X Zhang","year":"2023","unstructured":"Zhang X, Chen X, Zheng G et al (2023) Improved prediction of chlorophyll-a concentrations in reservoirs by gru neural network based on particle swarm algorithm optimized variational modal decomposition. Environ Res 221:115259","journal-title":"Environ Res"},{"key":"1569_CR59","doi-asserted-by":"publisher","unstructured":"Zhang Y, Ragettli S, Molnar P et al (2022) Generalization of an encoder-decoder lstm model for flood prediction in ungauged catchments. J Hydrol 614. https:\/\/doi.org\/10.1016\/j.jhydrol.2022.128577","DOI":"10.1016\/j.jhydrol.2022.128577"},{"key":"1569_CR60","doi-asserted-by":"publisher","unstructured":"Zuo G, Luo J, Wang N et al (2020) Decomposition ensemble model based on variational mode decomposition and long short-term memory for streamflow forecasting. J Hydrol 585. https:\/\/doi.org\/10.1016\/j.jhydrol.2020.124776","DOI":"10.1016\/j.jhydrol.2020.124776"}],"container-title":["Earth Science Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-024-01569-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12145-024-01569-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-024-01569-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,26]],"date-time":"2025-04-26T08:04:08Z","timestamp":1745654648000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12145-024-01569-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,13]]},"references-count":60,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["1569"],"URL":"https:\/\/doi.org\/10.1007\/s12145-024-01569-z","relation":{},"ISSN":["1865-0473","1865-0481"],"issn-type":[{"value":"1865-0473","type":"print"},{"value":"1865-0481","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,13]]},"assertion":[{"value":"31 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"38"}}