{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T11:05:00Z","timestamp":1774868700035,"version":"3.50.1"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2021YFB3900605;2018YFC1508100"],"award-info":[{"award-number":["2021YFB3900605;2018YFC1508100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1007\/s10489-025-07074-0","type":"journal-article","created":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T12:12:41Z","timestamp":1769429561000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SFM_MF: A streamflow forecasting model based on model fusion for small-sample data in small and medium-sized rivers"],"prefix":"10.1007","volume":"56","author":[{"given":"Suhe","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2824-1880","authenticated-orcid":false,"given":"Yufeng","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Qingqing","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Qun","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Ziyang","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,26]]},"reference":[{"issue":"1","key":"7074_CR1","doi-asserted-by":"publisher","first-page":"1487","DOI":"10.1002\/wat2.1487","volume":"8","author":"Y Guo","year":"2021","unstructured":"Guo Y, Zhang Y, Zhang L, Wang Z (2021) Regionalization of hydrological modeling for predicting streamflow in ungauged catchments: A comprehensive review. Wiley Interdiscip Rev Water 8(1):1487","journal-title":"Wiley Interdiscip Rev Water"},{"issue":"5","key":"7074_CR2","doi-asserted-by":"publisher","first-page":"3599","DOI":"10.1002\/2015WR018247","volume":"52","author":"HE Beck","year":"2016","unstructured":"Beck HE, Dijk AI, De Roo A, Miralles DG, McVicar TR, Schellekens J, Bruijnzeel LA (2016) Global-scale regionalization of hydrologic model parameters. Water Resour Res 52(5):3599\u20133622","journal-title":"Water Resour Res"},{"issue":"5","key":"7074_CR3","doi-asserted-by":"publisher","first-page":"4364","DOI":"10.1029\/2018WR023254","volume":"55","author":"C Prieto","year":"2019","unstructured":"Prieto C, Le Vine N, Kavetski D, Garc\u00eda E, Medina R (2019) Flow prediction in ungauged catchments using probabilistic random forests regionalization and new statistical adequacy tests. Water Resour Res 55(5):4364\u20134392","journal-title":"Water Resour Res"},{"key":"7074_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5194\/adgeo-29-1-2011","volume":"29","author":"A Randrianasolo","year":"2011","unstructured":"Randrianasolo A, Ramos MH, Andr\u00e9assian V (2011) Hydrological ensemble forecasting at ungauged basins: using neighbour catchments for model setup and updating. Adv Geosci 29:1\u201311","journal-title":"Adv Geosci"},{"issue":"4","key":"7074_CR5","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1016\/0169-2070(89)90012-5","volume":"5","author":"RT Clemen","year":"1989","unstructured":"Clemen RT (1989) Combining forecasts: A review and annotated bibliography. Int J Forecast 5(4):559\u2013583","journal-title":"Int J Forecast"},{"issue":"3","key":"7074_CR6","doi-asserted-by":"publisher","first-page":"732","DOI":"10.3390\/forecast4030040","volume":"4","author":"P Cawood","year":"2022","unstructured":"Cawood P, Van Zyl T (2022) Evaluating state-of-the-art forecasting ensembles and meta-learning strategies for model fusion. Forecasting 4(3):732\u2013751","journal-title":"Forecasting"},{"key":"7074_CR7","unstructured":"KIMURA N, BABA D (2020) Convolutional neural network (CNN)-based transfer learning implemented to time-series flood forecast. In: Proceedings of the 22nd IAHR APD congress (Sapporo 2020)"},{"key":"7074_CR8","unstructured":"Oruche R, O\u2019Donncha F (2023) Attention-based domain adaptation forecasting of streamflow in data-sparse regions. arXiv preprint arXiv:2302.05386"},{"key":"7074_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2024.132409","volume":"649","author":"S Hou","year":"2025","unstructured":"Hou S, Wei J, Hou M, Xu J, Han L (2025) A hydrological knowledge-informed lstm model for monthly streamflow reconstruction using distributed data: Application to typical rivers across the tibetan plateau. J Hydrol 649:132409","journal-title":"J Hydrol"},{"key":"7074_CR10","unstructured":"Zaremba W, Sutskever I, Vinyals O (2014) Recurrent neural network regularization. arXiv preprint arXiv:1409.2329"},{"issue":"8","key":"7074_CR11","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","journal-title":"Neural Comput"},{"issue":"14","key":"7074_CR12","doi-asserted-by":"publisher","first-page":"16214","DOI":"10.1007\/s11227-022-04506-3","volume":"78","author":"Q Cheng","year":"2022","unstructured":"Cheng Q, Chen Y, Xiao Y, Yin H, Liu W (2022) A dual-stage attention-based bi-lstm network for multivariate time series prediction. J Supercomput 78(14):16214\u201316235","journal-title":"J Supercomput"},{"key":"7074_CR13","doi-asserted-by":"publisher","first-page":"36538","DOI":"10.1109\/ACCESS.2022.3163384","volume":"10","author":"AA Abdullah","year":"2022","unstructured":"Abdullah AA, Hassan MM, Mustafa YT (2022) A review on bayesian deep learning in healthcare: Applications and challenges. IEEe Access 10:36538\u201336562","journal-title":"IEEe Access"},{"key":"7074_CR14","doi-asserted-by":"crossref","unstructured":"Gatys LA, Ecker AS, Bethge M (2015) A neural algorithm of artistic style. arXiv preprint arXiv:1508.06576","DOI":"10.1167\/16.12.326"},{"key":"7074_CR15","doi-asserted-by":"crossref","unstructured":"Chou Jc, Yeh Cc, Lee Hy (2019) One-shot voice conversion by separating speaker and content representations with instance normalization. arXiv preprint arXiv:1904.05742","DOI":"10.21437\/Interspeech.2019-2663"},{"key":"7074_CR16","unstructured":"Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. Adv Neural Inf Process Syst 27"},{"key":"7074_CR17","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.jhydrol.2017.06.004","volume":"551","author":"S Han","year":"2017","unstructured":"Han S, Coulibaly P (2017) Bayesian flood forecasting methods: A review. J Hydrol 551:340\u2013351","journal-title":"J Hydrol"},{"key":"7074_CR18","doi-asserted-by":"crossref","unstructured":"Widiasari IR, Nugoho LE, Efendi R et\u00a0al (2018) Context-based hydrology time series data for a flood prediction model using lstm. In: 2018 5th international conference on information technology computer and electrical engineering (ICITACEE), IEEE, pp 385\u2013390","DOI":"10.1109\/ICITACEE.2018.8576900"},{"key":"7074_CR19","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1016\/j.jhydrol.2017.11.018","volume":"556","author":"D Zhang","year":"2018","unstructured":"Zhang D, Lindholm G, Ratnaweera H (2018) Use long short-term memory to enhance internet of things for combined sewer overflow monitoring. J Hydrol 556:409\u2013418","journal-title":"J Hydrol"},{"issue":"2","key":"7074_CR20","doi-asserted-by":"publisher","first-page":"937","DOI":"10.1007\/s11269-022-03414-8","volume":"37","author":"J Wu","year":"2023","unstructured":"Wu J, Wang Z, Hu Y, Tao S, Dong J (2023) Runoff forecasting using convolutional neural networks and optimized bi-directional long short-term memory. Water Resour Manage 37(2):937\u2013953","journal-title":"Water Resour Manage"},{"issue":"4","key":"7074_CR21","doi-asserted-by":"publisher","first-page":"12827","DOI":"10.1111\/jfr3.12827","volume":"15","author":"Q Cao","year":"2022","unstructured":"Cao Q, Zhang H, Zhu F, Hao Z, Yuan F (2022) Multi-step-ahead flood forecasting using an improved bilstm-s2s model. Journal of Flood Risk Management 15(4):12827","journal-title":"Journal of Flood Risk Management"},{"issue":"1","key":"7074_CR22","doi-asserted-by":"publisher","first-page":"396","DOI":"10.2166\/ws.2022.426","volume":"23","author":"X Zhang","year":"2023","unstructured":"Zhang X, Qiao W, Huang J, Shi J, Zhang M (2023) Flow prediction in the lower yellow river based on ceemdan-bilstm coupled model. Water Supply 23(1):396\u2013409","journal-title":"Water Supply"},{"issue":"4","key":"7074_CR23","doi-asserted-by":"publisher","first-page":"2437","DOI":"10.1007\/s40996-023-01053-6","volume":"47","author":"I Ayus","year":"2023","unstructured":"Ayus I, Natarajan N, Gupta D (2023) Prediction of water level using machine learning and deep learning techniques. Iranian Journal of Science and Technology Transactions of Civil Engineering 47(4):2437\u20132447","journal-title":"Iranian Journal of Science and Technology Transactions of Civil Engineering"},{"issue":"6","key":"7074_CR24","doi-asserted-by":"publisher","first-page":"1693","DOI":"10.2166\/hydro.2020.043","volume":"22","author":"K Li","year":"2020","unstructured":"Li K, Wan D, Zhu Y, Yao C, Yu Y, Si C, Ruan X (2020) The applicability of ascs_lstm_att model for water level prediction in small-and medium-sized basins in china. J Hydroinf 22(6):1693\u20131717","journal-title":"J Hydroinf"},{"key":"7074_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2025.133063","volume":"657","author":"W Ouyang","year":"2025","unstructured":"Ouyang W, Zhang C, Ye L, Zhang H, Meng Z, Chu J (2025) Dive into transfer-learning for daily rainfall-runoff modeling in data-limited basins. J Hydrol 657:133063","journal-title":"J Hydrol"},{"issue":"1","key":"7074_CR26","doi-asserted-by":"publisher","first-page":"2019","DOI":"10.1029\/2019WR025326","volume":"56","author":"Z Xiang","year":"2020","unstructured":"Xiang Z, Yan J, Demir I (2020) A rainfall-runoff model with lstm-based sequence-to-sequence learning. Water Resour Res 56(1):2019\u2013025326","journal-title":"Water Resour Res"},{"key":"7074_CR27","doi-asserted-by":"publisher","DOI":"10.1002\/9781119951001","volume-title":"Rainfall-runoff Modelling: The Primer","author":"KJ Beven","year":"2012","unstructured":"Beven KJ (2012) Rainfall-runoff Modelling: The Primer. John Wiley & Sons, Chichester West Sussex UK"},{"key":"7074_CR28","first-page":"21524","volume":"33","author":"M Balandat","year":"2020","unstructured":"Balandat M, Karrer B, Jiang D, Daulton S, Letham B, Wilson AG, Bakshy E (2020) Botorch: A framework for efficient monte-carlo bayesian optimization. Adv Neural Inf Process Syst 33:21524\u201321538","journal-title":"Adv Neural Inf Process Syst"},{"key":"7074_CR29","unstructured":"Dingman SL () Physical hydrology 3rd edn, p. 621. Waveland Press, Long Grove IL USA (2015). Chapter 12: Hydrologic Forecasting"},{"issue":"4","key":"7074_CR30","doi-asserted-by":"publisher","first-page":"1221","DOI":"10.2166\/nh.2017.076","volume":"49","author":"O Eray","year":"2018","unstructured":"Eray O, Mert C, Kisi O (2018) Comparison of multi-gene genetic programming and dynamic evolving neural-fuzzy inference system in modeling pan evaporation. Hydrol Res 49(4):1221\u20131233","journal-title":"Hydrol Res"},{"key":"7074_CR31","doi-asserted-by":"crossref","unstructured":"Nguyen TT, Huu QN, Li MJ (2015) Forecasting time series water levels on mekong river using machine learning models. In: 2015 7th international conference on knowledge and systems engineering (KSE), IEEE, pp 292\u2013297","DOI":"10.1109\/KSE.2015.53"},{"key":"7074_CR32","doi-asserted-by":"crossref","unstructured":"Fang J, Liu W, Chen L, Lauria S, Miron A, Liu X (2023) A survey of algorithms applications and trends for particle swarm optimization","DOI":"10.53941\/ijndi0201002"},{"issue":"12","key":"7074_CR33","doi-asserted-by":"publisher","first-page":"5089","DOI":"10.5194\/hess-23-5089-2019","volume":"23","author":"F Kratzert","year":"2019","unstructured":"Kratzert F, Klotz D, Shalev G, Klambauer G, Hochreiter S, Nearing G (2019) Towards learning universal regional and local hydrological behaviors via machine learning applied to large-sample datasets. Hydrol Earth Syst Sci 23(12):5089\u20135110","journal-title":"Hydrol Earth Syst Sci"},{"key":"7074_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2022.127781","volume":"609","author":"H Yin","year":"2022","unstructured":"Yin H, Guo Z, Zhang X, Chen J, Zhang Y (2022) Rr-former: Rainfall-runoff modeling based on transformer. J Hydrol 609:127781","journal-title":"J Hydrol"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-07074-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-025-07074-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-07074-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T10:15:59Z","timestamp":1774865759000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-025-07074-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":34,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["7074"],"URL":"https:\/\/doi.org\/10.1007\/s10489-025-07074-0","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1]]},"assertion":[{"value":"27 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 January 2026","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 have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and Informed Consent"}}],"article-number":"60"}}