{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T09:18:33Z","timestamp":1783070313224,"version":"3.54.6"},"reference-count":43,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012165","name":"Key Technologies Research and Development Program","doi-asserted-by":"publisher","award":["2022YFC3202701"],"award-info":[{"award-number":["2022YFC3202701"]}],"id":[{"id":"10.13039\/501100012165","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Environmental Modelling &amp; Software"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.envsoft.2026.107041","type":"journal-article","created":{"date-parts":[[2026,5,24]],"date-time":"2026-05-24T00:41:15Z","timestamp":1779583275000},"page":"107041","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Improving extreme streamflow prediction by integrating CEEMDAN, autoencoders, and attention-enhanced BiLSTM optimized via GSA-PSO"],"prefix":"10.1016","volume":"203","author":[{"given":"Jian","family":"Sha","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinglong","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Man","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhong-Liang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9888-2165","authenticated-orcid":false,"given":"Xue","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.envsoft.2026.107041_bib1","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2020.125717","article-title":"A hybrid of Random Forest and Deep Auto-Encoder with support vector regression methods for accuracy improvement and uncertainty reduction of long-term streamflow prediction","volume":"597","author":"Abbasi","year":"2021","journal-title":"J. Hydrol."},{"key":"10.1016\/j.envsoft.2026.107041_bib2","doi-asserted-by":"crossref","first-page":"56589","DOI":"10.1109\/ACCESS.2021.3071400","article-title":"Attention-Based Bi-Directional long-short term memory network for earthquake prediction","volume":"9","author":"Banna","year":"2021","journal-title":"IEEE Access"},{"issue":"1","key":"10.1016\/j.envsoft.2026.107041_bib3","doi-asserted-by":"crossref","DOI":"10.1029\/2020WR028544","article-title":"Time-Varying sensitivity analysis reveals relationships between watershed climate and variations in annual parameter importance in regions with strong interannual variability","volume":"57","author":"Basijokaite","year":"2021","journal-title":"Water Resour. Res."},{"key":"10.1016\/j.envsoft.2026.107041_bib4","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1016\/j.energy.2017.03.054","article-title":"Hybrid gravitational search algorithm-particle swarm optimization with time varying acceleration coefficients for large scale CHPED problem","volume":"126","author":"Beigvand","year":"2017","journal-title":"Energy"},{"key":"10.1016\/j.envsoft.2026.107041_bib5","article-title":"Algorithms for hyper-parameter optimization","volume":"24","author":"Bergstra","year":"2011","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"1","key":"10.1016\/j.envsoft.2026.107041_bib6","doi-asserted-by":"crossref","first-page":"1919","DOI":"10.1038\/s41467-025-56573-8","article-title":"Artificial intelligence for modeling and understanding extreme weather and climate events","volume":"16","author":"Camps-Valls","year":"2025","journal-title":"Nat. Commun."},{"issue":"1","key":"10.1016\/j.envsoft.2026.107041_bib7","doi-asserted-by":"crossref","first-page":"13","DOI":"10.2166\/hydro.2017.078","article-title":"Review and comparison of performance indices for automatic model induction","volume":"21","author":"Chadalawada","year":"2017","journal-title":"J. Hydroinform."},{"issue":"13","key":"10.1016\/j.envsoft.2026.107041_bib8","doi-asserted-by":"crossref","first-page":"2397","DOI":"10.5194\/hess-27-2397-2023","article-title":"When best is the enemy of good \u2013 critical evaluation of performance criteria in hydrological models","volume":"27","author":"Cinkus","year":"2023","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"10.1016\/j.envsoft.2026.107041_bib9","doi-asserted-by":"crossref","DOI":"10.1016\/j.jenvman.2020.111713","article-title":"Machine learning models for streamflow regionalization in a tropical watershed","volume":"280","author":"Ferreira","year":"2021","journal-title":"J. Environ. Manag."},{"issue":"4","key":"10.1016\/j.envsoft.2026.107041_bib10","doi-asserted-by":"crossref","first-page":"2045","DOI":"10.5194\/hess-25-2045-2021","article-title":"Rainfall\u2013runoff prediction at multiple timescales with a single Long Short-Term Memory network","volume":"25","author":"Gauch","year":"2021","journal-title":"Hydrol. Earth Syst. Sci."},{"issue":"3","key":"10.1016\/j.envsoft.2026.107041_bib11","doi-asserted-by":"crossref","first-page":"1106","DOI":"10.1111\/coin.12336","article-title":"Gravitational search algorithm-based optimization of hybrid wind and solar renewable energy system","volume":"38","author":"Geleta","year":"2022","journal-title":"Comput. Intell."},{"key":"10.1016\/j.envsoft.2026.107041_bib12","doi-asserted-by":"crossref","DOI":"10.1016\/j.jenvman.2025.124353","article-title":"Combined effect of landuse\/landcover and climate change projection on the spatiotemporal streamflow response in cryosphere catchment in the Tibetan Plateau","volume":"376","author":"Gu\u00e9d\u00e9","year":"2025","journal-title":"J. Environ. Manag."},{"issue":"1971","key":"10.1016\/j.envsoft.2026.107041_bib13","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1098\/rspa.1998.0193","article-title":"The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis","volume":"454","author":"Huang","year":"1998","journal-title":"Proc. R. Soc. London, Ser. A: Math. Phys. Eng. Sci."},{"key":"10.1016\/j.envsoft.2026.107041_bib14","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1016\/j.jhydrol.2014.01.062","article-title":"Monthly streamflow prediction using modified EMD-based support vector machine","volume":"511","author":"Huang","year":"2014","journal-title":"J. Hydrol."},{"issue":"12","key":"10.1016\/j.envsoft.2026.107041_bib15","doi-asserted-by":"crossref","first-page":"4133","DOI":"10.1175\/JCLI-D-22-0737.1","article-title":"Long-Term changes, synoptic behaviors, and future projections of large-scale anomalous precipitation events in China detected by a deep learning autoencoder","volume":"36","author":"Huang","year":"2023","journal-title":"J. Clim."},{"key":"10.1016\/j.envsoft.2026.107041_bib16","series-title":"Automated Machine Learning: Methods, Systems, Challenges","author":"Hutter","year":"2019"},{"key":"10.1016\/j.envsoft.2026.107041_bib17","doi-asserted-by":"crossref","first-page":"1942","DOI":"10.1109\/ICNN.1995.488968","article-title":"Particle swarm optimization","volume":"1944","author":"Kennedy","year":"1995","journal-title":"Proceedings of ICNN'95 - International Conference on Neural Networks"},{"key":"10.1016\/j.envsoft.2026.107041_bib18","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1016\/j.ijdrr.2018.12.014","article-title":"Evaluating a primary healthcare centre's preparedness for disasters using the hospital safety index: lessons learned from the 2014 floods in Obrenovac, Serbia","volume":"34","author":"Lap\u010devi\u0107","year":"2019","journal-title":"Int. J. Disaster Risk Reduct."},{"issue":"3","key":"10.1016\/j.envsoft.2026.107041_bib19","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1038\/s41558-025-02248-7","article-title":"Extreme weather events have strong but different impacts on plant and insect phenology","volume":"15","author":"Li","year":"2025","journal-title":"Nat. Clim. Change"},{"issue":"15","key":"10.1016\/j.envsoft.2026.107041_bib20","doi-asserted-by":"crossref","first-page":"1857","DOI":"10.1080\/02626667.2019.1680846","article-title":"Comparison of daily streamflow forecasts using extreme learning machines and the random forest method","volume":"64","author":"Li","year":"2019","journal-title":"Hydrol. Sci. J."},{"issue":"1","key":"10.1016\/j.envsoft.2026.107041_bib21","doi-asserted-by":"crossref","first-page":"191","DOI":"10.2166\/hydro.2017.189","article-title":"Comparison of hybrid models for daily streamflow prediction in a forested basin","volume":"20","author":"Li","year":"2017","journal-title":"J. Hydroinform."},{"key":"10.1016\/j.envsoft.2026.107041_bib22","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1016\/j.procir.2021.03.088","article-title":"A survey on long short-term memory networks for time series prediction","volume":"99","author":"Lindemann","year":"2021","journal-title":"Proced. CIRP"},{"key":"10.1016\/j.envsoft.2026.107041_bib23","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.neucom.2019.01.078","article-title":"Bidirectional LSTM with attention mechanism and convolutional layer for text classification","volume":"337","author":"Liu","year":"2019","journal-title":"Neurocomputing"},{"key":"10.1016\/j.envsoft.2026.107041_bib24","doi-asserted-by":"crossref","DOI":"10.1016\/j.jclepro.2022.131841","article-title":"Mapping recent (1997\u20132017) and future (2030) county-level social vulnerability to socio-economic conditions and natural hazards throughout Iran","volume":"355","author":"Mafi-Gholami","year":"2022","journal-title":"J. Clean. Prod."},{"issue":"22","key":"10.1016\/j.envsoft.2026.107041_bib25","doi-asserted-by":"crossref","first-page":"11125","DOI":"10.1016\/j.amc.2012.04.069","article-title":"Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm","volume":"218","author":"Mirjalili","year":"2012","journal-title":"Appl. Math. Comput."},{"key":"10.1016\/j.envsoft.2026.107041_bib26","doi-asserted-by":"crossref","first-page":"50388","DOI":"10.1109\/ACCESS.2019.2903137","article-title":"An improved hybrid method combining gravitational search Algorithm with dynamic multi swarm particle swarm optimization","volume":"7","author":"Nagra","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.envsoft.2026.107041_bib27","doi-asserted-by":"crossref","DOI":"10.1016\/j.jenvman.2024.121754","article-title":"Assessment of climate change on river streamflow under different representative concentration pathways","volume":"366","author":"Nakhaei","year":"2024","journal-title":"J. Environ. Manag."},{"key":"10.1016\/j.envsoft.2026.107041_bib28","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1016\/j.jhydrol.2019.03.046","article-title":"Daily river flow forecasting using ensemble empirical mode decomposition based heuristic regression models: application on the perennial rivers in Iran and South Korea","volume":"572","author":"Rezaie-Balf","year":"2019","journal-title":"J. Hydrol."},{"issue":"13","key":"10.1016\/j.envsoft.2026.107041_bib29","doi-asserted-by":"crossref","first-page":"1629","DOI":"10.1080\/02626667.2019.1661417","article-title":"Enhancing streamflow forecasting using the augmenting ensemble procedure coupled machine learning models: case study of Aswan High Dam","volume":"64","author":"Rezaie-Balf","year":"2019","journal-title":"Hydrol. Sci. J."},{"key":"10.1016\/j.envsoft.2026.107041_bib30","doi-asserted-by":"crossref","DOI":"10.1016\/j.envsoft.2023.105854","article-title":"FlowDyn: a daily streamflow prediction pipeline for dynamical deep neural network applications","volume":"170","author":"Sadeghi Tabas","year":"2023","journal-title":"Environ. Model. Software"},{"key":"10.1016\/j.envsoft.2026.107041_bib31","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2020.124666","article-title":"Uncertainty quantification using the particle filter for non-stationary hydrological frequency analysis","volume":"584","author":"Sen","year":"2020","journal-title":"J. Hydrol."},{"issue":"11","key":"10.1016\/j.envsoft.2026.107041_bib32","doi-asserted-by":"crossref","first-page":"1547","DOI":"10.3390\/w13111547","article-title":"Comparison of forecasting models for real-time monitoring of water quality parameters based on hybrid deep learning neural networks","volume":"13","author":"Sha","year":"2021","journal-title":"Water"},{"key":"10.1016\/j.envsoft.2026.107041_bib33","series-title":"2019 IEEE International Conference on Big Data (Big Data)","first-page":"3285","article-title":"The performance of LSTM and BiLSTM in forecasting time series","author":"Siami-Namini","year":"2019"},{"issue":"16","key":"10.1016\/j.envsoft.2026.107041_bib34","doi-asserted-by":"crossref","first-page":"13951","DOI":"10.1007\/s00521-022-07246-w","article-title":"On fine-tuning deep learning models using transfer learning and hyper-parameters optimization for disease identification in maize leaves","volume":"34","author":"Subramanian","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.envsoft.2026.107041_bib35","doi-asserted-by":"crossref","DOI":"10.1016\/j.cpc.2023.108955","article-title":"Comparison of Bayesian and particle swarm algorithms for hyperparameter optimisation in machine learning applications in high energy physics","volume":"294","author":"Tani","year":"2024","journal-title":"Comput. Phys. Commun."},{"key":"10.1016\/j.envsoft.2026.107041_bib36","series-title":"2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"4144","article-title":"A complete ensemble empirical mode decomposition with adaptive noise","author":"Torres","year":"2011"},{"key":"10.1016\/j.envsoft.2026.107041_bib37","doi-asserted-by":"crossref","DOI":"10.1016\/j.jenvman.2024.121375","article-title":"Investigating the impacts of climate change on hydroclimatic extremes in the Tar-Pamlico River basin, North Carolina","volume":"363","author":"Tran","year":"2024","journal-title":"J. Environ. Manag."},{"key":"10.1016\/j.envsoft.2026.107041_bib38","doi-asserted-by":"crossref","DOI":"10.1016\/j.envsoft.2024.106091","article-title":"Spatio-temporal deep learning model for accurate streamflow prediction with multi-source data fusion","volume":"178","author":"Wang","year":"2024","journal-title":"Environ. Model. Software"},{"key":"10.1016\/j.envsoft.2026.107041_bib39","doi-asserted-by":"crossref","DOI":"10.1016\/j.jenvman.2025.124121","article-title":"Dynamic classification and attention mechanism-based bidirectional long short-term memory network for daily runoff prediction in Aksu River basin, Northwest China","volume":"374","author":"Wei","year":"2025","journal-title":"J. Environ. Manag."},{"issue":"1","key":"10.1016\/j.envsoft.2026.107041_bib40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1142\/S1793536909000047","article-title":"Ensemble EMPIRICAL mode decomposition: a noise-assisted data analysis method","volume":"1","author":"Wu","year":"2009","journal-title":"Adv. Adapt. Data Anal."},{"key":"10.1016\/j.envsoft.2026.107041_bib41","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2022.128122","article-title":"Two-stage hybrid model for hydrological series prediction based on a new method of partitioning datasets","volume":"612","author":"Xu","year":"2022","journal-title":"J. Hydrol."},{"issue":"2","key":"10.1016\/j.envsoft.2026.107041_bib42","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1142\/S1793536910000422","article-title":"Complementary ensemble empirical mode decomposition: a novel noise enhanced data analysis method","volume":"2","author":"Yeh","year":"2010","journal-title":"Adv. Adapt. Data Anal."},{"key":"10.1016\/j.envsoft.2026.107041_bib43","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.jhydrol.2016.06.029","article-title":"CEREF: a hybrid data-driven model for forecasting annual streamflow from a socio-hydrological system","volume":"540","author":"Zhang","year":"2016","journal-title":"J. Hydrol."}],"container-title":["Environmental Modelling &amp; Software"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S136481522600188X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S136481522600188X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T08:43:28Z","timestamp":1783068208000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S136481522600188X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":43,"alternative-id":["S136481522600188X"],"URL":"https:\/\/doi.org\/10.1016\/j.envsoft.2026.107041","relation":{},"ISSN":["1364-8152"],"issn-type":[{"value":"1364-8152","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Improving extreme streamflow prediction by integrating CEEMDAN, autoencoders, and attention-enhanced BiLSTM optimized via GSA-PSO","name":"articletitle","label":"Article Title"},{"value":"Environmental Modelling & Software","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.envsoft.2026.107041","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"107041"}}