{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T10:10:12Z","timestamp":1774001412376,"version":"3.50.1"},"reference-count":48,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100015499","name":"Jeonbuk National University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100015499","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003668","name":"Korea Institute of Planning and Evaluation for Technology in Food Agriculture Forestry and Fisheries","doi-asserted-by":"publisher","award":["RS-2025-02215604"],"award-info":[{"award-number":["RS-2025-02215604"]}],"id":[{"id":"10.13039\/501100003668","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Environmental Modelling & Software"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1016\/j.envsoft.2026.106899","type":"journal-article","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T17:10:05Z","timestamp":1769533805000},"page":"106899","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Advancing water level prediction using clustering-based machine learning techniques in data-scarce regions"],"prefix":"10.1016","volume":"198","author":[{"given":"SangHyun","family":"Lee","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7337-8165","authenticated-orcid":false,"given":"Taeil","family":"Jang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.envsoft.2026.106899_bib1","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/0375-9601(93)90232-O","article-title":"Measuring complexity using information fluctuation","volume":"172","author":"Bates","year":"1993","journal-title":"Phys. Lett."},{"key":"10.1016\/j.envsoft.2026.106899_bib2","doi-asserted-by":"crossref","first-page":"4827","DOI":"10.1007\/s00477-023-02531-z","article-title":"A new criterion for determining the best decomposition level and filter for wavelet-based data-driven forecasting frameworks\u2014validating using three case studies on the CAMELS dataset","volume":"37","author":"Behbahani","year":"2023","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"10.1016\/j.envsoft.2026.106899_bib3","first-page":"3963","volume":"vol. 38","author":"Behbahani","year":"2024"},{"key":"10.1016\/j.envsoft.2026.106899_bib4","doi-asserted-by":"crossref","DOI":"10.1029\/2022WR033091","article-title":"Using machine learning to identify hydrologic signatures with an encoder\u2013decoder framework","volume":"59","author":"Botterill","year":"2023","journal-title":"Water Resour. Res."},{"key":"10.1016\/j.envsoft.2026.106899_bib5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/03610927408827101","article-title":"A dendrite method foe cluster analysis","volume":"3","author":"Cali\u00f1ski","year":"1974","journal-title":"Commun. Stat."},{"key":"10.1016\/j.envsoft.2026.106899_bib6","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.envsoft.2012.09.005","article-title":"Characterising and understanding emission sources using bivariate polar plots and k-means clustering","volume":"40","author":"Carslaw","year":"2013","journal-title":"Environ. Model. Software"},{"key":"10.1016\/j.envsoft.2026.106899_bib7","first-page":"11029","article-title":"Wavelet packet-genetic programming: a new model for meteorological drought hindcasting","volume":"32","author":"Danandeh Mehr","year":"2021","journal-title":"Teknik Dergi\/Technical Journal of Turkish Chamber of Civil Engineers"},{"key":"10.1016\/j.envsoft.2026.106899_bib8","doi-asserted-by":"crossref","DOI":"10.1016\/j.ecoinf.2023.102452","article-title":"Maximum energy entropy: a novel signal preprocessing approach for data-driven monthly streamflow forecasting","volume":"79","author":"Dariane","year":"2024","journal-title":"Ecol. Inform."},{"key":"10.1016\/j.envsoft.2026.106899_bib9","doi-asserted-by":"crossref","DOI":"10.1029\/2021WR029583","article-title":"The data synergy effects of time-series deep learning models in hydrology","volume":"58","author":"Fang","year":"2022","journal-title":"Water Resour. Res."},{"key":"10.1016\/j.envsoft.2026.106899_bib10","doi-asserted-by":"crossref","first-page":"4201","DOI":"10.1007\/s11269-018-2038-x","article-title":"Learning from multiple models using artificial intelligence to improve model prediction accuracies: application to River flows","volume":"32","author":"Ghorbani","year":"2018","journal-title":"Water Resour. Manag."},{"key":"10.1016\/j.envsoft.2026.106899_bib11","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"10.1016\/j.envsoft.2026.106899_bib13","doi-asserted-by":"crossref","DOI":"10.1029\/2010JC006269","article-title":"Hydrodynamic modeling of Lake Ontario: an intercomparison of three models","volume":"115","author":"Huang","year":"2010","journal-title":"J. Geophys. Res., Oceans"},{"key":"10.1016\/j.envsoft.2026.106899_bib14","doi-asserted-by":"crossref","DOI":"10.1016\/j.agwat.2023.108210","article-title":"Multi-steps drought forecasting in arid and humid climate environments: development of integrative machine learning model","volume":"281","author":"Karbasi","year":"2023","journal-title":"Agric. Water Manag."},{"key":"10.1016\/j.envsoft.2026.106899_bib15","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.jhydrol.2005.05.011","article-title":"Water balance of Lake Tana and its sensitivity to fluctuations in rainfall, Blue Nile basin, Ethiopia","volume":"316","author":"Kebede","year":"2006","journal-title":"J. Hydrol. (Amst.)"},{"key":"10.1016\/j.envsoft.2026.106899_bib16","doi-asserted-by":"crossref","first-page":"2946","DOI":"10.1002\/2013WR014127","article-title":"Root-zone soil moisture estimation using data-driven methods","volume":"50","author":"Kornelsen","year":"2014","journal-title":"Water Resour. Res."},{"key":"10.1016\/j.envsoft.2026.106899_bib17","article-title":"Evaluating vulnerable zones and spatiotemporal dynamics in Saemangeum lake under sluice gate operation","volume":"90","author":"Kwak","year":"2025","journal-title":"Reg. Stud. Mar. Sci."},{"key":"10.1016\/j.envsoft.2026.106899_bib18","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.jhydrol.2005.04.003","article-title":"Recent advances in wavelet analyses: part 1. A review of concepts","volume":"314","author":"Labat","year":"2005","journal-title":"J. Hydrol. (Amst.)"},{"key":"10.1016\/j.envsoft.2026.106899_bib19","first-page":"1383","article-title":"Predicting BOD under various hydrological conditions in the dongjin river basin using physics-based and data-driven models","volume":"13","author":"Lee","year":"2021","journal-title":"Water (Switzerland)"},{"key":"10.1016\/j.envsoft.2026.106899_bib20","doi-asserted-by":"crossref","DOI":"10.1029\/2024WR039744","article-title":"Large-Scale drought forecasting in the U.S. Southern plains through a hybrid cluster-based wavelet-machine learning approach","volume":"61","author":"Lee","year":"2025","journal-title":"Water Resour. Res."},{"key":"10.1016\/j.envsoft.2026.106899_bib21","doi-asserted-by":"crossref","first-page":"1809","DOI":"10.1175\/JHM-D-24-0041.1","article-title":"Wavelet-entropy enhanced clustering: a comprehensive analysis of drought patterns in the Southern plains, United States","volume":"25","author":"Lee","year":"2024","journal-title":"J. Hydrometeorol."},{"key":"10.1016\/j.envsoft.2026.106899_bib22","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2023.130025","article-title":"A combined hydrodynamic model and deep learning method to predict water level in ungauged rivers","volume":"625","author":"Li","year":"2023","journal-title":"J. Hydrol. (Amst.)"},{"key":"10.1016\/j.envsoft.2026.106899_bib23","first-page":"1982","article-title":"Impacts of climate change scenarios on non-point source pollution in the saemangeum watershed, South Korea","volume":"11","author":"Li","year":"2019","journal-title":"Water (Switzerland)"},{"key":"10.1016\/j.envsoft.2026.106899_bib24","doi-asserted-by":"crossref","DOI":"10.1016\/j.envsoft.2021.105075","article-title":"A hybrid bayesian Vine model for water level prediction","volume":"142","author":"Liu","year":"2021","journal-title":"Environ. Model. Software"},{"key":"10.1016\/j.envsoft.2026.106899_bib25","series-title":"A Wavelet Tour of Signal Processing: the Sparse Way","author":"Mallat","year":"2008"},{"key":"10.1016\/j.envsoft.2026.106899_bib26","doi-asserted-by":"crossref","DOI":"10.1016\/j.envsoft.2024.106033","article-title":"Estimating water levels and discharges in tidal rivers and estuaries: review of machine learning approaches","volume":"176","author":"Mihel","year":"2024","journal-title":"Environ. Model. Software"},{"key":"10.1016\/j.envsoft.2026.106899_bib27","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2023.130320","article-title":"Daily scale streamflow forecasting in multiple stream orders of Cauvery River, India: application of advanced ensemble and deep learning models","volume":"626","author":"Naganna","year":"2023","journal-title":"J. Hydrol. (Amst.)"},{"key":"10.1016\/j.envsoft.2026.106899_bib28","doi-asserted-by":"crossref","DOI":"10.1016\/0022-1694(70)90255-6","article-title":"River flow forecasting through conceptual models part I - a discussion of principles","volume":"10","author":"Nash","year":"1970","journal-title":"J. Hydrol. (Amst.)"},{"key":"10.1016\/j.envsoft.2026.106899_bib29","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2021.127272","article-title":"Robust clustering for assessing the spatiotemporal variability of groundwater quantity and quality","volume":"604","author":"Nourani","year":"2022","journal-title":"J. Hydrol. (Amst.)"},{"key":"10.1016\/j.envsoft.2026.106899_bib30","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.jhydrol.2015.02.048","article-title":"Wavelet-entropy data pre-processing approach for ANN-based groundwater level modeling","volume":"524","author":"Nourani","year":"2015","journal-title":"J. Hydrol. (Amst.)"},{"key":"10.1016\/j.envsoft.2026.106899_bib31","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.jhydrol.2014.03.057","article-title":"Applications of hybrid wavelet-Artificial Intelligence models in hydrology: a review","volume":"514","author":"Nourani","year":"2014","journal-title":"J. Hydrol. (Amst.)"},{"key":"10.1016\/j.envsoft.2026.106899_bib32","series-title":"Case Studies in Intelligent Computing: Achievements and Trends","article-title":"Implication of feature extraction methods to improve performance of hybrid wavelet-ann rainfall-runoff model","author":"Nourani","year":"2014"},{"key":"10.1016\/j.envsoft.2026.106899_bib33","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1002\/hyp.6250","article-title":"Determination of the unit hydrograph of a typical urban basin using genetic programming and artificial neural networks","volume":"21","author":"Rabu\u00f1al","year":"2007","journal-title":"Hydrol. Process."},{"key":"10.1016\/j.envsoft.2026.106899_bib34","first-page":"3602","article-title":"A new multi-objective genetic programming model for meteorological drought forecasting","volume":"15","author":"Reihanifar","year":"2023","journal-title":"Water (Switzerland)"},{"key":"10.1016\/j.envsoft.2026.106899_bib35","doi-asserted-by":"crossref","first-page":"2096","DOI":"10.1080\/02626667.2015.1083651","article-title":"Using entropy theory to improve the definition of homogeneous regions in the semi-arid region of Brazil","volume":"61","author":"Rodriguez","year":"2016","journal-title":"Hydrol. Sci. J."},{"key":"10.1016\/j.envsoft.2026.106899_bib36","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.atmosres.2012.11.003","article-title":"A review on the applications of wavelet transform in hydrology time series analysis","volume":"122","author":"Sang","year":"2013","journal-title":"Atmos. Res."},{"key":"10.1016\/j.envsoft.2026.106899_bib37","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1007\/s00704-018-2390-z","article-title":"Temperature-based modeling of reference evapotranspiration using several artificial intelligence models: application of different modeling scenarios","volume":"135","author":"Sanikhani","year":"2019","journal-title":"Theor. Appl. Climatol."},{"key":"10.1016\/j.envsoft.2026.106899_bib38","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2022.127984","article-title":"Conjunction of cluster ensemble-model ensemble techniques for spatiotemporal assessment of groundwater depletion in semi-arid plains","volume":"610","author":"Sharghi","year":"2022","journal-title":"J. Hydrol. (Amst.)"},{"key":"10.1016\/j.envsoft.2026.106899_bib39","doi-asserted-by":"crossref","DOI":"10.1029\/2024WR038192","article-title":"Improving streamflow prediction using multiple hydrological models and machine learning methods","volume":"61","author":"Solanki","year":"2025","journal-title":"Water Resour. Res."},{"key":"10.1016\/j.envsoft.2026.106899_bib40","article-title":"Short-term wind power forecasting on multiple scales using VMD decomposition, K-Means clustering and LSTM principal computing","volume":"7","author":"Sun","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.envsoft.2026.106899_bib41","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.jhydrol.2015.01.006","article-title":"The drought risk atlas: enhancing decision support for drought risk management in the United States","volume":"526","author":"Svoboda","year":"2015","journal-title":"J. Hydrol. (Amst.)"},{"key":"10.1016\/j.envsoft.2026.106899_bib42","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1007\/s12145-023-01212-3","article-title":"A compound approach for ten-day runoff prediction by coupling wavelet denoising, attention mechanism, and LSTM based on GPU parallel acceleration technology","volume":"17","author":"Wang","year":"2024","journal-title":"Earth Sci Inform"},{"key":"10.1016\/j.envsoft.2026.106899_bib43","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/s12145-024-01524-y","article-title":"A singular spectrum analysis-enhanced BiTCN-selfattention model for runoff prediction","volume":"18","author":"Wang","year":"2025","journal-title":"Earth Sci Inform"},{"key":"10.1016\/j.envsoft.2026.106899_bib44","doi-asserted-by":"crossref","DOI":"10.1016\/j.agwat.2025.110078","article-title":"Estimating actual evapotranspiration from widely available meteorological data with a hybrid CNN\u2013LSTM","volume":"323","author":"Woo","year":"2026","journal-title":"Agric. Water Manag."},{"key":"10.1016\/j.envsoft.2026.106899_bib45","doi-asserted-by":"crossref","DOI":"10.1016\/j.envsoft.2024.106194","article-title":"Analysis of the spatial heterogeneity of glacier melting in Tibet Autonomous Region and its influential factors using the K-means and XGBoost-SHAP algorithms","volume":"182","author":"Xu","year":"2024","journal-title":"Environ. Model. Software"},{"key":"10.1016\/j.envsoft.2026.106899_bib46","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1016\/j.jhydrol.2018.11.069","article-title":"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","volume":"569","author":"Yaseen","year":"2019","journal-title":"J. Hydrol. (Amst.)"},{"key":"10.1016\/j.envsoft.2026.106899_bib47","doi-asserted-by":"crossref","DOI":"10.1016\/j.envsoft.2022.105468","article-title":"A two-stage modelling method for multi-station daily water level prediction","volume":"156","author":"Yuan","year":"2022","journal-title":"Environ. Model. Software"},{"key":"10.1016\/j.envsoft.2026.106899_bib48","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2020.125285","article-title":"Research on the long-term and short-term forecasts of navigable river's water-level fluctuation based on the adaptive multilayer perceptron","volume":"591","author":"Zhou","year":"2020","journal-title":"J. Hydrol. (Amst.)"},{"key":"10.1016\/j.envsoft.2026.106899_bib49","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2020.124819","article-title":"Forecasting of water level in multiple temperate lakes using machine learning models","volume":"585","author":"Zhu","year":"2020","journal-title":"J. Hydrol. (Amst.)"}],"container-title":["Environmental Modelling & Software"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1364815226000460?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1364815226000460?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T07:23:05Z","timestamp":1773991385000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1364815226000460"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":48,"alternative-id":["S1364815226000460"],"URL":"https:\/\/doi.org\/10.1016\/j.envsoft.2026.106899","relation":{},"ISSN":["1364-8152"],"issn-type":[{"value":"1364-8152","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Advancing water level prediction using clustering-based machine learning techniques in data-scarce regions","name":"articletitle","label":"Article Title"},{"value":"Environmental Modelling & Software","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.envsoft.2026.106899","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":"106899"}}