{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T21:54:37Z","timestamp":1781560477643,"version":"3.54.5"},"reference-count":55,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003524","name":"New Zealand Ministry of Business Innovation and Employment","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003524","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":[[2026,9]]},"DOI":"10.1016\/j.envsoft.2026.107050","type":"journal-article","created":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T16:36:20Z","timestamp":1780331780000},"page":"107050","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Short-term forecasting of daily dissolved oxygen in streams using SWAT, remote sensing and explainable machine learning"],"prefix":"10.1016","volume":"204","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9303-9056","authenticated-orcid":false,"given":"Thanh D.","family":"Dang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linh","family":"Hoang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K.","family":"Benjamin Woodward","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tam V.","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander H.","family":"Elliott","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"10","key":"10.1016\/j.envsoft.2026.107050_bib1","doi-asserted-by":"crossref","first-page":"1783","DOI":"10.1016\/0016-7037(96)00055-5","article-title":"Photochemical and microbial consumption of dissolved organic carbon and dissolved oxygen in the Amazon river system","volume":"60","author":"Amon","year":"1996","journal-title":"Geochem. Cosmochim. Acta"},{"key":"10.1016\/j.envsoft.2026.107050_bib2","doi-asserted-by":"crossref","first-page":"1895","DOI":"10.1016\/j.jhydrol.2014.10.009","article-title":"Modelling of dissolved oxygen in the Danube River using artificial neural networks and Monte Carlo simulation uncertainty analysis","volume":"519","author":"Antanasijevi\u0107","year":"2014","journal-title":"J. Hydrol."},{"issue":"1","key":"10.1016\/j.envsoft.2026.107050_bib3","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1111\/j.1752-1688.1998.tb05961.x","article-title":"Large area hydrologic modeling and assessment part I: model development 1","volume":"34","author":"Arnold","year":"1998","journal-title":"JAWRA Journal of the American Water Resources Association"},{"issue":"3","key":"10.1016\/j.envsoft.2026.107050_bib4","doi-asserted-by":"crossref","DOI":"10.1029\/2023WR035744","article-title":"A machine learning and remote sensing-based model for algae pigment and dissolved oxygen retrieval on a small inland lake","volume":"60","author":"Beal","year":"2024","journal-title":"Water Resour. Res."},{"issue":"3","key":"10.1016\/j.envsoft.2026.107050_bib5","doi-asserted-by":"crossref","first-page":"179","DOI":"10.4296\/cwrj3203179","article-title":"A review of statistical water temperature models","volume":"32","author":"Benyahya","year":"2007","journal-title":"Can. Water Resour. J."},{"key":"10.1016\/j.envsoft.2026.107050_bib6","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2023.129354","article-title":"Physics-informed machine learning method for modelling transport of a conservative pollutant in surface water systems","volume":"619","author":"Bertels","year":"2023","journal-title":"J. Hydrol."},{"key":"10.1016\/j.envsoft.2026.107050_bib7","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"issue":"4","key":"10.1016\/j.envsoft.2026.107050_bib8","doi-asserted-by":"crossref","first-page":"113","DOI":"10.22271\/fish.2022.v10.i4b.2693","article-title":"Effects of dissolved oxygen concentration on freshwater fish: a review","volume":"10","author":"Bulbul Ali","year":"2022","journal-title":"International Journal of Fisheries and Aquatic Studies"},{"issue":"5","key":"10.1016\/j.envsoft.2026.107050_bib9","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1061\/(ASCE)0733-9372(1991)117:5(640)","article-title":"Delta method for estimating primary production, respiration, and reaeration in streams","volume":"117","author":"Chapra","year":"1991","journal-title":"Journal of environmental engineering"},{"key":"10.1016\/j.envsoft.2026.107050_bib10","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/S0048-9697(03)00062-7","article-title":"A review of dissolved oxygen modelling techniques for lowland rivers","volume":"314","author":"Cox","year":"2003","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.envsoft.2026.107050_bib11","article-title":"Modeling temporal and spatial variations of biogeochemical processes in a large subtropical lake: assessing alternative solutions to algal blooms in Lake Okeechobee, Florida","volume":"47","author":"Dang","year":"2023","journal-title":"J. Hydrol.: Reg. Stud."},{"key":"10.1016\/j.envsoft.2026.107050_bib12","doi-asserted-by":"crossref","DOI":"10.1080\/02626667.2026.2666375","article-title":"Harnessing high-performance computing and job scheduling for improving SWAT calibration and uncertainty analysis","author":"Dang","year":"2026","journal-title":"Hydrol. Sci. J."},{"issue":"4","key":"10.1016\/j.envsoft.2026.107050_bib13","first-page":"439","article-title":"Artificial intelligence-based approaches for multi-station modelling of dissolve oxygen in river","volume":"4","author":"Elkiran","year":"2018","journal-title":"Global Journal of Environmental Science and Management"},{"key":"10.1016\/j.envsoft.2026.107050_bib14","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1016\/j.jhydrol.2016.01.034","article-title":"Using the soil and water assessment tool (SWAT) to model ecosystem services: a systematic review","volume":"535","author":"Francesconi","year":"2016","journal-title":"J. Hydrol."},{"issue":"1","key":"10.1016\/j.envsoft.2026.107050_bib15","doi-asserted-by":"crossref","first-page":"28","DOI":"10.2307\/1942049","article-title":"Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types","volume":"5","author":"Gamon","year":"1995","journal-title":"Ecol. Appl."},{"issue":"7","key":"10.1016\/j.envsoft.2026.107050_bib16","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1007\/s10661-023-11492-3","article-title":"Modeling dissolved oxygen concentration using machine learning techniques with dimensionality reduction approach","volume":"195","author":"Garabaghi","year":"2023","journal-title":"Environ. Monit. Assess."},{"key":"10.1016\/j.envsoft.2026.107050_bib17","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1016\/j.jhydrol.2018.02.061","article-title":"Modelling daily dissolved oxygen concentration using least square support vector machine, multivariate adaptive regression splines and M5 model tree","volume":"559","author":"Heddam","year":"2018","journal-title":"J. Hydrol."},{"issue":"32","key":"10.1016\/j.envsoft.2026.107050_bib18","article-title":"\u2018Estimating nitrogen loss from a dairy farming catchment using the soil and water assessment tool (SWAT)\u2019, Nutrient loss mitigations for compliance in agriculture","author":"Hoang","year":"2019","journal-title":"Occasional Report"},{"issue":"8","key":"10.1016\/j.envsoft.2026.107050_bib19","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.2026.107050_bib20","doi-asserted-by":"crossref","DOI":"10.1016\/j.ecoinf.2024.102695","article-title":"A long-term multivariate time series prediction model for dissolved oxygen","volume":"82","author":"Hu","year":"2024","journal-title":"Ecol. Inform."},{"key":"10.1016\/j.envsoft.2026.107050_bib21","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2021.126991","article-title":"Successful modelling of river dissolved oxygen dynamics requires knowledge of stream channel environments","volume":"603","author":"Hutchins","year":"2021","journal-title":"J. Hydrol."},{"key":"10.1016\/j.envsoft.2026.107050_bib22","series-title":"Prioritisation of Areas in the Hauraki Gulf Marine Park for Biodiversity Conservation","author":"Jackson","year":"2014"},{"issue":"2","key":"10.1016\/j.envsoft.2026.107050_bib23","doi-asserted-by":"crossref","DOI":"10.1002\/clen.201500395","article-title":"Modeling of dissolved oxygen concentration and its hysteresis behavior in rivers using wavelet transform-based hybrid models","volume":"45","author":"Khani","year":"2017","journal-title":"CLEAN\u2013Soil, Air, Water"},{"key":"10.1016\/j.envsoft.2026.107050_bib24","doi-asserted-by":"crossref","DOI":"10.1016\/j.envint.2019.105301","article-title":"Application of satellite remote sensing in monitoring dissolved oxygen variabilities: a case study for coastal waters in Korea","volume":"134","author":"Kim","year":"2020","journal-title":"Environ. Int."},{"issue":"1","key":"10.1016\/j.envsoft.2026.107050_bib25","doi-asserted-by":"crossref","first-page":"25","DOI":"10.7158\/13241583.2013.11465417","article-title":"A standard approach to baseflow separation using the Lyne and Hollick filter","volume":"17","author":"Ladson","year":"2013","journal-title":"Australasian Journal of Water Resources"},{"key":"10.1016\/j.envsoft.2026.107050_bib27","series-title":"Land Cover Database","year":"2020"},{"key":"10.1016\/j.envsoft.2026.107050_bib29","article-title":"An accurate forecasting model for key water quality factors based on Transformer with multi-scale attention mechanism","author":"Li","year":"2025","journal-title":"Environ. Model. Software"},{"issue":"3\u20134","key":"10.1016\/j.envsoft.2026.107050_bib30","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.ecolmodel.2007.01.004","article-title":"Structural uncertainty in a river water quality modelling system","volume":"204","author":"Lindenschmidt","year":"2007","journal-title":"Ecol. Model."},{"issue":"7","key":"10.1016\/j.envsoft.2026.107050_bib31","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1080\/13658816.2024.2342321","article-title":"Physics-guided spatio\u2013temporal neural network for predicting dissolved oxygen concentration in rivers","volume":"38","author":"Liu","year":"2024","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"10.1016\/j.envsoft.2026.107050_bib32","doi-asserted-by":"crossref","DOI":"10.3389\/fmars.2023.1117794","article-title":"Modelling the biogeochemical footprint of rivers in the Hauraki Gulf, New Zealand","volume":"10","author":"Macdonald","year":"2023","journal-title":"Front. Mar. Sci."},{"issue":"3","key":"10.1016\/j.envsoft.2026.107050_bib33","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1111\/emr.12232","article-title":"Riparian management: a restoration tool for New Zealand streams","volume":"17","author":"McKergow","year":"2016","journal-title":"Ecol. Manag. Restor."},{"issue":"25","key":"10.1016\/j.envsoft.2026.107050_bib35","doi-asserted-by":"crossref","first-page":"3689","DOI":"10.1002\/hyp.7809","article-title":"Identifying and linking source areas of flow and P transport in dairy-grazed headwater catchments, North Island, New Zealand","volume":"24","author":"M\u00fcller","year":"2010","journal-title":"Hydrol. Process."},{"issue":"6","key":"10.1016\/j.envsoft.2026.107050_bib34","doi-asserted-by":"crossref","first-page":"1763","DOI":"10.13031\/trans.58.10715","article-title":"Hydrologic and water quality models: performance measures and evaluation criteria","volume":"58","author":"Moriasi","year":"2015","journal-title":"Trans. ASABE"},{"key":"10.1016\/j.envsoft.2026.107050_bib36","doi-asserted-by":"crossref","DOI":"10.1016\/j.envsoft.2022.105497","article-title":"An interactive graphical interface tool for parameter calibration, sensitivity analysis, uncertainty analysis, and visualization for the soil and water assessment tool","volume":"156","author":"Nguyen","year":"2022","journal-title":"Environ. Model. Software"},{"key":"10.1016\/j.envsoft.2026.107050_bib37","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.ecoinf.2018.12.006","article-title":"Mapping vegetation types in semi-arid riparian regions using random forest and object-based image approach: a case study of the colorado river ecosystem, Grand Canyon, Arizona","volume":"50","author":"Nguyen","year":"2019","journal-title":"Ecol. Inform."},{"key":"10.1016\/j.envsoft.2026.107050_bib38","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2020.125220","article-title":"Water quality prediction using SWAT ANN coupled approach","volume":"590","author":"Noori","year":"2020","journal-title":"J. Hydrol."},{"issue":"1","key":"10.1016\/j.envsoft.2026.107050_bib39","article-title":"Use of artificial neural networks and multiple linear regression model for the prediction of dissolved oxygen in rivers: case study of hydrographic basin of river Nyando, Kenya","volume":"2020","author":"Ouma","year":"2020","journal-title":"Complexity"},{"issue":"3","key":"10.1016\/j.envsoft.2026.107050_bib40","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1080\/00288233.2012.676991","article-title":"Nitrogen inputs and outputs for New Zealand from 1990 to 2010 at national and regional scales","volume":"55","author":"Parfitt","year":"2012","journal-title":"New Zealand Journal of Agricultural Research"},{"issue":"6","key":"10.1016\/j.envsoft.2026.107050_bib41","doi-asserted-by":"crossref","first-page":"852","DOI":"10.3390\/rs18060852","article-title":"Advancing forest inventory and fuel monitoring with multi-sensor hybrid models: a comparative framework for basal area estimation","volume":"18","author":"Salehnia","year":"2026","journal-title":"Remote Sens."},{"key":"10.1016\/j.envsoft.2026.107050_bib42","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1016\/j.scitotenv.2016.06.151","article-title":"An integrated numerical framework for water quality modelling in cold region rivers: a case of the lower Athabasca River","volume":"569","author":"Shakibaeinia","year":"2016","journal-title":"Sci. Total Environ."},{"issue":"6","key":"10.1016\/j.envsoft.2026.107050_bib43","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1016\/j.ecolmodel.2009.01.004","article-title":"Artificial neural network modeling of the river water quality\u2014a case study","volume":"220","author":"Singh","year":"2009","journal-title":"Ecol. Model."},{"key":"10.1016\/j.envsoft.2026.107050_bib44","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.jher.2021.06.004","article-title":"Data- and model-driven determination of flow pathways in the Piako catchment, New Zealand","volume":"37","author":"Singh","year":"2021","journal-title":"J. Hydro-environ. Res."},{"issue":"2","key":"10.1016\/j.envsoft.2026.107050_bib46","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1080\/02626667.2011.637497","article-title":"Water and nutrient predictions in ungauged basins: set-up and evaluation of a model at the national scale","volume":"57","author":"Str\u00f6mqvist","year":"2012","journal-title":"Hydrol. Sci. J."},{"issue":"3","key":"10.1016\/j.envsoft.2026.107050_bib45","doi-asserted-by":"crossref","first-page":"458","DOI":"10.3390\/w17030458","article-title":"Soil and water assessment tool-based prediction of runoff under scenarios of land use\/land cover and climate change across Indian agro-climatic zones: implications for sustainable development goals","volume":"17","author":"Subbarayan","year":"2025","journal-title":"Water"},{"key":"10.1016\/j.envsoft.2026.107050_bib47","first-page":"25","article-title":"An assessment of the accuracy of interpolated daily rainfall for New Zealand","author":"Tait","year":"2012","journal-title":"Journal of Hydrology (New Zealand)"},{"key":"10.1016\/j.envsoft.2026.107050_bib48","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.isprsjprs.2020.04.001","article-title":"Google earth engine for geo-big data applications: a meta-analysis and systematic review","volume":"164","author":"Tamiminia","year":"2020","journal-title":"ISPRS J. Photogrammetry Remote Sens."},{"key":"10.1016\/j.envsoft.2026.107050_bib49","series-title":"Agricultural Waste Manual","author":"Vanderholm","year":"1984"},{"key":"10.1016\/j.envsoft.2026.107050_bib50","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"19","key":"10.1016\/j.envsoft.2026.107050_bib51","doi-asserted-by":"crossref","first-page":"3485","DOI":"10.5194\/hess-27-3485-2023","article-title":"Calibrating macroscale hydrological models in poorly gauged and heavily regulated basins","volume":"27","author":"Vu","year":"2023","journal-title":"Hydrol. Earth Syst. Sci."},{"issue":"2","key":"10.1016\/j.envsoft.2026.107050_bib52","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/0304-3800(78)90034-0","article-title":"Mathematical models of dissolved oxygen concentration in fresh water","volume":"5","author":"Wang","year":"1978","journal-title":"Ecol. Model."},{"issue":"4","key":"10.1016\/j.envsoft.2026.107050_bib53","doi-asserted-by":"crossref","DOI":"10.1002\/hyp.14563","article-title":"Understanding event runoff coefficient variability across Australia using the hydroEvents R package","volume":"36","author":"Wasko","year":"2022","journal-title":"Hydrol. Process."},{"issue":"5","key":"10.1016\/j.envsoft.2026.107050_bib54","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1071\/MF12155","article-title":"Trends in water quality of five dairy farming streams in response to adoption of best practice and benefits of long-term monitoring at the catchment scale","volume":"64","author":"Wilcock","year":"2013","journal-title":"Mar. Freshw. Res."},{"issue":"1\u20132","key":"10.1016\/j.envsoft.2026.107050_bib55","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.jhydrol.2005.07.017","article-title":"Streamflow simulation within UK ungauged catchments using a daily rainfall-runoff model","volume":"320","author":"Young","year":"2006","journal-title":"J. Hydrol."},{"key":"10.1016\/j.envsoft.2026.107050_bib56","article-title":"At-LSTM: an attention-based LSTM model for financial time series prediction","volume":"569","author":"Zhang","year":"2019"},{"issue":"4","key":"10.1016\/j.envsoft.2026.107050_bib57","doi-asserted-by":"crossref","first-page":"2357","DOI":"10.1021\/acs.est.0c06783","article-title":"From hydrometeorology to river water quality: can a deep learning model predict dissolved oxygen at the continental scale?","volume":"55","author":"Zhi","year":"2021","journal-title":"Environmental science & technology"}],"container-title":["Environmental Modelling & Software"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1364815226001970?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1364815226001970?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T21:25:32Z","timestamp":1781558732000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1364815226001970"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":55,"alternative-id":["S1364815226001970"],"URL":"https:\/\/doi.org\/10.1016\/j.envsoft.2026.107050","relation":{},"ISSN":["1364-8152"],"issn-type":[{"value":"1364-8152","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Short-term forecasting of daily dissolved oxygen in streams using SWAT, remote sensing and explainable machine learning","name":"articletitle","label":"Article Title"},{"value":"Environmental Modelling & Software","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.envsoft.2026.107050","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"107050"}}