{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T21:59:25Z","timestamp":1790200765658,"version":"4.1.0"},"reference-count":64,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Water Research"],"published-print":{"date-parts":[[2027,1]]},"DOI":"10.1016\/j.watres.2026.126916","type":"journal-article","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T09:45:52Z","timestamp":1789119952000},"page":"126916","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PC","title":["From low-cost measurements to rapid water quality monitoring: a hybrid machine learning\u2013optimization framework for predicting laboratory-intensive parameters"],"prefix":"10.1016","volume":"308","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5343-3243","authenticated-orcid":false,"given":"Mehrdad","family":"Ranaie","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saeid","family":"Pourmanafi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9299-5567","authenticated-orcid":false,"given":"Ali","family":"Lotfi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Nemati","family":"Varnosfaderany","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mostafa","family":"Tarkesh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1\u20132","key":"10.1016\/j.watres.2026.126916_bib1","doi-asserted-by":"crossref","first-page":"1569","DOI":"10.1080\/10807039.2019.1594154","article-title":"Assessing groundwater quality and health risks of fluoride pollution in the Shasler Vagu (SV) watershed of Nalgonda, India","volume":"26","author":"Adimalla","year":"2020","journal-title":"Hum. Ecol. Risk Assess."},{"issue":"2","key":"10.1016\/j.watres.2026.126916_bib2","first-page":"99","article-title":"Developing a water quality management model for Karun and Dez Rivers. Iran","volume":"4","author":"Afkhami","year":"2007","journal-title":"J. Environ. Health Sci. Engineer."},{"key":"10.1016\/j.watres.2026.126916_bib3","doi-asserted-by":"crossref","DOI":"10.1016\/j.jclepro.2024.140715","article-title":"Applications of machine learning to water resources management: a review of present status and future opportunities","volume":"441","author":"Ahmed","year":"2024","journal-title":"J. Clean. Prod."},{"issue":"1","key":"10.1016\/j.watres.2026.126916_bib4","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-023-36580-9","article-title":"Spatial distribution, sources and health risk assessment of heavy metals in topsoil around oil and natural gas drilling sites, Andhra Pradesh India","volume":"13","author":"Aradhi","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.watres.2026.126916_bib5","article-title":"A GIS-based geomorphological framework for estimating river reaeration coefficients and hypoxia risk screening in data-scarce basins","volume":"23","author":"Arzhangi","year":"2026","journal-title":"J. Hazard. Mater. Adv."},{"issue":"2","key":"10.1016\/j.watres.2026.126916_bib6","doi-asserted-by":"crossref","first-page":"20","DOI":"10.3390\/environments6020020","article-title":"Identification of critical source areas (CSAs) and evaluation of best management practices (BMPs) in controlling eutrophication in the Dez River Basin","volume":"6","author":"Babaei","year":"2019","journal-title":"Environments"},{"issue":"21","key":"10.1016\/j.watres.2026.126916_bib7","doi-asserted-by":"crossref","first-page":"3099","DOI":"10.3390\/w16213099","article-title":"A river water quality prediction method based on dual signal decomposition and deep learning","volume":"16","author":"Bai","year":"2024","journal-title":"Water"},{"key":"10.1016\/j.watres.2026.126916_bib8","first-page":"281","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J. Mach. Learn. Res."},{"issue":"4","key":"10.1016\/j.watres.2026.126916_bib9","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1007\/s10661-020-8225-2","article-title":"Mercury, arsenic, lead and cadmium in waters of the Singrauli coal mining and power plants industrial zone, central East India","volume":"192","author":"Bhardwaj","year":"2020","journal-title":"Environ. Monit. Assess."},{"issue":"2","key":"10.1016\/j.watres.2026.126916_bib10","article-title":"Hyperparameter optimization: foundations, algorithms, best practices, and open challenges","volume":"13","author":"Bischl","year":"2023","journal-title":"WIRES Data Min. Knowl. Discov."},{"key":"10.1016\/j.watres.2026.126916_bib11","doi-asserted-by":"crossref","DOI":"10.1016\/j.jwpe.2024.105598","article-title":"Hybridized machine learning models for phosphate pollution modeling in water systems for multiple uses","volume":"64","author":"Boratto","year":"2024","journal-title":"J. Water Process Eng."},{"issue":"1","key":"10.1016\/j.watres.2026.126916_bib12","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.watres.2026.126916_bib13","first-page":"477","article-title":"Boosting algorithms: regularization, prediction and model fitting","volume":"22","author":"B\u00fchlmann","year":"2007","journal-title":"Stat. Sci."},{"key":"10.1016\/j.watres.2026.126916_bib14","doi-asserted-by":"crossref","DOI":"10.1016\/j.watres.2023.120895","article-title":"A coupled model to improve river water quality prediction towards addressing non-stationarity and data limitation","volume":"248","author":"Chen","year":"2024","journal-title":"Water Res."},{"key":"10.1016\/j.watres.2026.126916_bib15","series-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"785","article-title":"XGBoost: a scalable tree boosting system","author":"Chen","year":"2016"},{"key":"10.1016\/j.watres.2026.126916_bib16","series-title":"2nd IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China","first-page":"1714","article-title":"Variable selection based on maximum information coefficient for data modeling","author":"Chu","year":"2017"},{"issue":"3","key":"10.1016\/j.watres.2026.126916_bib17","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1023\/A:1022627411411","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"issue":"2","key":"10.1016\/j.watres.2026.126916_bib18","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1007\/s40808-025-02291-5","article-title":"Innovative approaches to surface water quality management: advancing nitrate (NO3) forecasting with hybrid CNN-LSTM and CNN-GRU techniques","volume":"11","author":"Davoudi","year":"2025","journal-title":"Model. Earth Syst. Environ."},{"key":"10.1016\/j.watres.2026.126916_bib19","doi-asserted-by":"crossref","DOI":"10.1016\/j.jwpe.2025.108013","article-title":"Optimization of Graph Wavenet model for dissolved oxygen prediction using self-distillation and whale optimization algorithm","volume":"75","author":"Ding","year":"2025","journal-title":"J. Water Process Eng."},{"issue":"1","key":"10.1016\/j.watres.2026.126916_bib20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v033.i01","article-title":"Regularization paths for generalized linear models via coordinate descent","volume":"33","author":"Friedman","year":"2010","journal-title":"J. Stat. Softw."},{"key":"10.1016\/j.watres.2026.126916_bib21","doi-asserted-by":"crossref","DOI":"10.1016\/j.envpol.2025.126801","article-title":"Prediction of water quality parameters and pollution exceedance analysis in typical rivers of semi-arid regions based on interpretable deep learning models","volume":"383","author":"Gao","year":"2025","journal-title":"Environ. Pollut."},{"issue":"6","key":"10.1016\/j.watres.2026.126916_bib22","doi-asserted-by":"crossref","DOI":"10.1007\/s00477-026-03287-y","article-title":"Hotspots and uncertainties in agricultural phosphorus runoff from synthetic fertilizers: a global assessment","volume":"40","author":"Golkar","year":"2026","journal-title":"Stoch. Environ. Res. Risk Assess."},{"issue":"12","key":"10.1016\/j.watres.2026.126916_bib23","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0337830","article-title":"Improved predictive formulae for wave overtopping at sloped breakwaters using interpretable machine learning models","volume":"20","author":"Habib","year":"2025","journal-title":"PLoS One"},{"issue":"2","key":"10.1016\/j.watres.2026.126916_bib24","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1162\/106365601750190398","article-title":"Completely derandomized self-adaptation in evolution strategies","volume":"9","author":"Hansen","year":"2001","journal-title":"Evol. Comput."},{"key":"10.1016\/j.watres.2026.126916_bib25","article-title":"Weighted k-nearest-neighbor techniques and ordinal classification","volume":"399","author":"Hechenbichler","year":"2004"},{"key":"10.1016\/j.watres.2026.126916_bib26","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1016\/j.future.2019.02.028","article-title":"Harris hawks optimization: algorithm and applications","volume":"97","author":"Heidari","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"10.1016\/j.watres.2026.126916_bib27","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2024.131767","article-title":"Two-stage meta-ensembling machine learning model for enhanced water quality forecasting","volume":"641","author":"Heydari","year":"2024","journal-title":"J. Hydrol."},{"key":"10.1016\/j.watres.2026.126916_bib28","doi-asserted-by":"crossref","unstructured":"Holland, J.H. (1992). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence.","DOI":"10.7551\/mitpress\/1090.001.0001"},{"key":"10.1016\/j.watres.2026.126916_bib29","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.swevo.2018.02.013","article-title":"A novel nature-inspired algorithm for optimization: squirrel search algorithm","volume":"44","author":"Jain","year":"2019","journal-title":"Swarm. Evol. Comput."},{"key":"10.1016\/j.watres.2026.126916_bib30","first-page":"3146","article-title":"LightGBM: a highly efficient gradient boosting decision tree","volume":"30","author":"Ke","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.watres.2026.126916_bib31","series-title":"Proceedings of ICNN'95-International Conference on Neural Networks","first-page":"1942","article-title":"Particle swarm optimization","volume":"4","author":"Kennedy","year":"1995"},{"key":"10.1016\/j.watres.2026.126916_bib32","doi-asserted-by":"crossref","first-page":"6405","DOI":"10.1007\/s11356-025-36062-7","article-title":"Enhanced water quality prediction model using advanced hybridized resampling alternating tree-based and deep learning algorithms","volume":"32","author":"Khosravi","year":"2025","journal-title":"Environ. Sci. Pollut. Res."},{"key":"10.1016\/j.watres.2026.126916_bib33","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1016\/j.jhydrol.2012.01.011","article-title":"Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios","volume":"424-425","author":"Kling","year":"2012","journal-title":"J. Hydrol."},{"key":"10.1016\/j.watres.2026.126916_bib34","doi-asserted-by":"crossref","first-page":"4323","DOI":"10.5194\/hess-23-4323-2019","article-title":"Technical note: inherent benchmark or not? Comparing Nash\u2013Sutcliffe and Kling\u2013Gupta efficiency scores","volume":"23","author":"Knoben","year":"2019","journal-title":"Hydrol. Earth Syst. Sci."},{"issue":"1","key":"10.1016\/j.watres.2026.126916_bib35","article-title":"A surrogate-assisted numerical framework for predicting wave interaction with porous marine structures","volume":"20","author":"Liu","year":"2026","journal-title":"Eng. Appl. Comput. Fluid Mech."},{"key":"10.1016\/j.watres.2026.126916_bib36","doi-asserted-by":"crossref","DOI":"10.1016\/j.jenvman.2024.120777","article-title":"Analysis of dissolved oxygen influencing factors and concentration prediction using input variable selection technique: a hybrid machine learning approach","volume":"357","author":"Liu","year":"2024","journal-title":"J. Environ. Manage."},{"key":"10.1016\/j.watres.2026.126916_bib37","doi-asserted-by":"crossref","DOI":"10.1016\/j.watres.2019.115343","article-title":"Ensemble data assimilation methods for improving river water quality forecasting accuracy","volume":"171","author":"Loos","year":"2020","journal-title":"Water Res."},{"key":"10.1016\/j.watres.2026.126916_bib38","doi-asserted-by":"crossref","DOI":"10.1016\/j.chemosphere.2020.126169","article-title":"Hybrid decision tree-based machine learning models for short-term water quality prediction","volume":"249","author":"Lu","year":"2020","journal-title":"Chemosphere"},{"key":"10.1016\/j.watres.2026.126916_bib39","doi-asserted-by":"crossref","DOI":"10.1016\/j.watres.2026.125367","article-title":"Linking hypolimnion to epilimnion in a stratified arctic lake: machine learning-based estimation of hypolimnetic water quality from epilimnetic measurements","volume":"294","author":"Mahdian","year":"2026","journal-title":"Water Res."},{"issue":"13","key":"10.1016\/j.watres.2026.126916_bib40","first-page":"1","article-title":"Approaches for the short-term prediction of natural daily streamflows using hybrid machine learning enhanced with grey wolf optimization","volume":"67","author":"Martinho","year":"2022","journal-title":"Hydrol. Sci. J."},{"issue":"11","key":"10.1016\/j.watres.2026.126916_bib41","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1007\/s11270-023-06745-3","article-title":"A hybrid machine learning model for modeling nitrate concentration in water sources","volume":"234","author":"Mazraeh","year":"2023","journal-title":"Water Air Soil Pollut."},{"key":"10.1016\/j.watres.2026.126916_bib42","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","article-title":"Grey wolf optimizer","volume":"69","author":"Mirjalili","year":"2014","journal-title":"Adv. Eng. Softw."},{"issue":"1","key":"10.1016\/j.watres.2026.126916_bib43","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/s40899-024-01188-y","article-title":"Forecasting biochemical oxygen demand (BOD) in river ganga: a case study employing supervised machine learning and ANN techniques","volume":"11","author":"Mishra","year":"2025","journal-title":"Sustain. Water Resour. Manag."},{"key":"10.1016\/j.watres.2026.126916_bib44","series-title":"IFIP Technical Conference on Optimization Techniques","first-page":"400","article-title":"On Bayesian methods for seeking the extremum","author":"Mo\u010dkus","year":"1974"},{"key":"10.1016\/j.watres.2026.126916_bib45","doi-asserted-by":"crossref","DOI":"10.1016\/j.jenvman.2025.125600","article-title":"Sustainable water allocation under climate change: deep learning approaches to predict drinking water shortages","volume":"385","author":"Moghaddam","year":"2025","journal-title":"J. Environ. Manage."},{"key":"10.1016\/j.watres.2026.126916_bib46","doi-asserted-by":"crossref","DOI":"10.1016\/j.rineng.2025.105421","article-title":"Advanced water quality assessment using machine learning: source identification and probabilistic health risk analysis","volume":"27","author":"Mohammadpour","year":"2025","journal-title":"Results Eng."},{"key":"10.1016\/j.watres.2026.126916_bib47","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2024.173743","article-title":"Enhancing machine learning models for total organic carbon prediction by integrating geospatial parameters in river watersheds","volume":"943","author":"Oh","year":"2024","journal-title":"Sci. Total Environ."},{"issue":"2","key":"10.1016\/j.watres.2026.126916_bib48","doi-asserted-by":"crossref","first-page":"2717","DOI":"10.1007\/s10668-024-05075-6","article-title":"Evaluation of water quality based on artificial intelligence: performance of multilayer perceptron neural networks and multiple linear regression versus water quality indexes","volume":"28","author":"Palab\u0131y\u0131k","year":"2026","journal-title":"Environ. Dev. Sustain."},{"key":"10.1016\/j.watres.2026.126916_bib49","series-title":"5th Australian Joint Conference on Artificial Intelligence, Hobart, Tasmania","first-page":"343","article-title":"Learning with continuous classes","volume":"92","author":"Quinlan","year":"1992"},{"key":"10.1016\/j.watres.2026.126916_bib50","series-title":"R: A Language and Environment for Statistical Computing","year":"2026"},{"key":"10.1016\/j.watres.2026.126916_bib51","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2023.165504","article-title":"Remote sensing-enabled machine learning for river water quality modeling under multidimensional uncertainty","volume":"898","author":"Rahat","year":"2023","journal-title":"Sci. Total Environ."},{"issue":"6062","key":"10.1016\/j.watres.2026.126916_bib52","doi-asserted-by":"crossref","first-page":"1518","DOI":"10.1126\/science.1205438","article-title":"Detecting novel associations in large data sets","volume":"334","author":"Reshef","year":"2011","journal-title":"Science"},{"key":"10.1016\/j.watres.2026.126916_bib53","series-title":"Pattern Recognition and Neural Networks","author":"Ripley","year":"1996"},{"issue":"8","key":"10.1016\/j.watres.2026.126916_bib54","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1111\/ecog.02881","article-title":"Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure","volume":"40","author":"Roberts","year":"2017","journal-title":"Ecography"},{"issue":"6","key":"10.1016\/j.watres.2026.126916_bib55","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 a case study","volume":"220","author":"Singh","year":"2009","journal-title":"Ecol. Modell."},{"issue":"9","key":"10.1016\/j.watres.2026.126916_bib56","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.3390\/w13091179","article-title":"Inversion of chlorophyll-a concentration in Donghu Lake based on machine learning algorithm","volume":"13","author":"Tang","year":"2021","journal-title":"Water"},{"key":"10.1016\/j.watres.2026.126916_bib57","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2023.129356","article-title":"A parsimonious model for predicting the NO3\u2212-N concentration in shallow groundwater in intensive agricultural areas using few easily accessible indicators and small datasets based on machine learning","volume":"619","author":"Wang","year":"2023","journal-title":"J. Hydrol."},{"issue":"4","key":"10.1016\/j.watres.2026.126916_bib58","doi-asserted-by":"crossref","first-page":"610","DOI":"10.3390\/w14040610","article-title":"A hybrid model for water quality prediction based on an artificial neural network, wavelet transform, and long short-term memory","volume":"14","author":"Wu","year":"2022","journal-title":"Water"},{"key":"10.1016\/j.watres.2026.126916_bib59","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2023.168521","article-title":"Predictive modeling of nitrogen and phosphorus concentrations in rivers using a machine learning framework: a case study in an urban-rural transitional area in Wenzhou China","volume":"910","author":"Xue","year":"2024","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.watres.2026.126916_bib60","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.neucom.2020.07.061","article-title":"On hyperparameter optimization of machine learning algorithms: theory and practice","volume":"415","author":"Yang","year":"2020","journal-title":"Neurocomputing"},{"key":"10.1016\/j.watres.2026.126916_bib61","doi-asserted-by":"crossref","DOI":"10.1016\/j.jclepro.2021.126783","article-title":"Effect of hydrogeological conditions on groundwater nitrate pollution and human health risk assessment of nitrate in Jiaokou irrigation district","volume":"298","author":"Zhang","year":"2021","journal-title":"J. Clean. Prod."},{"issue":"1","key":"10.1016\/j.watres.2026.126916_bib62","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1007\/s11269-023-03666-y","article-title":"Recent advances in surface water quality prediction using artificial intelligence models","volume":"38","author":"Zhang","year":"2024","journal-title":"Water Resour. Manag."},{"key":"10.1016\/j.watres.2026.126916_bib63","doi-asserted-by":"crossref","DOI":"10.1016\/j.envres.2023.115259","article-title":"Improved prediction of chlorophyll-a concentrations in reservoirs by GRU neural network based on particle swarm algorithm optimized variational modal decomposition","volume":"221","author":"Zhang","year":"2023","journal-title":"Environ. Res."},{"key":"10.1016\/j.watres.2026.126916_bib64","doi-asserted-by":"crossref","first-page":"6662","DOI":"10.1038\/srep06662","article-title":"A novel algorithm for the precise calculation of the maximal information coefficient","volume":"4","author":"Zhang","year":"2014","journal-title":"Sci. Rep."}],"container-title":["Water Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0043135426015897?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0043135426015897?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T21:00:25Z","timestamp":1790197225000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0043135426015897"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2027,1]]},"references-count":64,"alternative-id":["S0043135426015897"],"URL":"https:\/\/doi.org\/10.1016\/j.watres.2026.126916","relation":{},"ISSN":["0043-1354"],"issn-type":[{"value":"0043-1354","type":"print"}],"subject":[],"published":{"date-parts":[[2027,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"From low-cost measurements to rapid water quality monitoring: a hybrid machine learning\u2013optimization framework for predicting laboratory-intensive parameters","name":"articletitle","label":"Article Title"},{"value":"Water Research","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.watres.2026.126916","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":"126916"}}