{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T02:46:35Z","timestamp":1777603595266,"version":"3.51.4"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"S2","license":[{"start":{"date-parts":[[2023,9,9]],"date-time":"2023-09-09T00:00:00Z","timestamp":1694217600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,9]],"date-time":"2023-09-09T00:00:00Z","timestamp":1694217600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"published-print":{"date-parts":[[2023,11]]},"DOI":"10.1007\/s10462-023-10594-1","type":"journal-article","created":{"date-parts":[[2023,9,9]],"date-time":"2023-09-09T05:01:36Z","timestamp":1694235696000},"page":"2893-2915","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Artificial intelligence-assisted water quality index determination for healthcare"],"prefix":"10.1007","volume":"56","author":[{"given":"Ankush","family":"Manocha","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sandeep Kumar","family":"Sood","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Munish","family":"Bhatia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,9]]},"reference":[{"key":"10594_CR1","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.watres.2019.04.054","volume":"159","author":"S Bindal","year":"2019","unstructured":"Bindal S, Singh CK (2019) Predicting groundwater arsenic contamination: regions at risk in highest populated state of India. Water Res 159:65\u201376","journal-title":"Water Res"},{"key":"10594_CR2","doi-asserted-by":"crossref","unstructured":"Cho K, Van Merri\u00ebnboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078","DOI":"10.3115\/v1\/D14-1179"},{"issue":"7","key":"10594_CR3","doi-asserted-by":"publisher","first-page":"1114","DOI":"10.1080\/02626667.2022.2060106","volume":"67","author":"Z Chen","year":"2022","unstructured":"Chen Z, Jiang P, Liu J, Zheng S, Shan Z, Li Z, Hmelnov AE (2022) An adaptive data cleaning framework: a case study of the water quality monitoring system in China. Hydrol Sci J 67(7):1114\u20131129","journal-title":"Hydrol Sci J"},{"key":"10594_CR4","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1007\/BF00577258","volume":"31","author":"SS Dixit","year":"1994","unstructured":"Dixit SS, Smol JP (1994) Diatoms as indicators in the environmental monitoring and assessment program-surface waters (EMAP-SW). Environ Monit Assess 31:275\u2013307","journal-title":"Environ Monit Assess"},{"key":"10594_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10661-017-6035-y","volume":"189","author":"X Du","year":"2017","unstructured":"Du X, Shao F, Wu S, Zhang H, Xu S (2017) Water quality assessment with hierarchical cluster analysis based on Mahalanobis distance. Environ Monit Assess 189:1\u201312","journal-title":"Environ Monit Assess"},{"issue":"4","key":"10594_CR6","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1007\/s10661-023-10989-1","volume":"195","author":"MA Gani","year":"2023","unstructured":"Gani MA, Sajib AM, Siddik MA, Moniruzzaman M (2023) Assessing the impact of land use and land cover on river water quality using water quality index and remote sensing techniques. Environ Monit Assess 195(4):449","journal-title":"Environ Monit Assess"},{"key":"10594_CR7","volume-title":"Deep learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT press, Cambridge"},{"key":"10594_CR8","doi-asserted-by":"crossref","unstructured":"Haque H, Labeeb K, Riha RB, Khan MNR (2021, March) IoT based water quality monitoring system by using Zigbee protocol. In 2021 International Conference on Emerging Smart Computing and Informatics (ESCI) (pp. 619-622). IEEE","DOI":"10.1109\/ESCI50559.2021.9397031"},{"key":"10594_CR9","doi-asserted-by":"crossref","unstructured":"Hawari HFB, Mokhtar MNSB, Sarang S (2022, November) Development of Real-Time Internet of Things (IoT) Based Water Quality Monitoring System. In International Conference on Artificial Intelligence for Smart Community: AISC 2020, 17-18 December, Universiti Teknologi Petronas, Malaysia (pp. 443\u2013454). Singapore: Springer Nature Singapore","DOI":"10.1007\/978-981-16-2183-3_43"},{"issue":"2","key":"10594_CR10","doi-asserted-by":"publisher","first-page":"1358","DOI":"10.1109\/JSYST.2016.2538082","volume":"12","author":"S Imen","year":"2016","unstructured":"Imen S, Chang NB, Yang YJ, Golchubian A (2016) Developing a model-based drinking water decision support system featuring remote sensing and fast learning techniques. IEEE Syst J 12(2):1358\u20131368","journal-title":"IEEE Syst J"},{"key":"10594_CR11","unstructured":"IS10500 BIS (2012) Indian standard drinking water-specification (second revision). Bureau of Indian Standards (BIS), New Delhi"},{"key":"10594_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.jenvman.2020.111077","volume":"272","author":"Q Jiang","year":"2020","unstructured":"Jiang Q, Feng C, Ding J, Bartley E, Lin Y, Fei J, Christakos G (2020) The decade long achievements of China\u2019s marine ecological civilization construction (2006\u20132016). J Environ Manag 272:111077","journal-title":"J Environ Manag"},{"key":"10594_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2021.151353","volume":"806","author":"JA Khattak","year":"2022","unstructured":"Khattak JA, Farooqi A, Hussain I, Kumar A, Singh CK, Mailloux BJ, van Geen A (2022) Groundwater fluoride across the Punjab plains of Pakistan and India: distribution and underlying mechanisms. Sci Total Environ 806:151353","journal-title":"Sci Total Environ"},{"key":"10594_CR14","unstructured":"Kristensen P, Whalley C, Zal FNN, Christiansen T (2018) European waters assessment of status and pressures 2018. EEA Report, (7\/2018)"},{"key":"10594_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.envpol.2019.113324","volume":"256","author":"A Kumar","year":"2020","unstructured":"Kumar A, Singh CK (2020) Arsenic enrichment in groundwater and associated health risk in Bari doab region of Indus basin, Punjab India. Environ Pollut 256:113324","journal-title":"Environ Pollut"},{"key":"10594_CR16","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3238123","author":"M Kumar","year":"2023","unstructured":"Kumar M, Singh T, Maurya MK, Shivhare A, Raut A, Singh PK (2023) Quality assessment and monitoring of river water using IoT infrastructure. IEEE Internet Things J. https:\/\/doi.org\/10.1109\/JIOT.2023.3238123","journal-title":"IEEE Internet Things J"},{"key":"10594_CR18","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1016\/j.jclepro.2019.02.080","volume":"219","author":"H Li","year":"2019","unstructured":"Li H, Liu G, Yang Z (2019) Improved gray water footprint calculation method based on a mass-balance model and on fuzzy synthetic evaluation. J Clean Prod 219:377\u2013390","journal-title":"J Clean Prod"},{"key":"10594_CR19","unstructured":"Libelium (2020) Wireless sensor networks with waspmote and meshlium. [Online]. Available: http:\/\/www.libelium.com\/libeliumworld\/smart-water\/"},{"issue":"1","key":"10594_CR20","doi-asserted-by":"publisher","first-page":"2001","DOI":"10.22178\/pos.30-3","volume":"4","author":"HM Maishanu","year":"2018","unstructured":"Maishanu HM, Mainasara MM, Magami IM (2018) Assessment of productivity status using carlson\u2019s TSI and fish diversity of goronyo dam, sokoto state Nigeria. Traektori\u00e2 Nauki= Path Sci 4(1):2001\u20132006","journal-title":"Traektori\u00e2 Nauki= Path Sci"},{"key":"10594_CR21","doi-asserted-by":"publisher","first-page":"502","DOI":"10.1016\/j.ijinfomgt.2019.05.020","volume":"49","author":"Q Min","year":"2019","unstructured":"Min Q, Lu Y, Liu Z, Su C, Wang B (2019) Machine learning based digital twin framework for production optimization in petrochemical industry. Int J Inf Manag 49:502\u2013519","journal-title":"Int J Inf Manag"},{"key":"10594_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecolind.2019.105470","volume":"106","author":"A Mort\u00e1gua","year":"2019","unstructured":"Mort\u00e1gua A, Vasselon V, Oliveira R, Elias C, Chardon C, Bouchez A, Almeida SF (2019) Applicability of DNA metabarcoding approach in the bioassessment of Portuguese rivers using diatoms. Ecol indic 106:105470","journal-title":"Ecol indic"},{"key":"10594_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.jwpe.2022.102920","volume":"48","author":"N Nasir","year":"2022","unstructured":"Nasir N, Kansal A, Alshaltone O, Barneih F, Sameer M, Shanableh A, Al-Shamma\u2019a A (2022) Water quality classification using machine learning algorithms. J Water Process Eng 48:102920","journal-title":"J Water Process Eng"},{"key":"10594_CR24","doi-asserted-by":"publisher","first-page":"63523","DOI":"10.1007\/s11356-020-10509-5","volume":"28","author":"S Nihalani","year":"2020","unstructured":"Nihalani S, Meeruty A (2020) Water quality index evaluation for major rivers in Gujarat. Environ Sci Pollut Res 28:63523\u201363531","journal-title":"Environ Sci Pollut Res"},{"issue":"40","key":"10594_CR25","doi-asserted-by":"publisher","first-page":"56658","DOI":"10.1007\/s11356-021-13758-0","volume":"28","author":"A Oukil","year":"2021","unstructured":"Oukil A, Soltani AA, Boutaghane H, Abdalla O, Bermad A, Hasbaia M, Boulassel MR (2021) A surrogate water quality index to assess groundwater using a unified DEA-OWA framework. Environ Sci Pollut Res 28(40):56658\u201356685","journal-title":"Environ Sci Pollut Res"},{"issue":"7","key":"10594_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2020.e04096","volume":"6","author":"S Pasika","year":"2020","unstructured":"Pasika S, Gandla ST (2020) Smart water quality monitoring system with cost-effective using IoT. Heliyon 6(7):e04096","journal-title":"Heliyon"},{"key":"10594_CR27","unstructured":"Planning Commission (2008) Eleventh five year plan 2007-2012. Government of India, 1"},{"issue":"19","key":"10594_CR28","doi-asserted-by":"publisher","first-page":"7119","DOI":"10.3390\/ijerph17197119","volume":"17","author":"J Podgorski","year":"2020","unstructured":"Podgorski J, Wu R, Chakravorty B, Polya DA (2020) Groundwater arsenic distribution in India by machine learning geospatial modeling. Int J Environ Res Public Health 17(19):7119","journal-title":"Int J Environ Res Public Health"},{"issue":"17","key":"10594_CR29","doi-asserted-by":"publisher","first-page":"9889","DOI":"10.1021\/acs.est.8b01679","volume":"52","author":"JE Podgorski","year":"2018","unstructured":"Podgorski JE, Labhasetwar P, Saha D, Berg M (2018) Prediction modeling and mapping of groundwater fluoride contamination throughout India. Environ Sci Technol 52(17):9889\u20139898","journal-title":"Environ Sci Technol"},{"key":"10594_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecolind.2020.106952","volume":"121","author":"AA Soltani","year":"2021","unstructured":"Soltani AA, Oukil A, Boutaghane H, Bermad A, Boulassel MR (2021) A new methodology for assessing water quality, based on data envelopment analysis: application to Algerian dams. Ecol Indic 121:106952","journal-title":"Ecol Indic"},{"issue":"2","key":"10594_CR31","doi-asserted-by":"publisher","first-page":"192","DOI":"10.4314\/wsa.v43i2.03","volume":"43","author":"B Utete","year":"2017","unstructured":"Utete B, Tsamba J (2017) Trophic state categorisation and assessment of water quality in Manjirenji Dam, Zimbabwe, a shallow reservoir with designated multi-purpose water uses. Water Sa 43(2):192\u2013199","journal-title":"Water Sa"},{"key":"10594_CR32","doi-asserted-by":"publisher","first-page":"1358","DOI":"10.1016\/j.scitotenv.2018.11.201","volume":"654","author":"A van Geen","year":"2019","unstructured":"van Geen A, Farooqi A, Kumar A, Khattak JA, Mushtaq N, Hussain I, Singh CK (2019) Field testing of over 30,000 wells for arsenic across 400 villages of the Punjab plains of Pakistan and India: implications for prioritizing mitigation. Sci Total Environ 654:1358\u20131363","journal-title":"Sci Total Environ"},{"issue":"4","key":"10594_CR33","doi-asserted-by":"publisher","first-page":"275","DOI":"10.3390\/w9040275","volume":"9","author":"T Wang","year":"2017","unstructured":"Wang T, Xu S, Liu J (2017) Dynamic assessment of comprehensive water quality considering the release of sediment pollution. Water 9(4):275","journal-title":"Water"},{"key":"10594_CR34","doi-asserted-by":"publisher","first-page":"1037","DOI":"10.1007\/s11071-019-04837-6","volume":"96","author":"X Wang","year":"2019","unstructured":"Wang X, Zhou Y, Zhao Z, Wang L, Xu J, Yu J (2019) A novel water quality mechanism modeling and eutrophication risk assessment method of lakes and reservoirs. Nonlinear Dyn 96:1037\u20131053","journal-title":"Nonlinear Dyn"},{"key":"10594_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2022.159678","volume":"857","author":"D Xie","year":"2023","unstructured":"Xie D, Li X, Zhou T, Feng Y (2023) Estimating the contribution of environmental variables to water quality in the postrestoration littoral zones of Taihu Lake using the APCS-MLR model. Sci Total Environ 857:159678","journal-title":"Sci Total Environ"},{"key":"10594_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecolind.2022.108561","volume":"135","author":"H Xu","year":"2022","unstructured":"Xu H, Gao Q, Yuan B (2022) Analysis and identification of pollution sources of comprehensive river water quality: evidence from two river basins in China. Ecol Indic 135:108561","journal-title":"Ecol Indic"},{"issue":"2","key":"10594_CR37","doi-asserted-by":"publisher","first-page":"2230","DOI":"10.3390\/ijerph120202230","volume":"12","author":"S Xu","year":"2015","unstructured":"Xu S, Wang T, Hu S (2015) Dynamic assessment of water quality based on a variable fuzzy pattern recognition model. Int J Environ Res Public Health 12(2):2230\u20132248","journal-title":"Int J Environ Res Public Health"},{"key":"10594_CR38","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1007\/s11069-019-03770-6","volume":"99","author":"B Yan","year":"2019","unstructured":"Yan B, Yu F, Xiao X, Wang X (2019) Groundwater quality evaluation using a classification model: a case study of Jilin City, China. Nat Hazards 99:735\u2013751","journal-title":"Nat Hazards"},{"issue":"5","key":"10594_CR39","doi-asserted-by":"publisher","first-page":"807","DOI":"10.1007\/s41742-021-00348-8","volume":"15","author":"H Yousefi","year":"2021","unstructured":"Yousefi H, Jamal Omidi M, Moridi A, Sarang A (2021) Groundwater monitoring network design using optimized DRASTIC method and capture zone analysis. Int J Environ Res 15(5):807\u2013817","journal-title":"Int J Environ Res"},{"key":"10594_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.114663","volume":"173","author":"L Yu","year":"2021","unstructured":"Yu L, Zhang C, Jiang J, Yang H, Shang H (2021) Reinforcement learning approach for resource allocation in humanitarian logistics. Expert Syst Appl 173:114663","journal-title":"Expert Syst Appl"},{"issue":"4","key":"10594_CR41","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.12892","volume":"40","author":"H Zeng","year":"2023","unstructured":"Zeng H, Dhiman G, Sharma A, Sharma A, Tselykh A (2023) An IoT and blockchain-based approach for the smart water management system in agriculture. Expert Syst 40(4):e12892","journal-title":"Expert Syst"},{"key":"10594_CR42","doi-asserted-by":"crossref","unstructured":"Zhang HX, Sauer GM, Generaux J, VanGorp C (2007, October) Application of multivariate trophic state index tool for lake nutrient TMDL development in iowa. In WEFTEC 2007 (pp. 7206-7218). Water Environment Federation","DOI":"10.2175\/193864707787223709"},{"issue":"4","key":"10594_CR43","doi-asserted-by":"publisher","first-page":"1769","DOI":"10.1109\/TR.2016.2591504","volume":"65","author":"E Zio","year":"2016","unstructured":"Zio E (2016) Some challenges and opportunities in reliability engineering. IEEE Trans Reliab 65(4):1769\u20131782","journal-title":"IEEE Trans Reliab"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-023-10594-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-023-10594-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-023-10594-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T19:23:36Z","timestamp":1699903416000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-023-10594-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,9]]},"references-count":42,"journal-issue":{"issue":"S2","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["10594"],"URL":"https:\/\/doi.org\/10.1007\/s10462-023-10594-1","relation":{},"ISSN":["0269-2821","1573-7462"],"issn-type":[{"value":"0269-2821","type":"print"},{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,9]]},"assertion":[{"value":"25 August 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 September 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}