{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T04:22:28Z","timestamp":1791951748020,"version":"4.3.4"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"DOI":"10.1007\/s44163-025-00454-y","type":"journal-article","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:36:19Z","timestamp":1754566579000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Water quality prediction model based on improved long short-term memory neural network and empirical mode decomposition"],"prefix":"10.1007","volume":"5","author":[{"given":"Feng","family":"Lin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,7]]},"reference":[{"issue":"1","key":"454_CR1","first-page":"46","volume":"18","author":"AM Anusha Bamini","year":"2024","unstructured":"Anusha Bamini AM, Chitra R, Agarwal S, et al. IoT-based automatic water quality monitoring system with optimized neural network. KSII Trans Internet Inf Syst (TIIS). 2024;18(1):46\u201363.","journal-title":"KSII Trans Internet Inf Syst (TIIS)"},{"issue":"3","key":"454_CR2","doi-asserted-by":"publisher","first-page":"1281","DOI":"10.1007\/s11269-024-04033-1","volume":"39","author":"X Sheng","year":"2025","unstructured":"Sheng X, Tang Y, Yue S, et al. Characteristics and prediction of reservoir water quality under the rainfall-runoff impact by long short-term memory based encoder-decoder model. Water Resour Manage. 2025;39(3):1281\u201399.","journal-title":"Water Resour Manage"},{"issue":"3","key":"454_CR3","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1038\/s41558-023-01923-x","volume":"14","author":"L Li","year":"2024","unstructured":"Li L, Knapp JLA, Lintern A, et al. River water quality shaped by land\u2013river connectivity in a changing climate. Nat Clim Chang. 2024;14(3):225\u201337.","journal-title":"Nat Clim Chang"},{"issue":"1","key":"454_CR4","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1007\/s11269-023-03666-y","volume":"38","author":"Q Zhang","year":"2024","unstructured":"Zhang Q, You X. Recent advances in surface water quality prediction using artificial intelligence models. Water Resour Manage. 2024;38(1):235\u201350.","journal-title":"Water Resour Manage"},{"issue":"12","key":"454_CR5","doi-asserted-by":"publisher","first-page":"35307","DOI":"10.1007\/s11042-023-16737-4","volume":"83","author":"MY Shams","year":"2024","unstructured":"Shams MY, Elshewey AM, El-Kenawy ESM, et al. Water quality prediction using machine learning models based on grid search method. Multimed Tools Appl. 2024;83(12):35307\u201334.","journal-title":"Multimed Tools Appl"},{"issue":"5","key":"454_CR6","doi-asserted-by":"publisher","first-page":"11397","DOI":"10.1007\/s10668-023-03335-5","volume":"26","author":"M El-Shebli","year":"2024","unstructured":"El-Shebli M, Sharrab Y, Al-Fraihat D. Prediction and modeling of water quality using deep neural networks. Environ Dev Sustain. 2024;26(5):11397\u2013430.","journal-title":"Environ Dev Sustain"},{"issue":"9","key":"454_CR7","doi-asserted-by":"publisher","first-page":"3269","DOI":"10.2166\/ws.2024.189","volume":"24","author":"MK Elshaarawy","year":"2024","unstructured":"Elshaarawy MK, Eltarabily MG. Machine learning models for predicting water quality index: optimization and performance analysis for El Moghra. Egypt Water Supply. 2024;24(9):3269\u201394.","journal-title":"Egypt Water Supply"},{"issue":"7","key":"454_CR8","doi-asserted-by":"publisher","first-page":"2399","DOI":"10.1007\/s11269-024-03774-3","volume":"38","author":"J Pang","year":"2024","unstructured":"Pang J, Luo W, Yao Z, Chen J, Dong C, Lin K. Water quality prediction in urban waterways based on wavelet packet denoising and LSTM. Water Resour Manage. 2024;38(7):2399\u2013420.","journal-title":"Water Resour Manage"},{"issue":"6","key":"454_CR9","doi-asserted-by":"publisher","first-page":"8455","DOI":"10.1007\/s10499-024-01574-5","volume":"32","author":"GD Rahul","year":"2024","unstructured":"Rahul GD, Harigovindan VP, Rasheed AHKP, Amrtha B. Attention-driven LSTM and GRU deep learning techniques for precise water quality prediction in smart aquaculture. Aquacult Int. 2024;32(6):8455\u201378.","journal-title":"Aquacult Int"},{"issue":"21","key":"454_CR10","doi-asserted-by":"publisher","first-page":"13223","DOI":"10.1007\/s00521-024-09698-8","volume":"36","author":"KP Wai","year":"2024","unstructured":"Wai KP, Koo CH, Huang YF, Chong WC. Decomposed intrinsic mode functions and deep learning algorithms for water quality index forecasting. Neural Comput Appl. 2024;36(21):13223\u201342.","journal-title":"Neural Comput Appl"},{"issue":"1","key":"454_CR11","first-page":"60078","volume":"10","author":"KPRA Haq","year":"2022","unstructured":"Haq KPRA, Harigovindan VP. Water quality prediction for smart aquaculture using hybrid deep learning models. IEEE Access. 2022;10(1):60078\u201398.","journal-title":"IEEE Access"},{"issue":"50","key":"454_CR12","doi-asserted-by":"publisher","first-page":"75664","DOI":"10.1007\/s11356-022-21115-y","volume":"29","author":"Q Zhang","year":"2022","unstructured":"Zhang Q, Wang R, Qi Y, et al. A watershed water quality prediction model based on attention mechanism and Bi-LSTM. Environ Sci Pollut Res. 2022;29(50):75664\u201380.","journal-title":"Environ Sci Pollut Res"},{"issue":"4","key":"454_CR13","doi-asserted-by":"publisher","first-page":"4082","DOI":"10.2166\/ws.2022.038","volume":"22","author":"JH Lee","year":"2022","unstructured":"Lee JH, Lee JY, Lee MH, et al. Development of a short-term water quality prediction model for urban rivers using real-time water quality data. Water Supply. 2022;22(4):4082\u201397.","journal-title":"Water Supply"},{"issue":"10","key":"454_CR14","doi-asserted-by":"publisher","first-page":"3715","DOI":"10.1007\/s11269-022-03224-y","volume":"36","author":"L Zhang","year":"2022","unstructured":"Zhang L, Jiang Z, He S, et al. Study on water quality prediction of urban reservoir by coupled CEEMDAN decomposition and LSTM neural network model. Water Resour Manage. 2022;36(10):3715\u201335.","journal-title":"Water Resour Manage"},{"issue":"25","key":"454_CR15","doi-asserted-by":"publisher","first-page":"38066","DOI":"10.1007\/s11356-022-18757-3","volume":"29","author":"C Song","year":"2022","unstructured":"Song C, Yao L. Application of artificial intelligence based on synchrosqueezed wavelet transform and improved deep extreme learning machine in water quality prediction. Environ Sci Pollut Res. 2022;29(25):38066\u201382.","journal-title":"Environ Sci Pollut Res"},{"issue":"2","key":"454_CR16","doi-asserted-by":"publisher","first-page":"2007","DOI":"10.32604\/csse.2023.037723","volume":"47","author":"S Sikandar","year":"2023","unstructured":"Sikandar S, Mahum R, Aladhadh S. Automatic crop expert system using improved LSTM with attention block. Comput Syst Sci Eng. 2023;47(2):2007\u201325.","journal-title":"Comput Syst Sci Eng"},{"issue":"1","key":"454_CR17","doi-asserted-by":"publisher","first-page":"32382","DOI":"10.1007\/s11356-024-33027-0","volume":"31","author":"T Hai","year":"2024","unstructured":"Hai T, Ahmadianfar I, Halder B, et al. Surface water quality index forecasting using a multivariate complementing approach reinforced with a locally weighted linear regression model. Environ Sci Pollut Res. 2024;31(1):32382\u2013406.","journal-title":"Environ Sci Pollut Res"},{"issue":"3","key":"454_CR18","doi-asserted-by":"publisher","first-page":"1943","DOI":"10.32604\/iasc.2022.029660","volume":"34","author":"W Tan","year":"2022","unstructured":"Tan W, Zhang J, Wu J, et al. Application of CNN and long short-term memory network in water quality predicting. Intell Autom Soft Comput. 2022;34(3):1943\u201358.","journal-title":"Intell Autom Soft Comput"},{"issue":"1","key":"454_CR19","doi-asserted-by":"publisher","first-page":"960","DOI":"10.11591\/ijece.v14i1.pp960-970","volume":"14","author":"N Nikentari","year":"2024","unstructured":"Nikentari N, Wei HL. Multi-task learning using non-linear autoregressive models and recurrent neural networks for tide level forecasting. Int J Electr Comput Eng (IJECE). 2024;14(1):960\u201370.","journal-title":"Int J Electr Comput Eng (IJECE)"},{"issue":"1","key":"454_CR20","doi-asserted-by":"publisher","first-page":"13231","DOI":"10.3390\/su142013231","volume":"14","author":"H Chen","year":"2022","unstructured":"Chen H, Yang J, Fu X, et al. Water quality prediction based on LSTM and attention mechanism: a case study of the Burnett River, Australia. Sustainability. 2022;14(1):13231\u201313231.","journal-title":"Sustainability"},{"issue":"1","key":"454_CR21","doi-asserted-by":"publisher","first-page":"2069","DOI":"10.1016\/j.procs.2024.04.196","volume":"235","author":"D Dayakaran","year":"2024","unstructured":"Dayakaran D, Kadiresan N. Federated learning framework for human activity recognition using smartphones. Proced Comput Sci. 2024;235(1):2069\u201378.","journal-title":"Proced Comput Sci"},{"issue":"1","key":"454_CR22","doi-asserted-by":"publisher","first-page":"6045","DOI":"10.1007\/s11269-024-03943-4","volume":"38","author":"HC Kilinc","year":"2024","unstructured":"Kilinc HC, Apak S, Ozkan F, et al. Multimodal fusion of optimized GRU\u2013LSTM with self-attention layer for hydrological time series forecasting. Water Resour Manage. 2024;38(1):6045\u201362.","journal-title":"Water Resour Manage"},{"issue":"2","key":"454_CR23","first-page":"1168","volume":"139","author":"S Amin","year":"2024","unstructured":"Amin S, Zeb MA, Alshahrani H, et al. Social media-based surveillance systems for health informatics using machine and deep learning techniques: a comprehensive review and open challenges. CMES-Comput Model Eng Sci. 2024;139(2):1168\u2013202.","journal-title":"CMES-Comput Model Eng Sci"},{"issue":"42","key":"454_CR24","doi-asserted-by":"publisher","first-page":"63640","DOI":"10.1007\/s11356-022-20293-z","volume":"29","author":"Y Hou","year":"2022","unstructured":"Hou Y, Zhang A, Lv R, et al. A study on water quality parameters estimation for urban rivers based on ground hyperspectral remote sensing technology. Environ Sci Pollut Res. 2022;29(42):63640\u201354.","journal-title":"Environ Sci Pollut Res"},{"issue":"2","key":"454_CR25","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1002\/for.2905","volume":"42","author":"Y Dong","year":"2023","unstructured":"Dong Y, Wang J, Niu X, et al. Combined water quality forecasting system based on multiobjective optimization and improved data decomposition integration strategy. J Forecast. 2023;42(2):260\u201387.","journal-title":"J Forecast"},{"issue":"2","key":"454_CR26","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1002\/rra.3902","volume":"38","author":"DA de Santos","year":"2022","unstructured":"de Santos DA, Martins AC, da Silva KM, et al. Identifying water factors that are related to ammonia nitrogen concentrations in Columbia River using a reversed hazard exponential model. River Res Appl. 2022;38(2):256\u201366.","journal-title":"River Res Appl"}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00454-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-025-00454-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00454-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T18:12:41Z","timestamp":1757355161000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-025-00454-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"references-count":26,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["454"],"URL":"https:\/\/doi.org\/10.1007\/s44163-025-00454-y","relation":{},"ISSN":["2731-0809"],"issn-type":[{"value":"2731-0809","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,7]]},"assertion":[{"value":"21 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 August 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"199"}}