{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T00:21:42Z","timestamp":1789950102582,"version":"4.0.1"},"reference-count":72,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T00:00:00Z","timestamp":1731369600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52293445"],"award-info":[{"award-number":["52293445"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52321005"],"award-info":[{"award-number":["52321005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010877","name":"Science, Technology and Innovation Commission of Shenzhen Municipality","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010877","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017610","name":"Shenzhen Science and Technology Innovation Program","doi-asserted-by":"publisher","award":["KQTD20190929172630447"],"award-info":[{"award-number":["KQTD20190929172630447"]}],"id":[{"id":"10.13039\/501100017610","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017610","name":"Shenzhen Science and Technology Innovation Program","doi-asserted-by":"publisher","award":["KCXFZ20211020163404007"],"award-info":[{"award-number":["KCXFZ20211020163404007"]}],"id":[{"id":"10.13039\/501100017610","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Environmental Science and Ecotechnology"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1016\/j.ese.2024.100512","type":"journal-article","created":{"date-parts":[[2024,11,17]],"date-time":"2024-11-17T15:16:50Z","timestamp":1731856610000},"page":"100512","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":20,"special_numbering":"C","title":["Augmented machine learning for sewage quality assessment with limited data"],"prefix":"10.1016","volume":"23","author":[{"given":"Jia-Qiang","family":"Lv","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wan-Xin","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8342-0275","authenticated-orcid":false,"given":"Jia-Min","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao-Yi","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi-Ling","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji-Xian","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ai-Jie","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6555-1722","authenticated-orcid":false,"given":"Hong-Cheng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ese.2024.100512_bib1","first-page":"247","article-title":"Multifaceted benefits of magnesium hydroxide dosing in sewer systems: impacts on downstream wastewater treatment processes","author":"Cen","year":"2023","journal-title":"Water Res."},{"issue":"3","key":"10.1016\/j.ese.2024.100512_bib2","doi-asserted-by":"crossref","first-page":"1270","DOI":"10.1021\/acs.est.7b05121","article-title":"Co-variation between distribution of microbial communities and biological metabolization of organics in urban sewer systems","volume":"52","author":"Jin","year":"2018","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.ese.2024.100512_bib3","article-title":"Virtual sample generation empowers machine learning-based effluent prediction in constructed wetlands","volume":"346","author":"Dong","year":"2023","journal-title":"J.\u00a0Environ. Manag."},{"issue":"6198","key":"10.1016\/j.ese.2024.100512_bib4","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1126\/science.1251418","article-title":"Reducing sewer corrosion through integrated urban water management","volume":"345","author":"Pikaar","year":"2014","journal-title":"Science"},{"issue":"3","key":"10.1016\/j.ese.2024.100512_bib5","doi-asserted-by":"crossref","DOI":"10.1007\/s11783-018-1023-1","article-title":"Current state and future perspectives of sewer networks in urban China","volume":"12","author":"Huang","year":"2018","journal-title":"Front. Environ. Sci. Eng."},{"key":"10.1016\/j.ese.2024.100512_bib6","doi-asserted-by":"crossref","DOI":"10.1016\/j.watres.2023.120077","article-title":"In-situ advanced oxidation of sediment iron for sulfide control in sewers","volume":"240","author":"Liu","year":"2023","journal-title":"Water Res."},{"key":"10.1016\/j.ese.2024.100512_bib7","first-page":"240","article-title":"Hydrogen sulfide control in sewer systems: a critical review of recent progress","author":"Zhang","year":"2023","journal-title":"Water Res."},{"issue":"43","key":"10.1016\/j.ese.2024.100512_bib8","doi-asserted-by":"crossref","first-page":"16522","DOI":"10.1021\/acs.est.3c03665","article-title":"Highly efficient coremoval of nitrate and phosphate driven by a sulfur-siderite composite reactive filler toward secondary effluent polishing","volume":"57","author":"Xu","year":"2023","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.ese.2024.100512_bib9","doi-asserted-by":"crossref","DOI":"10.1016\/j.watres.2024.121576","article-title":"Deciphering carbon emissions in urban sewer networks: bridging urban sewer networks with city-wide environmental dynamics","volume":"256","author":"Chen","year":"2024","journal-title":"Water Res."},{"key":"10.1016\/j.ese.2024.100512_bib10","doi-asserted-by":"crossref","DOI":"10.1186\/s13068-019-1441-8","article-title":"Non-negligible greenhouse gases from urban sewer system","volume":"12","author":"Jin","year":"2019","journal-title":"Biotechnol. Biofuels"},{"key":"10.1016\/j.ese.2024.100512_bib11","doi-asserted-by":"crossref","DOI":"10.1016\/j.watres.2022.118973","article-title":"The role of deep learning in urban water management: a critical review","volume":"223","author":"Fu","year":"2022","journal-title":"Water Res."},{"issue":"19","key":"10.1016\/j.ese.2024.100512_bib12","first-page":"12741","article-title":"Machine learning: new ideas and tools in environmental science and engineering","volume":"55","author":"Zhong","year":"2021","journal-title":"Environ. Sci. Technol."},{"issue":"4","key":"10.1016\/j.ese.2024.100512_bib13","doi-asserted-by":"crossref","first-page":"2124","DOI":"10.1021\/acs.est.1c06157","article-title":"Data-driven machine learning in environmental pollution: gains and problems","volume":"56","author":"Liu","year":"2022","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.ese.2024.100512_bib14","doi-asserted-by":"crossref","DOI":"10.1016\/j.ese.2023.100333","article-title":"Knowledge-guided machine learning reveals pivotal drivers for gas-to-particle conversion of atmospheric nitrate","volume":"19","author":"Xu","year":"2024","journal-title":"Environ. Sci. Ecotechnol."},{"issue":"3","key":"10.1016\/j.ese.2024.100512_bib15","doi-asserted-by":"crossref","first-page":"487","DOI":"10.2166\/wh.2024.249","article-title":"A\u00a0machine learning approach for predicting and localizing the failure and damage point in sewer networks due to pipe properties","volume":"22","author":"Goodarzi","year":"2024","journal-title":"J.\u00a0Water Health."},{"key":"10.1016\/j.ese.2024.100512_bib16","first-page":"144","article-title":"Proactive exfiltration severity management in sewer networks: a hyperparameter optimization for two-tiered machine learning prediction","author":"Ma","year":"2024","journal-title":"Tunn. Undergr. Space Technol."},{"key":"10.1016\/j.ese.2024.100512_bib17","first-page":"1","article-title":"Artificial Intelligence of Things (AIoT)-oriented framework for blockage assessment at cross-drainage hydraulic structures","author":"Iqbal","year":"2023","journal-title":"Aust. J. Water Resour."},{"issue":"6","key":"10.1016\/j.ese.2024.100512_bib18","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1016\/j.ijsrc.2023.07.003","article-title":"Ensemble and optimized hybrid algorithms through Runge Kutta optimizer for sewer sediment transport modeling using a data pre-processing approach","volume":"38","author":"Gul","year":"2023","journal-title":"Int. J. Sediment Res."},{"key":"10.1016\/j.ese.2024.100512_bib19","first-page":"628","article-title":"Forecasting and optimization for minimizing combined sewer overflows using Machine learning frameworks and its inversion techniques","author":"Yin","year":"2024","journal-title":"J.\u00a0Hydrol."},{"issue":"4","key":"10.1016\/j.ese.2024.100512_bib20","doi-asserted-by":"crossref","DOI":"10.1061\/JOEEDU.EEENG-7543","article-title":"Fat, oil, and grease sewer waste management system: a modeling platform for simulating the formation of FOG deposits in sewer networks","volume":"150","author":"Simmons","year":"2024","journal-title":"J.\u00a0Environ. Eng."},{"issue":"4","key":"10.1016\/j.ese.2024.100512_bib21","doi-asserted-by":"crossref","first-page":"338","DOI":"10.5006\/4457","article-title":"Predicting microbiologically influenced concrete corrosion in self-cleansing sewers using meta-learning techniques","volume":"80","author":"Zounemat-Kermani","year":"2024","journal-title":"Corrosion"},{"key":"10.1016\/j.ese.2024.100512_bib22","first-page":"947","article-title":"Machine learning for high-precision simulation of dissolved organic matter in sewer: overcoming data restrictions with generative adversarial networks","author":"Hou","year":"2024","journal-title":"Sci. Total Environ."},{"issue":"5","key":"10.1016\/j.ese.2024.100512_bib23","doi-asserted-by":"crossref","first-page":"1433","DOI":"10.2166\/wst.2024.282","article-title":"Modelling an urban wastewater system via a space-time multivariate calibration to understand and improve water bodies quality","volume":"90","author":"Mendes","year":"2024","journal-title":"Water Sci. Technol."},{"issue":"2","key":"10.1016\/j.ese.2024.100512_bib24","doi-asserted-by":"crossref","first-page":"1920","DOI":"10.1109\/TII.2023.3281835","article-title":"Enhanced deep predictive modelling of wastewater plants with limited data","volume":"20","author":"Alvi","year":"2023","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.ese.2024.100512_bib25","doi-asserted-by":"crossref","DOI":"10.1016\/j.ese.2024.100402","article-title":"Water quality prediction based on sparse dataset using enhanced machine learning","volume":"20","author":"Huang","year":"2024","journal-title":"Environ. Sci. Ecotechnol."},{"issue":"10","key":"10.1016\/j.ese.2024.100512_bib26","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1093\/jamia\/ocy068","article-title":"Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review","volume":"25","author":"Xiao","year":"2018","journal-title":"J.\u00a0Am. Med. Inf. Assoc."},{"key":"10.1016\/j.ese.2024.100512_bib27","doi-asserted-by":"crossref","DOI":"10.1016\/j.ese.2022.100172","article-title":"Data augmentation and machine learning techniques for control strategy development in bio-polymerization process","volume":"11","author":"Wei","year":"2022","journal-title":"Environ. Sci. Ecotechnol."},{"key":"10.1016\/j.ese.2024.100512_bib28","doi-asserted-by":"crossref","DOI":"10.1016\/j.watres.2023.120409","article-title":"A\u00a0freshwater algae classification system based on machine learning with StyleGAN2-ADA augmentation for limited and imbalanced datasets","volume":"243","author":"Chan","year":"2023","journal-title":"Water Res."},{"key":"10.1016\/j.ese.2024.100512_bib29","doi-asserted-by":"crossref","first-page":"776","DOI":"10.1016\/j.scitotenv.2017.05.225","article-title":"Simultaneous use of caustic and oxygen for efficient sulfide control in sewers","volume":"601","author":"Lin","year":"2017","journal-title":"Sci. Total Environ."},{"issue":"9","key":"10.1016\/j.ese.2024.100512_bib30","doi-asserted-by":"crossref","first-page":"11","DOI":"10.2166\/wst.2002.0194","article-title":"Integrated design of sewers and wastewater treatment plants","volume":"46","author":"Vollertsen","year":"2002","journal-title":"Water Sci. Technol."},{"key":"10.1016\/j.ese.2024.100512_bib31","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.watres.2017.11.007","article-title":"Modelling the long-term effect of wastewater compositions on maximum sulfide and methane production rates of sewer biofilm","volume":"129","author":"Sun","year":"2018","journal-title":"Water Res."},{"key":"10.1016\/j.ese.2024.100512_bib32","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.watres.2019.03.061","article-title":"Effects of food waste addition on biofilm formation and sulfide production in a gravity sewer","volume":"157","author":"Zan","year":"2019","journal-title":"Water Res."},{"key":"10.1016\/j.ese.2024.100512_bib33","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1016\/j.jes.2021.12.038","article-title":"Experimental and modeling investigations on the unexpected hydrogen sulfide rebound in a sewer receiving nitrate addition: mechanism and solution","volume":"125","author":"Liang","year":"2023","journal-title":"J.\u00a0Environ. Sci."},{"key":"10.1016\/j.ese.2024.100512_bib34","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.watres.2019.01.033","article-title":"Systematic evaluation of a dynamic sewer process model for prediction of odor formation and mitigation in large-scale pressurized sewers in Hong Kong","volume":"154","author":"Liang","year":"2019","journal-title":"Water Res."},{"key":"10.1016\/j.ese.2024.100512_bib35","doi-asserted-by":"crossref","DOI":"10.3389\/fmicb.2024.1377047","article-title":"Development of an in\u00a0vitro biofilm model for the study of the impact of fluoroquinolones on sewer biofilm microbiota","volume":"15","author":"Naudin","year":"2024","journal-title":"Front. Microbiol."},{"key":"10.1016\/j.ese.2024.100512_bib36","series-title":"Computational Intelligence Techniques for Bioprocess Modelling, Supervision and Control","first-page":"1","article-title":"Computational intelligence techniques as tools for bioprocess modelling, optimization, supervision and control","author":"Nicoletti","year":"2009"},{"issue":"1","key":"10.1016\/j.ese.2024.100512_bib37","doi-asserted-by":"crossref","DOI":"10.1038\/s41467-023-40380-0","article-title":"A\u00a0neural-mechanistic hybrid approach improving the predictive power of genome-scale metabolic models","volume":"14","author":"Faure","year":"2023","journal-title":"Nat. Commun."},{"key":"10.1016\/j.ese.2024.100512_bib38","doi-asserted-by":"crossref","DOI":"10.1016\/j.wroa.2024.100234","article-title":"Making waves: knowledge and data fusion in urban water modelling","volume":"24","author":"Duan","year":"2024","journal-title":"Water Res. X"},{"key":"10.1016\/j.ese.2024.100512_bib39","doi-asserted-by":"crossref","DOI":"10.1016\/j.wroa.2024.100230","article-title":"Integrating machine learning algorithm with sewer process model to realize swift prediction and real-time control of H2S pollution in sewer systems","volume":"23","author":"Liang","year":"2024","journal-title":"Water Res. X"},{"issue":"6\u20137","key":"10.1016\/j.ese.2024.100512_bib40","doi-asserted-by":"crossref","first-page":"1421","DOI":"10.1016\/j.watres.2007.10.014","article-title":"Methane formation in sewer systems","volume":"42","author":"Guisasola","year":"2008","journal-title":"Water Res."},{"issue":"4","key":"10.1016\/j.ese.2024.100512_bib41","doi-asserted-by":"crossref","first-page":"2816","DOI":"10.1021\/acs.est.1c05020","article-title":"An integrated first principal and deep learning approach for modeling nitrous oxide emissions from wastewater treatment plants","volume":"56","author":"Li","year":"2022","journal-title":"Environ. Sci. Technol."},{"issue":"46","key":"10.1016\/j.ese.2024.100512_bib42","doi-asserted-by":"crossref","first-page":"18246","DOI":"10.1021\/acs.est.3c05104","article-title":"Graph machine learning for improved imputation of missing tropospheric ozone data","volume":"57","author":"Betancourt","year":"2023","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.ese.2024.100512_bib43","doi-asserted-by":"crossref","DOI":"10.1016\/j.watres.2022.118166","article-title":"Hybrid differential equations: integrating mechanistic and data-driven techniques for modelling of water systems","volume":"213","author":"Quaghebeur","year":"2022","journal-title":"Water Res."},{"issue":"2","key":"10.1016\/j.ese.2024.100512_bib44","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1039\/D1EW00739D","article-title":"Emerging investigator series: modeling of wastewater treatment bioprocesses: current development and future opportunities","volume":"8","author":"Yao","year":"2022","journal-title":"Environ. Sci. Water Res. Technol."},{"key":"10.1016\/j.ese.2024.100512_bib45","doi-asserted-by":"crossref","DOI":"10.1016\/j.watres.2021.117494","article-title":"Rapid dynamic quantification of sulfide generation flux in spatially heterogeneous sediments of gravity sewers","volume":"203","author":"Zuo","year":"2021","journal-title":"Water Res."},{"key":"10.1016\/j.ese.2024.100512_bib46","series-title":"Monitoring and Analytical Methods of Water and Wastewater","year":"2016"},{"issue":"16","key":"10.1016\/j.ese.2024.100512_bib47","doi-asserted-by":"crossref","first-page":"6086","DOI":"10.1016\/j.watres.2013.07.027","article-title":"pH dynamics in sewers and its modeling","volume":"47","author":"Sharma","year":"2013","journal-title":"Water Res."},{"key":"10.1016\/j.ese.2024.100512_bib48","first-page":"393","article-title":"Enhancing effluent quality prediction in wastewater treatment plants through the integration of factor analysis and machine learning","author":"Lv","year":"2024","journal-title":"Bioresour. Technol."},{"key":"10.1016\/j.ese.2024.100512_bib49","first-page":"246","article-title":"Machine learning framework for intelligent aeration control in wastewater treatment plants: automatic feature engineering based on variation sliding layer","author":"Wang","year":"2023","journal-title":"Water Res."},{"key":"10.1016\/j.ese.2024.100512_bib50","first-page":"291","article-title":"Artificial intelligence based ensemble modeling of wastewater treatment plant using jittered data","author":"Nourani","year":"2021","journal-title":"J.\u00a0Clean. Prod."},{"key":"10.1016\/j.ese.2024.100512_bib51","doi-asserted-by":"crossref","DOI":"10.1016\/j.watres.2021.117666","article-title":"Machine learning in natural and engineered water systems","volume":"205","author":"Huang","year":"2021","journal-title":"Water Res."},{"key":"10.1016\/j.ese.2024.100512_bib52","doi-asserted-by":"crossref","DOI":"10.1016\/j.ese.2023.100321","article-title":"Data-driven interpretable analysis for polysaccharide yield prediction","volume":"19","author":"Tian","year":"2024","journal-title":"Environ. Sci. Ecotechnol."},{"key":"10.1016\/j.ese.2024.100512_bib53","doi-asserted-by":"crossref","DOI":"10.1016\/j.ese.2022.100207","article-title":"Deep learning-based prediction of effluent quality of a constructed wetland","volume":"13","author":"Yang","year":"2023","journal-title":"Environ. Sci. Ecotechnol."},{"key":"10.1016\/j.ese.2024.100512_bib54","doi-asserted-by":"crossref","DOI":"10.1016\/j.compenvurbsys.2022.101845","article-title":"Extracting spatial effects from machine learning model using local interpretation method: an example of SHAP and XGBoost","volume":"96","author":"Li","year":"2022","journal-title":"Comput. Environ. Urban Syst."},{"key":"10.1016\/j.ese.2024.100512_bib55","first-page":"832","article-title":"Interpretation of ensemble learning to predict water quality using explainable artificial intelligence","author":"Park","year":"2022","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.ese.2024.100512_bib56","doi-asserted-by":"crossref","DOI":"10.1016\/j.watres.2022.118682","article-title":"Prediction model and application of machine learning for supersaturated total dissolved gas generation in high dam discharge","volume":"220","author":"Wang","year":"2022","journal-title":"Water Res."},{"key":"10.1016\/j.ese.2024.100512_bib57","first-page":"291","article-title":"An interpretable machine learning method for supporting ecosystem management: application to species distribution models of freshwater macroinvertebrates","author":"Cha","year":"2021","journal-title":"J.\u00a0Environ. Manag."},{"issue":"11","key":"10.1016\/j.ese.2024.100512_bib58","doi-asserted-by":"crossref","first-page":"2874","DOI":"10.1016\/j.watres.2009.03.040","article-title":"Development of a model for assessing methane formation in rising main sewers","volume":"43","author":"Guisasola","year":"2009","journal-title":"Water Res."},{"key":"10.1016\/j.ese.2024.100512_bib59","first-page":"307","article-title":"Impact of reduced water consumption on sulfide and methane production in rising main sewers","volume":"154","author":"Sun","year":"2015","journal-title":"J.\u00a0Environ. Manag."},{"issue":"1","key":"10.1016\/j.ese.2024.100512_bib60","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-023-47787-1","article-title":"Study on the factors of hydrogen sulfide production from lignite bacterial sulfate reduction based on response surface method","volume":"13","author":"Deng","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ese.2024.100512_bib61","doi-asserted-by":"crossref","DOI":"10.1016\/j.engfailanal.2022.106482","article-title":"Microbial spatial distribution and corrosion evaluation in urban sewer systems with different service lives","volume":"139","author":"Yuan","year":"2022","journal-title":"Eng. Fail. Anal."},{"key":"10.1016\/j.ese.2024.100512_bib62","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.watres.2014.12.019","article-title":"Sulfide and methane production in sewer sediments","volume":"70","author":"Liu","year":"2015","journal-title":"Water Res."},{"issue":"22","key":"10.1016\/j.ese.2024.100512_bib63","doi-asserted-by":"crossref","first-page":"7042","DOI":"10.1128\/AEM.02146-14","article-title":"Stratified microbial structure and activity in sulfide- and methane-producing anaerobic sewer biofilms","volume":"80","author":"Sun","year":"2014","journal-title":"Appl. Environ. Microbiol."},{"issue":"3","key":"10.1016\/j.ese.2024.100512_bib64","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1021\/acsestengg.0c00163","article-title":"In situ exploration of the sulfidogenic process at the water-sediment interface in sewers: mechanism and implications","volume":"1","author":"Zuo","year":"2021","journal-title":"Acs Es&T Engineering"},{"key":"10.1016\/j.ese.2024.100512_bib65","first-page":"169","article-title":"Rapid and strong biocidal effect of ferrate on sulfidogenic and methanogenic sewer biofilms","author":"Yan","year":"2020","journal-title":"Water Res."},{"key":"10.1016\/j.ese.2024.100512_bib66","first-page":"415","article-title":"Sustainable engineering of sewers and sewage treatment plants for scenarios with urine diversion","author":"Freguia","year":"2021","journal-title":"J.\u00a0Hazard Mater."},{"key":"10.1016\/j.ese.2024.100512_bib67","first-page":"245","article-title":"Deep learning in wastewater treatment: a critical review","author":"Alvi","year":"2023","journal-title":"Water Res."},{"issue":"6","key":"10.1016\/j.ese.2024.100512_bib68","doi-asserted-by":"crossref","first-page":"V471","DOI":"10.1190\/geo2020-0644.1","article-title":"Reconstruction of irregular missing seismic data using conditional generative adversarial networks","volume":"86","author":"Wei","year":"2021","journal-title":"Geophysics"},{"key":"10.1016\/j.ese.2024.100512_bib69","doi-asserted-by":"crossref","DOI":"10.1016\/j.watres.2020.116103","article-title":"An integrated approach based on virtual data augmentation and deep neural networks modeling for VFA production prediction in anaerobic fermentation process","volume":"184","author":"Xu","year":"2020","journal-title":"Water Res."},{"issue":"6","key":"10.1016\/j.ese.2024.100512_bib70","doi-asserted-by":"crossref","first-page":"1694","DOI":"10.1021\/ie990557r","article-title":"Use of hybrid models in wastewater systems","volume":"39","author":"Anderson","year":"2000","journal-title":"Ind. Eng. Chem. Res."},{"key":"10.1016\/j.ese.2024.100512_bib71","doi-asserted-by":"crossref","DOI":"10.1016\/j.watres.2022.119171","article-title":"TLT: recurrent fine-tuning transfer learning for water quality long-term prediction","volume":"225","author":"Peng","year":"2022","journal-title":"Water Res."},{"issue":"3","key":"10.1016\/j.ese.2024.100512_bib72","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.jbiotec.2004.09.001","article-title":"Parallel hybrid modeling methods for a full-scale cokes wastewater treatment plant","volume":"115","author":"Lee","year":"2005","journal-title":"J.\u00a0Biotechnol."}],"container-title":["Environmental Science and Ecotechnology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2666498424001261?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2666498424001261?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T21:01:08Z","timestamp":1740171668000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2666498424001261"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":72,"alternative-id":["S2666498424001261"],"URL":"https:\/\/doi.org\/10.1016\/j.ese.2024.100512","relation":{},"ISSN":["2666-4984"],"issn-type":[{"value":"2666-4984","type":"print"}],"subject":[],"published":{"date-parts":[[2025,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Augmented machine learning for sewage quality assessment with limited data","name":"articletitle","label":"Article Title"},{"value":"Environmental Science and Ecotechnology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ese.2024.100512","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Authors. Published by Elsevier B.V. on behalf of Chinese Society for Environmental Sciences, Harbin Institute of Technology, Chinese Research Academy of Environmental Sciences.","name":"copyright","label":"Copyright"}],"article-number":"100512"}}