{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T01:43:05Z","timestamp":1790732585191,"version":"4.1.0"},"reference-count":48,"publisher":"IWA Publishing","issue":"8","license":[{"start":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T00:00:00Z","timestamp":1712620800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2022YFC3203200"],"award-info":[{"award-number":["2022YFC3203200"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100022963","name":"Key Research and Development Program of Zhejiang Province","doi-asserted-by":"publisher","award":["2020C03082"],"award-info":[{"award-number":["2020C03082"]}],"id":[{"id":"10.13039\/100022963","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ningbo Young Technology Innovation Leading Talent Program","award":["2023QL028"],"award-info":[{"award-number":["2023QL028"]}]}],"content-domain":{"domain":["iwaponline.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,4,15]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Rainfall-derived inflow\/infiltration (RDII) modelling during heavy rainfall events is essential for sewer flow management. In this study, two machine learning algorithms, random forest (RF) and long short-term memory (LSTM), were developed for sewer flow prediction and RDII estimation based on field monitoring data. The study implemented feature engineering for extracting physically significant features in sewer flow modelling and investigated the importance of the relevant features. The results from two case studies indicated the superior capability of machine learning models in RDII estimation in the combined and separated sewer systems, and LSTM model outperformed the two models. Compared to traditional methods, machine learning models were capable of simulating the temporal variation in RDII processes and improved prediction accuracy for peak flows and RDII volumes in storm events.<\/jats:p>","DOI":"10.2166\/wst.2024.115","type":"journal-article","created":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T12:35:50Z","timestamp":1712666150000},"page":"1928-1945","update-policy":"https:\/\/doi.org\/10.2166\/iwapcrossmarkpolicypage","source":"Crossref","is-referenced-by-count":8,"title":["Assessment of rainfall-derived inflow and infiltration in sewer systems with machine learning approaches"],"prefix":"10.2166","volume":"89","author":[{"given":"Yong","family":"Wang","sequence":"first","affiliation":[{"name":"a School of Civil and Environmental Engineering, Ningbo University, Ningbo 315211, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9403-4508","authenticated-orcid":false,"given":"Biao","family":"Huang","sequence":"additional","affiliation":[{"name":"a School of Civil and Environmental Engineering, Ningbo University, Ningbo 315211, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David Z.","family":"Zhu","sequence":"additional","affiliation":[{"name":"a School of Civil and Environmental Engineering, Ningbo University, Ningbo 315211, China"},{"name":"b Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"945","published-online":{"date-parts":[[2024,4,9]]},"reference":[{"key":"key-10.2166\/wst.2024.115-1","doi-asserted-by":"publisher","DOI":"10.5194\/hess-25-5917-2021"},{"key":"key-10.2166\/wst.2024.115-2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-021-10944-7"},{"key":"key-10.2166\/wst.2024.115-3","doi-asserted-by":"publisher","DOI":"10.2166\/wst.2012.229"},{"key":"key-10.2166\/wst.2024.115-4","volume-title":"Using Flow Prediction Technologies to Control Sanitary Sewer Overflows","author":"Bennett","year":"1999"},{"key":"key-10.2166\/wst.2024.115-5","first-page":"8","article-title":"Real time adjustment of slow changing flow components in distributed urban runoff models","author":"Borup","year":"2011"},{"key":"key-10.2166\/wst.2024.115-6","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2019.134413"},{"key":"key-10.2166\/wst.2024.115-7","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3123304"},{"key":"key-10.2166\/wst.2024.115-8","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)EE.1943-7870.0002038"},{"key":"key-10.2166\/wst.2024.115-9","doi-asserted-by":"crossref","DOI":"10.14796\/JWMM.R204-17","article-title":"Methods for estimating inflow and infiltration into sanitary sewers","author":"Crawford","year":"1999","journal-title":"J. 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