{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T03:15:56Z","timestamp":1774062956817,"version":"3.50.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"18","license":[{"start":{"date-parts":[[2022,4,23]],"date-time":"2022-04-23T00:00:00Z","timestamp":1650672000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,4,23]],"date-time":"2022-04-23T00:00:00Z","timestamp":1650672000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2022,9]]},"DOI":"10.1007\/s00521-022-07237-x","type":"journal-article","created":{"date-parts":[[2022,4,23]],"date-time":"2022-04-23T12:02:57Z","timestamp":1650715377000},"page":"15481-15497","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Standalone and ensemble-based machine learning techniques for particle Froude number prediction in a sewer system"],"prefix":"10.1007","volume":"34","author":[{"given":"Deepti","family":"Shakya","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8808-6608","authenticated-orcid":false,"given":"Vishal","family":"Deshpande","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mayank","family":"Agarwal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bimlesh","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,4,23]]},"reference":[{"key":"7237_CR1","unstructured":"Ab\u00a0Ghani AA (1993) Sediment transport in sewers. PhD thesis, Newcastle University"},{"key":"7237_CR2","unstructured":"Ackers J, Butler D, May R (1996) Design of sewers to control sediment problems. Construction Industry Research and Information Association London"},{"key":"7237_CR3","unstructured":"Alvarez-Hernandez EM (1990) The influence of cohesion on sediment movement in channels of circular cross-section. PhD thesis, Newcastle University"},{"issue":"1","key":"7237_CR4","first-page":"73","volume":"7","author":"N Arya Azar","year":"2021","unstructured":"Arya Azar N, Ghordoyee Milan S, Kardan N (2021) Development of a hybrid ann-evolutionary algorithms models to predict the froude number in open channel flows in modeling of sediment transport. Environ Water Eng 7(1):73\u201387","journal-title":"Environ Water Eng"},{"issue":"7","key":"7237_CR5","doi-asserted-by":"publisher","first-page":"2605","DOI":"10.1007\/s00521-020-05139-4","volume":"33","author":"A Baran","year":"2021","unstructured":"Baran A, Lerch S, El Ayari M, Baran S (2021) Machine learning for total cloud cover prediction. Neural Comput Appl 33(7):2605\u20132620","journal-title":"Neural Comput Appl"},{"issue":"4","key":"7237_CR6","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1061\/(ASCE)0733-9429(2003)129:4(276)","volume":"129","author":"D Butler","year":"2003","unstructured":"Butler D, May R, Ackers J (2003) Self-cleansing sewer design based on sediment transport principles. J Hydraul Eng 129(4):276\u2013282","journal-title":"J Hydraul Eng"},{"issue":"5","key":"7237_CR7","doi-asserted-by":"publisher","first-page":"1131","DOI":"10.2166\/hydro.2018.217","volume":"20","author":"N Caradot","year":"2018","unstructured":"Caradot N, Riechel M, Fesneau M, Hernandez N, Torres A, Sonnenberg H, Eckert E, Lengemann N, Waschnewski J, Rouault P (2018) Practical benchmarking of statistical and machine learning models for predicting the condition of sewer pipes in Berlin, Germany. J Hydroinf 20(5):1131\u20131147","journal-title":"J Hydroinf"},{"key":"7237_CR8","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C (2016) Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp 785\u2013794","DOI":"10.1145\/2939672.2939785"},{"issue":"2","key":"7237_CR9","doi-asserted-by":"publisher","first-page":"04020002","DOI":"10.1061\/(ASCE)PS.1949-1204.0000449","volume":"11","author":"A Danandeh Mehr","year":"2020","unstructured":"Danandeh Mehr A, Safari MJS (2020) Application of soft computing techniques for particle froude number estimation in sewer pipes. J Pipeline Syst Eng Pract 11(2):04020002","journal-title":"J Pipeline Syst Eng Pract"},{"issue":"3","key":"7237_CR10","first-page":"382","volume":"7","author":"I Ebtehaj","year":"2013","unstructured":"Ebtehaj I, Bonakdari H (2013) Evaluation of sediment transport in sewer using artificial neural network. Eng Appl Comput Fluid Mech 7(3):382\u2013392","journal-title":"Eng Appl Comput Fluid Mech"},{"issue":"10","key":"7237_CR11","doi-asserted-by":"publisher","first-page":"1695","DOI":"10.2166\/wst.2014.434","volume":"70","author":"I Ebtehaj","year":"2014","unstructured":"Ebtehaj I, Bonakdari H (2014) Comparison of genetic algorithm and imperialist competitive algorithms in predicting bed load transport in clean pipe. Water Sci Technol 70(10):1695\u20131701","journal-title":"Water Sci Technol"},{"issue":"13","key":"7237_CR12","doi-asserted-by":"publisher","first-page":"4765","DOI":"10.1007\/s11269-014-0774-0","volume":"28","author":"I Ebtehaj","year":"2014","unstructured":"Ebtehaj I, Bonakdari H (2014) Performance evaluation of adaptive neural fuzzy inference system for sediment transport in sewers. Water Resour Manage 28(13):4765\u20134779","journal-title":"Water Resour Manage"},{"issue":"2","key":"7237_CR13","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1007\/s12205-015-0630-7","volume":"20","author":"I Ebtehaj","year":"2016","unstructured":"Ebtehaj I, Bonakdari H (2016) Bed load sediment transport estimation in a clean pipe using multilayer perceptron with different training algorithms. KSCE J Civ Eng 20(2):581\u2013589","journal-title":"KSCE J Civ Eng"},{"key":"7237_CR14","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.flowmeasinst.2015.11.002","volume":"47","author":"I Ebtehaj","year":"2016","unstructured":"Ebtehaj I, Bonakdari H, Shamshirband S, Mohammadi K (2016) A combined support vector machine-wavelet transform model for prediction of sediment transport in sewer. Flow Meas Instrum 47:19\u201327","journal-title":"Flow Meas Instrum"},{"issue":"1","key":"7237_CR15","doi-asserted-by":"publisher","first-page":"176","DOI":"10.2166\/wst.2016.174","volume":"74","author":"I Ebtehaj","year":"2016","unstructured":"Ebtehaj I, Bonakdari H, Zaji AH (2016) An expert system with radial basis function neural network based on decision trees for predicting sediment transport in sewers. Water Sci Technol 74(1):176\u2013183","journal-title":"Water Sci Technol"},{"issue":"2","key":"7237_CR16","doi-asserted-by":"publisher","first-page":"04016018","DOI":"10.1061\/(ASCE)PS.1949-1204.0000252","volume":"8","author":"I Ebtehaj","year":"2017","unstructured":"Ebtehaj I, Bonakdari H, Shamshirband S, Ismail Z, Hashim R (2017) New approach to estimate velocity at limit of deposition in storm sewers using vector machine coupled with firefly algorithm. J Pipeline Syst Eng Pract 8(2):04016018","journal-title":"J Pipeline Syst Eng Pract"},{"key":"7237_CR17","unstructured":"El-Zaemey AKS (1991) Sediment transport over deposited beds in sewers. PhD thesis, Newcastle University"},{"key":"7237_CR18","doi-asserted-by":"crossref","unstructured":"Gao Z, Hu Q, Xu X (2021) Condition monitoring and life prediction of the turning tool based on extreme learning machine and transfer learning. Neural Comput Appl, pp 1\u201312","DOI":"10.1007\/s00521-021-05716-1"},{"issue":"1","key":"7237_CR19","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","volume":"63","author":"P Geurts","year":"2006","unstructured":"Geurts P, Ernst D, Wehenkel L (2006) Extremely randomized trees. Mach Learn 63(1):3\u201342","journal-title":"Mach Learn"},{"key":"7237_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.flowmeasinst.2017.08.004","volume":"57","author":"F Granata","year":"2017","unstructured":"Granata F, de Marinis G (2017) Machine learning methods for wastewater hydraulics. Flow Meas Instrum 57:1\u20139","journal-title":"Flow Meas Instrum"},{"key":"7237_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2019.102849","volume":"106","author":"SI Hassan","year":"2019","unstructured":"Hassan SI, Dang LM, Mehmood I, Im S, Choi C, Kang J, Park YS, Moon H (2019) Underground sewer pipe condition assessment based on convolutional neural networks. Autom Constr 106:102849","journal-title":"Autom Constr"},{"issue":"12","key":"7237_CR22","doi-asserted-by":"publisher","first-page":"2318","DOI":"10.2166\/wst.2019.229","volume":"79","author":"K Kargar","year":"2019","unstructured":"Kargar K, Safari MJS, Mohammadi M, Samadianfard S (2019) Sediment transport modeling in open channels using neuro-fuzzy and gene expression programming techniques. Water Sci Technol 79(12):2318\u20132327","journal-title":"Water Sci Technol"},{"key":"7237_CR23","doi-asserted-by":"crossref","unstructured":"Khan W, Ghazanfar MA, Azam MA, Karami A, Alyoubi KH, Alfakeeh AS (2020) Stock market prediction using machine learning classifiers and social media, news. J Ambient Intell Humaniz Comput, pp 1\u201324","DOI":"10.1007\/s00500-019-04347-y"},{"key":"7237_CR24","doi-asserted-by":"crossref","unstructured":"Khozani ZS, Safari MJS, Mehr AD, Mohtar WHMW (2020) An ensemble genetic programming approach to develop incipient sediment motion models in rectangular channels. J Hydrol, p 124753","DOI":"10.1016\/j.jhydrol.2020.124753"},{"issue":"2","key":"7237_CR25","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1007\/s00521-020-05002-6","volume":"33","author":"C Li","year":"2021","unstructured":"Li C, Xu P (2021) Application on traffic flow prediction of machine learning in intelligent transportation. Neural Comput Appl 33(2):613\u2013624","journal-title":"Neural Comput Appl"},{"issue":"4","key":"7237_CR26","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1061\/(ASCE)CP.1943-5487.0000089","volume":"25","author":"J Mashford","year":"2011","unstructured":"Mashford J, Marlow D, Tran D, May R (2011) Prediction of sewer condition grade using support vector machines. J Comput Civ Eng 25(4):283\u2013290","journal-title":"J Comput Civ Eng"},{"key":"7237_CR27","unstructured":"May R (1993) Sediment transport in pipes, sewers and deposited beds. Tech. rep"},{"issue":"9","key":"7237_CR28","doi-asserted-by":"publisher","first-page":"195","DOI":"10.2166\/wst.1996.0210","volume":"33","author":"RW May","year":"1996","unstructured":"May RW, Ackers JC, Butler D, Si\u00e2n J (1996) Development of design methodology for self-cleansing sewers. Water Sci Technol 33(9):195","journal-title":"Water Sci Technol"},{"issue":"6","key":"7237_CR29","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1061\/(ASCE)1084-0699(2006)11:6(597)","volume":"11","author":"RH McCuen","year":"2006","unstructured":"McCuen RH, Knight Z, Cutter AG (2006) Evaluation of the Nash-Sutcliffe efficiency index. J Hydrol Eng 11(6):597\u2013602","journal-title":"J Hydrol Eng"},{"issue":"8","key":"7237_CR30","doi-asserted-by":"publisher","first-page":"3369","DOI":"10.1007\/s00521-017-3283-2","volume":"31","author":"UM Mohapatra","year":"2019","unstructured":"Mohapatra UM, Majhi B, Satapathy SC (2019) Financial time series prediction using distributed machine learning techniques. Neural Comput Appl 31(8):3369\u20133384","journal-title":"Neural Comput Appl"},{"key":"7237_CR31","unstructured":"M\u00fcller AC, Guido S, et\u00a0al. (2016) Introduction to machine learning with Python: a guide for data scientists. O\u2019Reilly Media, Inc"},{"issue":"5\u20136","key":"7237_CR32","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/0925-2312(91)90023-5","volume":"2","author":"F Murtagh","year":"1991","unstructured":"Murtagh F (1991) Multilayer perceptrons for classification and regression. Neurocomputing 2(5\u20136):183\u2013197","journal-title":"Neurocomputing"},{"issue":"8\u20139","key":"7237_CR33","doi-asserted-by":"publisher","first-page":"123","DOI":"10.2166\/wst.1997.0654","volume":"36","author":"C Nalluri","year":"1997","unstructured":"Nalluri C, El-Zaemey A, Chan H (1997) Sediment transport over fixed deposited beds in sewers-An appraisal of existing models. Water Sci Technol 36(8\u20139):123\u2013128","journal-title":"Water Sci Technol"},{"issue":"8","key":"7237_CR34","doi-asserted-by":"publisher","first-page":"115","DOI":"10.2166\/wst.1992.0185","volume":"25","author":"G Perrusqu\u00eda","year":"1992","unstructured":"Perrusqu\u00eda G (1992) An experimental study on the transport of sediment in sewer pipes with a permanent deposit. Water Sci Technol 25(8):115\u2013122","journal-title":"Water Sci Technol"},{"key":"7237_CR35","unstructured":"Perrusquia G (1993) An experimental study from flume to stream traction in pipe channels. Chalmers University of Technology, Tech. rep"},{"issue":"3","key":"7237_CR36","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1016\/j.ijsrc.2018.04.007","volume":"33","author":"MJS Safari","year":"2018","unstructured":"Safari MJS, Mehr AD (2018) Multigene genetic programming for sediment transport modeling in sewers for conditions of non-deposition with a bed deposit. Int J Sedim Res 33(3):262\u2013270","journal-title":"Int J Sedim Res"},{"issue":"17","key":"7237_CR37","doi-asserted-by":"publisher","first-page":"11255","DOI":"10.1007\/s00521-020-05571-6","volume":"33","author":"MJS Safari","year":"2021","unstructured":"Safari MJS, Rahimzadeh Arashloo S (2021) Kernel ridge regression model for sediment transport in open channel flow. Neural Comput Appl 33(17):11255\u201311271","journal-title":"Neural Comput Appl"},{"issue":"4","key":"7237_CR38","doi-asserted-by":"publisher","first-page":"04018017","DOI":"10.1061\/(ASCE)PS.1949-1204.0000335","volume":"9","author":"MJS Safari","year":"2018","unstructured":"Safari MJS, Mohammadi M, Ab Ghani A (2018) Experimental studies of self-cleansing drainage system design: a review. J Pipeline Syst Eng Pract 9(4):04018017","journal-title":"J Pipeline Syst Eng Pract"},{"issue":"7","key":"7237_CR39","doi-asserted-by":"publisher","first-page":"2085","DOI":"10.1007\/s00521-015-1997-6","volume":"31","author":"M Sarveghadi","year":"2019","unstructured":"Sarveghadi M, Gandomi AH, Bolandi H, Alavi AH (2019) Development of prediction models for shear strength of sfrcb using a machine learning approach. Neural Comput Appl 31(7):2085\u20132094","journal-title":"Neural Comput Appl"},{"issue":"9","key":"7237_CR40","doi-asserted-by":"publisher","first-page":"1761","DOI":"10.1016\/j.energy.2006.11.010","volume":"32","author":"GK Tso","year":"2007","unstructured":"Tso GK, Yau KK (2007) Predicting electricity energy consumption: a comparison of regression analysis, decision tree and neural networks. Energy 32(9):1761\u20131768","journal-title":"Energy"},{"issue":"3","key":"7237_CR41","doi-asserted-by":"publisher","first-page":"1327","DOI":"10.1016\/j.eswa.2007.08.013","volume":"35","author":"MD Yang","year":"2008","unstructured":"Yang MD, Su TC (2008) Automated diagnosis of sewer pipe defects based on machine learning approaches. Expert Syst Appl 35(3):1327\u20131337","journal-title":"Expert Syst Appl"},{"issue":"5","key":"7237_CR42","first-page":"121","volume":"30","author":"F Yosefvand","year":"2019","unstructured":"Yosefvand F, Shabanlo S, Izadbakhsh MA (2019) Prediction of froude number of three phases flow in sewer systems using extreme learning machines. J Water Wastewater; Ab va Fazilab 30(5):121\u2013126 (in persian)","journal-title":"J Water Wastewater; Ab va Fazilab"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07237-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07237-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07237-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T18:25:03Z","timestamp":1662056703000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07237-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,23]]},"references-count":42,"journal-issue":{"issue":"18","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["7237"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07237-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,23]]},"assertion":[{"value":"4 June 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 March 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 April 2022","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 that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}