{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T11:30:11Z","timestamp":1782387011339,"version":"3.54.5"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2020,1,8]],"date-time":"2020-01-08T00:00:00Z","timestamp":1578441600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,8]],"date-time":"2020-01-08T00:00:00Z","timestamp":1578441600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Engineering with Computers"],"DOI":"10.1007\/s00366-019-00921-y","type":"journal-article","created":{"date-parts":[[2020,1,8]],"date-time":"2020-01-08T09:02:42Z","timestamp":1578474162000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["Long short-term memory for predicting daily suspended sediment concentration"],"prefix":"10.1007","author":[{"given":"Keivan","family":"Kaveh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamid","family":"Kaveh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minh Duc","family":"Bui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Rutschmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,1,8]]},"reference":[{"key":"921_CR1","volume-title":"Applied modeling of hydrologic time series","author":"JD Salas","year":"1980","unstructured":"Salas JD, Delleur JW, Yevjevich V, Lane WL (1980) Applied modeling of hydrologic time series. Water Resources Publication, Littleton"},{"key":"921_CR2","doi-asserted-by":"crossref","unstructured":"ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000) Artificial neural networks in hydrology. I: Preliminary concepts, J Hydrol Eng 5(2):115\u2013123","DOI":"10.1061\/(ASCE)1084-0699(2000)5:2(115)"},{"issue":"1","key":"921_CR3","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1061\/(ASCE)0733-9429(2001)127:1(30)","volume":"127","author":"SK Jain","year":"2001","unstructured":"Jain SK (2001) Development of integrated sediment rating curves using ANNs. J Hydraul Eng 127(1):30\u201337","journal-title":"J Hydraul Eng"},{"issue":"6","key":"921_CR4","doi-asserted-by":"publisher","first-page":"588","DOI":"10.1061\/(ASCE)0733-9429(2002)128:6(588)","volume":"128","author":"HM Nagy","year":"2002","unstructured":"Nagy HM, Watanabe K, Hirano M (2002) Prediction of sediment load concentration in rivers using artificial neural network model. J Hydraul Eng 128(6):588\u2013595","journal-title":"J Hydraul Eng"},{"issue":"1","key":"921_CR5","doi-asserted-by":"publisher","first-page":"85","DOI":"10.4314\/wsa.v31i1.5125","volume":"31","author":"A Agarwal","year":"2005","unstructured":"Agarwal A, Singh RD, Mishra SK, Bhunya PK (2005) ANN-based sediment yield models for Vamsadhara river basin (India). Water SA 31(1):85\u2013100","journal-title":"Water SA"},{"issue":"2","key":"921_CR6","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.advengsoft.2005.05.002","volume":"37","author":"HK Cigizoglu","year":"2006","unstructured":"Cigizoglu HK, Alp M (2006) Generalized regression neural network in modelling river sediment yield. Adv Eng Softw 37(2):63\u201368","journal-title":"Adv Eng Softw"},{"issue":"4\u20135","key":"921_CR7","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1016\/j.pce.2004.12.001","volume":"30","author":"B Bhattacharya","year":"2005","unstructured":"Bhattacharya B, Price RK, Solomatine DP (2005) Data-driven modelling in the context of sediment transport. Phys Chem Earth 30(4\u20135):297\u2013302","journal-title":"Phys Chem Earth"},{"issue":"1","key":"921_CR8","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1061\/(ASCE)1084-0699(2006)11:1(71)","volume":"11","author":"NS Raghuwanshi","year":"2006","unstructured":"Raghuwanshi NS, Singh R, Reddy LS (2006) Runoff and sediment yield modeling using artificial neural networks: upper Siwane River, India. J Hydrol Eng 11(1):71\u201379","journal-title":"J Hydrol Eng"},{"issue":"12","key":"921_CR9","doi-asserted-by":"publisher","first-page":"1249","DOI":"10.1016\/j.advwatres.2003.08.005","volume":"26","author":"G Tayfur","year":"2003","unstructured":"Tayfur G, Ozdemir S, Singh VP (2003) Fuzzy logic algorithm for runoff-induced sediment transport from bare soil surfaces. Adv Water Resour 26(12):1249\u20131256","journal-title":"Adv Water Resour"},{"issue":"4","key":"921_CR10","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1623\/hysj.52.4.793","volume":"52","author":"AK Lohani","year":"2007","unstructured":"Lohani AK, Goel NK, Bhatia KS (2007) Deriving stage\u2013discharge\u2013sediment concentration relationships using fuzzy logic. Hydrol Sci J 52(4):793\u2013807","journal-title":"Hydrol Sci J"},{"issue":"15","key":"921_CR11","doi-asserted-by":"publisher","first-page":"2917","DOI":"10.1016\/j.scitotenv.2010.11.028","volume":"409","author":"T Rajaee","year":"2011","unstructured":"Rajaee T (2011) Wavelet and ANN combination model for prediction of daily suspended sediment load in rivers. Sci Total Environ 409(15):2917\u20132928","journal-title":"Sci Total Environ"},{"issue":"3","key":"921_CR12","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.ijsrc.2017.03.007","volume":"32","author":"K Kaveh","year":"2017","unstructured":"Kaveh K, Bui MD, Rutschmann P (2017) A comparative study of three different learning algorithms applied to ANFIS for predicting daily suspended sediment concentration. Int J Sedim Res 32(3):340\u2013350","journal-title":"Int J Sedim Res"},{"issue":"10","key":"921_CR13","doi-asserted-by":"publisher","first-page":"4170","DOI":"10.1002\/joc.6066","volume":"39","author":"DT Anh","year":"2019","unstructured":"Anh DT, Van SP, Dang TD, Hoang LP (2019) Downscaling rainfall using deep learning long short-term memory and feedforward neural network. Int J Climatol 39(10):4170\u20134188. https:\/\/doi.org\/10.1002\/joc.6066","journal-title":"Int J Climatol"},{"key":"921_CR14","unstructured":"Duong TA, Bui MD, Rutschmann P (2018) Long Short Term Memory for monthly rainfall prediction in Camau, Vietnam"},{"issue":"17","key":"921_CR15","doi-asserted-by":"publisher","first-page":"4916","DOI":"10.1016\/j.scitotenv.2009.05.016","volume":"407","author":"T Rajaee","year":"2009","unstructured":"Rajaee T, Mirbagheri SA, Zounemat-Kermani M, Nourani V (2009) Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models. Sci Total Environ 407(17):4916\u20134927","journal-title":"Sci Total Environ"},{"key":"921_CR16","doi-asserted-by":"publisher","DOI":"10.1623\/hysj.49.6.1025.55720","author":"\u00d6 Kisi","year":"2004","unstructured":"Kisi \u00d6 (2004) Multi-layer perceptrons with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation\/Pr\u00e9vision et estimation de la concentration en mati\u00e8res en suspension avec des perceptrons multi-couches et l\u2019algorithme d\u2019apprentissage de Levenberg-Marquardt. Hydrol Sci J. https:\/\/doi.org\/10.1623\/hysj.49.6.1025.55720","journal-title":"Hydrol Sci J"},{"key":"921_CR17","volume-title":"Neural networks: a comprehensive foundation","author":"S Haykin","year":"1999","unstructured":"Haykin S (1999) Neural networks: a comprehensive foundation. Prentice-Hall, Englewood Cliffs"},{"issue":"4","key":"921_CR18","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1061\/(ASCE)0733-9429(2007)133:4(440)","volume":"133","author":"B Bhattacharya","year":"2007","unstructured":"Bhattacharya B, Price RK, Solomatine DP (2007) Machine learning approach to modeling sediment transport. J Hydraul Eng 133(4):440\u2013450","journal-title":"J Hydraul Eng"},{"issue":"9","key":"921_CR19","doi-asserted-by":"publisher","first-page":"785","DOI":"10.1061\/(ASCE)1090-0241(2002)128:9(785)","volume":"128","author":"MA Shahin","year":"2002","unstructured":"Shahin MA, Maier HR, Jaksa MB (2002) Predicting settlement of shallow foundations using neural networks. J Geotech Geoenviron Eng ASCE. 128(9):785\u2013793","journal-title":"J Geotech Geoenviron Eng ASCE."},{"issue":"3","key":"921_CR20","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1109\/21.256541","volume":"23","author":"J-SR Jang","year":"1996","unstructured":"Jang J-SR (1996) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665\u2013685","journal-title":"IEEE Trans Syst Man Cybern"},{"issue":"2","key":"921_CR21","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1061\/(ASCE)0887-3801(2003)17:2(123)","volume":"17","author":"T Sayed","year":"2003","unstructured":"Sayed T, Tavakolie A, Razavi A (2003) Comparison of adaptive network based fuzzy inference systems and B-spline neuro-fuzzy mode choice models. J Comput Civ Eng 17(2):123\u2013130","journal-title":"J Comput Civ Eng"},{"issue":"3","key":"921_CR22","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1109\/5.364486","volume":"83","author":"J-SR Jang","year":"1995","unstructured":"Jang J-SR, Sun C-T (1995) Neuro-fuzzy modeling and control. Proc IEEE 83(3):378\u2013406","journal-title":"Proc IEEE"},{"issue":"8","key":"921_CR23","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"key":"921_CR24","unstructured":"Pascanu R, Mikolov T, Bengio Y (2013) On the difficulty of training recurrent neural networks. In: International conference on machine learning, pp 1310\u20131318"},{"key":"921_CR25","unstructured":"Graves A, Jaitly N (2014) Towards end-to-end speech recognition with recurrent neural networks. In: International conference on machine learning, pp 1764\u20131772"},{"issue":"3\u20134","key":"921_CR26","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1016\/S0022-1694(00)00214-6","volume":"230","author":"P Coulibaly","year":"2000","unstructured":"Coulibaly P, Anctil F, Bobee B (2000) Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. J Hydrol 230(3\u20134):244\u2013257","journal-title":"J Hydrol"},{"key":"921_CR27","doi-asserted-by":"publisher","DOI":"10.1029\/2006WR005383","author":"E Toth","year":"2007","unstructured":"Toth E, Brath A (2007) Multistep ahead streamflow forecasting: role of calibration data in conceptual and neural network modeling. Water Resour Res. https:\/\/doi.org\/10.1029\/2006WR005383","journal-title":"Water Resour Res"},{"issue":"8","key":"921_CR28","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.1243\/0954405011518999","volume":"215","author":"M Marquez","year":"2001","unstructured":"Marquez M, White A, Gill R (2001) A hybrid neural network-feature-based manufacturability analysis of mould reinforced plastic parts. Proc Inst Mech Eng B J Eng Manufac 215(8):1065\u20131079","journal-title":"Proc Inst Mech Eng B J Eng Manufac"},{"issue":"5","key":"921_CR29","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1016\/0893-6080(89)90020-8","volume":"2","author":"K Hornik","year":"1989","unstructured":"Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural networks. 2(5):359\u2013366","journal-title":"Neural networks."},{"issue":"1","key":"921_CR30","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/S0169-2070(97)00044-7","volume":"14","author":"G Zhang","year":"1998","unstructured":"Zhang G, Patuwo BE, Hu MY (1998) Forecasting with artificial neural networks: the state of the art. Int J Forecast 14(1):35\u201362","journal-title":"Int J Forecast"},{"issue":"1","key":"921_CR31","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1177\/030913330102500104","volume":"25","author":"CW Dawson","year":"2001","unstructured":"Dawson CW, Wilby RL (2001) Hydrological modelling using artificial neural networks. Prog Phys Geogr 25(1):80\u2013108","journal-title":"Prog Phys Geogr"},{"issue":"1","key":"921_CR32","doi-asserted-by":"publisher","first-page":"365","DOI":"10.5194\/hessd-2-365-2005","volume":"2","author":"NJ De Vos","year":"2005","unstructured":"De Vos NJ, Rientjes THM (2005) Constraints of artificial neural networks for rainfall-runoff modelling: trade-offs in hydrological state representation and model evaluation. Hydrol Earth Syst Sci Dis 2(1):365\u2013415","journal-title":"Hydrol Earth Syst Sci Dis"},{"issue":"1","key":"921_CR33","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.fluid.2006.02.013","volume":"245","author":"JE Schmitz","year":"2006","unstructured":"Schmitz JE, Zemp RJ, Mendes MJ (2006) Artificial neural networks for the solution of the phase stability problem. Fluid Phase Equilib 245(1):83\u201387","journal-title":"Fluid Phase Equilib"},{"issue":"1\u20134","key":"921_CR34","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.jhydrol.2009.03.038","volume":"372","author":"CL Wu","year":"2009","unstructured":"Wu CL, Chau KW, Li YS (2009) Methods to improve neural network performance in daily flows prediction. J Hydrol 372(1\u20134):80\u201393","journal-title":"J Hydrol"},{"issue":"1","key":"921_CR35","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1029\/1998WR900018","volume":"35","author":"DR Legates","year":"1999","unstructured":"Legates DR, McCabe GJ (1999) Evaluating the use of \u201cgoodness-of-fit\u201d measures in hydrologic and hydroclimatic model validation. Water Resour Res 35(1):233\u2013241","journal-title":"Water Resour Res"},{"issue":"1","key":"921_CR36","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1198\/073500102753410444","volume":"20","author":"FX Diebold","year":"2002","unstructured":"Diebold FX, Mariano RS (2002) Comparing predictive accuracy. J Bus Econ Stat 20(1):134\u2013144","journal-title":"J Bus Econ Stat"},{"issue":"3","key":"921_CR37","doi-asserted-by":"publisher","first-page":"1247","DOI":"10.5194\/gmd-7-1247-2014","volume":"7","author":"T Chai","year":"2014","unstructured":"Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)?\u2013Arguments against avoiding RMSE in the literature. Geosci Model Dev 7(3):1247\u20131250","journal-title":"Geosci Model Dev"},{"issue":"1\u20133","key":"921_CR38","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1016\/j.scitotenv.2008.04.022","volume":"400","author":"AJ Horowitz","year":"2008","unstructured":"Horowitz AJ (2008) Determining annual suspended sediment and sediment-associated trace element and nutrient fluxes. Sci Total Environ 400(1\u20133):315\u2013343","journal-title":"Sci Total Environ"}],"container-title":["Engineering with Computers"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00366-019-00921-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00366-019-00921-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00366-019-00921-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,1,7]],"date-time":"2021-01-07T01:16:07Z","timestamp":1609982167000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00366-019-00921-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,8]]},"references-count":38,"alternative-id":["921"],"URL":"https:\/\/doi.org\/10.1007\/s00366-019-00921-y","relation":{},"ISSN":["0177-0667","1435-5663"],"issn-type":[{"value":"0177-0667","type":"print"},{"value":"1435-5663","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,8]]},"assertion":[{"value":"16 May 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 December 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 January 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare no potential conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}