{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T04:27:09Z","timestamp":1781929629714,"version":"3.54.5"},"reference-count":90,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:00:00Z","timestamp":1630454400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:00:00Z","timestamp":1630454400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:00:00Z","timestamp":1630454400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:00:00Z","timestamp":1630454400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:00:00Z","timestamp":1630454400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:00:00Z","timestamp":1630454400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:00:00Z","timestamp":1630454400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Hydrology"],"published-print":{"date-parts":[[2021,9]]},"DOI":"10.1016\/j.jhydrol.2021.126506","type":"journal-article","created":{"date-parts":[[2021,5,30]],"date-time":"2021-05-30T18:17:24Z","timestamp":1622398644000},"page":"126506","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":141,"special_numbering":"C","title":["Artificial intelligence modelling integrated with Singular Spectral analysis and Seasonal-Trend decomposition using Loess approaches for streamflow predictions"],"prefix":"10.1016","volume":"600","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9875-7321","authenticated-orcid":false,"given":"Halit","family":"Apaydin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5139-2118","authenticated-orcid":false,"given":"Mohammad","family":"Taghi Sattari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kambiz","family":"Falsafian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ramendra","family":"Prasad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jhydrol.2021.126506_b0010","series-title":"Understanding of a Convolutional Neural Network","first-page":"1","author":"Albawi","year":"2017"},{"key":"10.1016\/j.jhydrol.2021.126506_b0015","doi-asserted-by":"crossref","unstructured":"Aman, Z., Ezzine, L., El Bahi, Y. F., & EL Moussami, H. (2019). Improving the modeling and forecasting of fuel selling price using the radial basis function technique: A case study. Journal of Algorithms & Computational Technology, 13, 174830261988112. https:\/\/doi.org\/10.1177\/1748302619881120.","DOI":"10.1177\/1748302619881120"},{"issue":"1","key":"10.1016\/j.jhydrol.2021.126506_b0020","doi-asserted-by":"crossref","first-page":"31","DOI":"10.3354\/cr028031","article-title":"Spatial interpolation techniques for climate data in the GAP region in Turkey","volume":"28","author":"Apaydin","year":"2004","journal-title":"Clim. Res."},{"issue":"5","key":"10.1016\/j.jhydrol.2021.126506_b0025","doi-asserted-by":"crossref","first-page":"1500","DOI":"10.3390\/w12051500","article-title":"Comparative analysis of recurrent neural network architectures for reservoir inflow forecasting","volume":"12","author":"Apaydin","year":"2020","journal-title":"Water"},{"key":"10.1016\/j.jhydrol.2021.126506_b0030","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/j.neucom.2004.04.016","article-title":"Neural networks and M5 model trees in modelling water level\u2013discharge relationship","volume":"63","author":"Bhattacharya","year":"2005","journal-title":"Neurocomputing"},{"key":"10.1016\/j.jhydrol.2021.126506_b0035","unstructured":"B\u00f3galo, J., Poncela P. and Senra, E. 2017 Automatic Signal Extraction for Stationary and Non-Stationary Time Series by Circulant SSA. MPRA Paper No. 76023."},{"key":"10.1016\/j.jhydrol.2021.126506_b0040","series-title":"Data-Driven Science and Engineering","author":"Brunton","year":"2019"},{"key":"10.1016\/j.jhydrol.2021.126506_b0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2019.124379","article-title":"Verification of novel integrations of swarm intelligence algorithms into deep learning neural network for flood susceptibility mapping","volume":"581","author":"Bui","year":"2020","journal-title":"J. Hydrol."},{"issue":"3","key":"10.1016\/j.jhydrol.2021.126506_b0055","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1623\/hysj.48.3.381.45286","article-title":"Artificial neural network approach to flood forecasting in the River Arno","volume":"48","author":"Campolo","year":"2003","journal-title":"Hydrol. Sci. J."},{"key":"10.1016\/j.jhydrol.2021.126506_b0060","unstructured":"Ceylan A, Akgunduz S, Demirors Z, Erkan A, Cinar S, Ozevren E. 2009. The aridity changes in the specified Index By Using Areas prone to desertification in Turkey (In Turkish). I. Ulusal Kurakl\u0131k ve Colle\u015fme Sempozyumu. Konya. 16-18 Haziran 2009."},{"issue":"3","key":"10.1016\/j.jhydrol.2021.126506_b0065","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.5194\/gmd-7-1247-2014","article-title":"Root mean square error (RMSE) or mean absolute error (MAE)? \u2013 Arguments against avoiding RMSE in the literature","volume":"7","author":"Chai","year":"2014","journal-title":"Geosci. Model Dev."},{"issue":"1","key":"10.1016\/j.jhydrol.2021.126506_b0070","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-020-15734-7","article-title":"Self-organizing maps of typhoon tracks allow for flood forecasts up to two days in advance","volume":"11","author":"Chang","year":"2020","journal-title":"Nat. Commun."},{"issue":"1","key":"10.1016\/j.jhydrol.2021.126506_b0075","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1111\/j.1365-246X.2012.05470.x","article-title":"Using empirical mode decomposition to process marine magnetotelluric data","volume":"190","author":"Chen","year":"2012","journal-title":"Geophys. J. Int."},{"key":"10.1016\/j.jhydrol.2021.126506_b0085","doi-asserted-by":"crossref","first-page":"91181","DOI":"10.1109\/ACCESS.2020.2995044","article-title":"Forecasting the Short-Term Metro Ridership With Seasonal and Trend Decomposition Using Loess and LSTM Neural Networks","volume":"8","author":"Chen","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.jhydrol.2021.126506_b0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2020.125376","article-title":"Long lead-time daily and monthly streamflow forecasting using machine learning methods","volume":"590","author":"Cheng","year":"2020","journal-title":"J. Hydrol."},{"key":"10.1016\/j.jhydrol.2021.126506_b0095","unstructured":"Chollet, F., & others. (2015). Keras. GitHub. Retrieved from https:\/\/github.com\/fchollet\/keras."},{"issue":"2","key":"10.1016\/j.jhydrol.2021.126506_b0100","doi-asserted-by":"crossref","first-page":"520","DOI":"10.2166\/hydro.2017.076","article-title":"Streamflow forecasting by combining neural networks and fuzzy models using advanced methods of input variable selection","volume":"20","author":"Dariane","year":"2018","journal-title":"J. Hydroinf."},{"issue":"190","key":"10.1016\/j.jhydrol.2021.126506_b0105","doi-asserted-by":"crossref","first-page":"138","DOI":"10.15446\/dyna.v82n190.43652","article-title":"Electricity consumption forecasting using singular spectrum analysis","volume":"82","author":"de Menezes","year":"2015","journal-title":"DYNA"},{"key":"10.1016\/j.jhydrol.2021.126506_b0110","article-title":"Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model","volume":"1\u201330","author":"Deo","year":"2016","journal-title":"Stoch. Env. Res. Risk Assess."},{"key":"10.1016\/j.jhydrol.2021.126506_b0115","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1016\/j.apenergy.2016.01.130","article-title":"A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset","volume":"168","author":"Deo","year":"2016","journal-title":"Appl. Energy"},{"key":"10.1016\/j.jhydrol.2021.126506_b0120","doi-asserted-by":"crossref","DOI":"10.1016\/j.envres.2017.01.035","article-title":"Very short-term reactive forecasting of the solar ultraviolet index using an extreme learning machine integrated with the solar zenith angle","author":"Deo","year":"2017","journal-title":"Environ. Res."},{"key":"10.1016\/j.jhydrol.2021.126506_b0125","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.atmosres.2016.10.004","article-title":"Drought forecasting in eastern Australia using multivariate adaptive regression spline, least-square support vector machine and M5Tree model","volume":"184","author":"Deo","year":"2017","journal-title":"Atmos. Res."},{"key":"10.1016\/j.jhydrol.2021.126506_b0130","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.atmosres.2015.03.018","article-title":"Application of the Artificial Neural Network model for prediction of monthly Standardized Precipitation and Evapotranspiration Index using hydrometeorological parameters and climate indices in eastern Australia","volume":"161\u2013162","author":"Deo","year":"2015","journal-title":"Atmos. Res."},{"key":"10.1016\/j.jhydrol.2021.126506_b0135","article-title":"An extreme learning machine model for the simulation of monthly mean streamflow water level in eastern Queensland","author":"Deo","year":"2016","journal-title":"Environ. Monit. Assess."},{"key":"10.1016\/j.jhydrol.2021.126506_b0140","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1016\/j.rser.2017.01.114","article-title":"Forecasting long-term global solar radiation with an ANN algorithm coupled with satellite-derived (MODIS) land surface temperature (LST) for regional locations in Queensland","volume":"72","author":"Deo","year":"2017","journal-title":"Renew. Sustain. Energy Rev."},{"issue":"3","key":"10.1016\/j.jhydrol.2021.126506_b0145","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1007\/s11269-006-9027-1","article-title":"A neuro-fuzzy model for inflow forecasting of the Nile River at Aswan high dam","volume":"21","author":"El-Shafie","year":"2007","journal-title":"Water Resour. Manage."},{"issue":"2","key":"10.1016\/j.jhydrol.2021.126506_b0150","first-page":"348","article-title":"Estimation of Drought by Streamflow Drought Index (SDI) and Artificial Neural Networks (ANNs) in Ankara-Nallihan Region","volume":"8","author":"Erogluer","year":"2020","journal-title":"Turk. J. Agric. Food Sci. Technol."},{"issue":"3\u20134","key":"10.1016\/j.jhydrol.2021.126506_b0155","doi-asserted-by":"crossref","first-page":"875","DOI":"10.1007\/s00704-016-1735-8","article-title":"Application of soft computing-based hybrid models in hydrological variables modeling: a comprehensive review","volume":"128","author":"Fahimi","year":"2017","journal-title":"Theor. Appl. Climatol."},{"key":"10.1016\/j.jhydrol.2021.126506_b0160","article-title":"Predicting flood susceptibility using LSTM neural networks","volume":"125734","author":"Fang","year":"2020","journal-title":"J. Hydrol."},{"key":"10.1016\/j.jhydrol.2021.126506_b0165","unstructured":"Flandrin P. 1998. Time-Frequency\/Time-Scale Analysis. Wavelet analysis and its applications. Academic Press, 1998, vol. 10."},{"issue":"7","key":"10.1016\/j.jhydrol.2021.126506_b0170","doi-asserted-by":"crossref","first-page":"4295","DOI":"10.1002\/wrcr.20339","article-title":"Tree-based iterative input variable selection for hydrological modeling","volume":"49","author":"Galelli","year":"2013","journal-title":"Water Resour. Res."},{"issue":"5","key":"10.1016\/j.jhydrol.2021.126506_b0175","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1057\/jors.2013.41","article-title":"Using singular spectrum analysis to obtain staffing level requirements in emergency units","volume":"65","author":"Gillard","year":"2014","journal-title":"J. Operat. Res. Soc."},{"key":"10.1016\/j.jhydrol.2021.126506_b0180","doi-asserted-by":"crossref","unstructured":"Golyandina, N., & Zhigljavsky, A. (2013). Singular Spectrum Analysis for Time Series. Springer Berlin Heidelberg. https:\/\/doi.org\/10.1007\/978-3-642-34913-3.","DOI":"10.1007\/978-3-642-34913-3"},{"key":"10.1016\/j.jhydrol.2021.126506_b0185","doi-asserted-by":"crossref","unstructured":"Golyandina, N., Korobeynikov, A., & Zhigljavsky, A. (2018). Singular Spectrum Analysis with R. Springer Berlin Heidelberg. https:\/\/doi.org\/10.1007\/978-3-662-57380-8.","DOI":"10.1007\/978-3-662-57380-8"},{"key":"10.1016\/j.jhydrol.2021.126506_b0190","doi-asserted-by":"crossref","unstructured":"Golyandina, N. (2019). Particularities and commonalities of singular spectrum analysis as a method of time series analysis and signal processing. https:\/\/doi.org\/10.1002\/wics.1487.","DOI":"10.1002\/wics.1487"},{"key":"10.1016\/j.jhydrol.2021.126506_b0195","series-title":"Deep Learning","author":"Goodfellow","year":"2016"},{"issue":"1\u20132","key":"10.1016\/j.jhydrol.2021.126506_b0200","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.jhydrol.2009.08.003","article-title":"Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling","volume":"377","author":"Gupta","year":"2009","journal-title":"J. Hydrol."},{"issue":"1","key":"10.1016\/j.jhydrol.2021.126506_b0205","doi-asserted-by":"crossref","first-page":"55","DOI":"10.13031\/2013.29502","article-title":"Modifying goodness-of-fit indicators to incorporate both measurement and model uncertainty in model calibration and validation","volume":"53","author":"Harmel","year":"2010","journal-title":"Trans. ASABE"},{"issue":"1","key":"10.1016\/j.jhydrol.2021.126506_b0210","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1109\/TSP.2017.2752720","article-title":"The sliding singular spectrum analysis: a data-driven nonstationary signal decomposition tool","volume":"66","author":"Harmouche","year":"2018","journal-title":"IEEE Trans. Signal Process."},{"issue":"2","key":"10.1016\/j.jhydrol.2021.126506_b0215","doi-asserted-by":"crossref","first-page":"239","DOI":"10.6339\/JDS.2007.05(2).396","article-title":"Singular spectrum analysis: methodology and comparison","volume":"5","author":"Hassani","year":"2007","journal-title":"J. Data Sci."},{"issue":"5","key":"10.1016\/j.jhydrol.2021.126506_b0220","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1111\/exsy.12111","article-title":"A review of performance criteria to validate simulation models","volume":"32","author":"Hora","year":"2015","journal-title":"Expert Systems"},{"issue":"6","key":"10.1016\/j.jhydrol.2021.126506_b0225","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1109\/MCOM.2019.1800155","article-title":"Deep learning with long short-term memory for time series prediction","volume":"57","author":"Hua","year":"2019","journal-title":"IEEE Commun. Mag."},{"key":"10.1016\/j.jhydrol.2021.126506_b0230","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1016\/j.jhydrol.2016.06.026","article-title":"A hybrid approach to monthly streamflow forecasting: Integrating hydrological model outputs into a Bayesian artificial neural network","volume":"540","author":"Humphrey","year":"2016","journal-title":"J. Hydrol."},{"key":"10.1016\/j.jhydrol.2021.126506_b0235","doi-asserted-by":"crossref","unstructured":"Hunter, J. D. 2007. \u201cMatplotlib: A 2D Graphics Environment\u201d, Computing in Science & Engineering, vol. 9, no. 3, pp. 90-95, 2007.","DOI":"10.1109\/MCSE.2007.55"},{"key":"10.1016\/j.jhydrol.2021.126506_b0245","doi-asserted-by":"crossref","unstructured":"Hyndman, R.J., & Athanasopoulos, G. (2018) Forecasting: principles and practice, 2nd edition, OTexts: Melbourne, Australia. OTexts.com\/fpp2. Accessed on March 16, 2020.","DOI":"10.32614\/CRAN.package.fpp2"},{"issue":"4","key":"10.1016\/j.jhydrol.2021.126506_b0250","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1016\/j.ijforecast.2006.03.001","article-title":"Another look at measures of forecast accuracy","volume":"22","author":"Hyndman","year":"2006","journal-title":"Int. J. Forecast."},{"key":"10.1016\/j.jhydrol.2021.126506_b0255","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2020.124631","article-title":"Exploring a Long Short-Term Memory based Encoder-Decoder framework for multi-step-ahead flood forecasting","volume":"583","author":"Kao","year":"2020","journal-title":"J. Hydrol."},{"key":"10.1016\/j.jhydrol.2021.126506_b0260","article-title":"Fusing stacked autoencoder and long short-term memory for regional multistep-ahead flood inundation forecasts","volume":"126371","author":"Kao,","year":"2021","journal-title":"J. Hydrol."},{"issue":"3","key":"10.1016\/j.jhydrol.2021.126506_b0265","doi-asserted-by":"crossref","first-page":"671","DOI":"10.2166\/hydro.2013.042","article-title":"Multi-step streamflow forecasting using data-driven non-linear methods in contrasting climate regimes","volume":"16","author":"Karran","year":"2014","journal-title":"J. Hydroinf."},{"issue":"2","key":"10.1016\/j.jhydrol.2021.126506_b0270","first-page":"138","article-title":"Assessment of irrigation schemes with performance indicators in southeastern irrigation district of Turkey","volume":"26","author":"Kartal","year":"2020","journal-title":"J. Agric. Sci."},{"issue":"8","key":"10.1016\/j.jhydrol.2021.126506_b0275","doi-asserted-by":"crossref","first-page":"2907","DOI":"10.1007\/s11269-019-02273-0","article-title":"A comparative study of MLR, KNN, ANN and ANFIS models with wavelet transform in monthly stream flow prediction","volume":"33","author":"Khazaee Poul","year":"2019","journal-title":"Water Resour. Manage."},{"issue":"11","key":"10.1016\/j.jhydrol.2021.126506_b0280","doi-asserted-by":"crossref","first-page":"6005","DOI":"10.5194\/hess-22-6005-2018","article-title":"Rainfall\u2013runoff modelling using Long Short-Term Memory (LSTM) networks","volume":"22","author":"Kratzert","year":"2018","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"10.1016\/j.jhydrol.2021.126506_b0285","doi-asserted-by":"crossref","first-page":"89","DOI":"10.5194\/adgeo-5-89-2005","article-title":"Comparison of different efficiency criteria for hydrological model assessment","volume":"5","author":"Krause","year":"2005","journal-title":"Adv. Geosci."},{"issue":"7553","key":"10.1016\/j.jhydrol.2021.126506_b0290","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"issue":"1","key":"10.1016\/j.jhydrol.2021.126506_b0295","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1029\/1998WR900018","article-title":"Evaluating the use of \u201cgoodness-of-fit\u201d measures in hydrologic and hydroclimatic model validation","volume":"35","author":"Legates","year":"1999","journal-title":"Water Resour. Res."},{"issue":"S1","key":"10.1016\/j.jhydrol.2021.126506_b0305","doi-asserted-by":"crossref","first-page":"69","DOI":"10.2166\/nh.2016.264","article-title":"Comparison of random forests and other statistical methods for the prediction of lake water level: a case study of the Poyang Lake in China","volume":"47","author":"Li","year":"2016","journal-title":"Hydrol. Res."},{"key":"10.1016\/j.jhydrol.2021.126506_b0310","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.enconman.2018.01.010","article-title":"Smart multi-step deep learning model for wind speed forecasting based on variational mode decomposition, singular spectrum analysis, LSTM network and ELM","volume":"159","author":"Liu","year":"2018","journal-title":"Energy Convers. Manage."},{"issue":"3","key":"10.1016\/j.jhydrol.2021.126506_b0315","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1109\/JAS.2019.1911447","article-title":"An embedded feature selection method for imbalanced data classification","volume":"6","author":"Liu","year":"2019","journal-title":"IEEE\/CAA J. Autom. Sin."},{"issue":"7","key":"10.1016\/j.jhydrol.2021.126506_b0325","doi-asserted-by":"crossref","first-page":"2191","DOI":"10.1007\/s13762-014-0613-0","article-title":"Successive station monthly streamflow prediction using different artificial neural network algorithms","volume":"12","author":"Mehr","year":"2014","journal-title":"Int. J. Environ. Sci. Technol."},{"issue":"6","key":"10.1016\/j.jhydrol.2021.126506_b0330","doi-asserted-by":"crossref","first-page":"1763","DOI":"10.13031\/trans.58.10715","article-title":"Hydrologic and water quality models: performance measures and evaluation criteria","volume":"58","author":"Moriasi","year":"2015","journal-title":"Trans. ASABE"},{"issue":"11","key":"10.1016\/j.jhydrol.2021.126506_b0335","doi-asserted-by":"crossref","first-page":"1536","DOI":"10.3390\/w10111536","article-title":"Flood prediction using machine learning models: literature review","volume":"10","author":"Mosavi","year":"2018","journal-title":"Water"},{"key":"10.1016\/j.jhydrol.2021.126506_b0340","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2019.124296","article-title":"Streamflow and rainfall forecasting by two long short-term memory-based models","volume":"583","author":"Ni","year":"2020","journal-title":"J. Hydrol."},{"issue":"1\u20132","key":"10.1016\/j.jhydrol.2021.126506_b0345","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.jhydrol.2011.03.002","article-title":"Two-hybrid Artificial Intelligence approaches for modeling rainfall-runoff process","volume":"402","author":"Nourani","year":"2011","journal-title":"J. Hydrol."},{"issue":"1","key":"10.1016\/j.jhydrol.2021.126506_b0350","first-page":"11","article-title":"Application of MLP Neural Network and M5P Model Tree in Predicting Streamflow_South Africa","volume":"4","author":"Onyari","year":"2013","journal-title":"Int. J. Innovat. Manage. Technol."},{"key":"10.1016\/j.jhydrol.2021.126506_b0355","first-page":"2825","article-title":"Scikit-learn: machine learning in python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"issue":"3","key":"10.1016\/j.jhydrol.2021.126506_b0360","doi-asserted-by":"crossref","first-page":"54","DOI":"10.3390\/fi12030054","article-title":"Feature selection algorithms as one of the python data analytical tools","volume":"12","author":"Pilnenskiy","year":"2020","journal-title":"Future Internet"},{"key":"10.1016\/j.jhydrol.2021.126506_b0365","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.atmosres.2017.06.014","article-title":"Input selection and performance optimization of ANN-based streamflow forecasts in the drought-prone murray darling basin region using IIS and MODWT algorithm","volume":"197","author":"Prasad","year":"2017","journal-title":"Atmos. Res."},{"key":"10.1016\/j.jhydrol.2021.126506_b0370","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.catena.2019.02.012","article-title":"Weekly soil moisture forecasting with multivariate sequential, ensemble empirical mode decomposition and Boruta-random forest hybridizer algorithm approach","volume":"177","author":"Prasad","year":"2019","journal-title":"Catena"},{"key":"10.1016\/j.jhydrol.2021.126506_b0375","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.renene.2020.01.005","article-title":"A double decomposition-based modelling approach to forecast weekly solar radiation","volume":"152","author":"Prasad","year":"2020","journal-title":"Renew. Energy"},{"key":"10.1016\/j.jhydrol.2021.126506_b0380","doi-asserted-by":"crossref","DOI":"10.1002\/2015WR016959","article-title":"Bootstrap rank-ordered conditional mutual information (broCMI)\u2014A nonlinear input variable selection method for water resources modeling","author":"Quilty","year":"2016","journal-title":"Water Resour. Res."},{"issue":"3","key":"10.1016\/j.jhydrol.2021.126506_b0385","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1007\/s11269-019-02481-8","article-title":"Ensemble based forecasting and optimization framework to optimize releases from water supply reservoirs for flood control","volume":"34","author":"Ramaswamy","year":"2020","journal-title":"Water Resour. Manage."},{"issue":"4","key":"10.1016\/j.jhydrol.2021.126506_b0390","doi-asserted-by":"crossref","first-page":"66","DOI":"10.3390\/hydrology5040066","article-title":"Hydrostats: a python package for characterizing errors between observed and predicted time series","volume":"5","author":"Roberts","year":"2018","journal-title":"Hydrology"},{"issue":"19","key":"10.1016\/j.jhydrol.2021.126506_b0395","doi-asserted-by":"crossref","first-page":"2507","DOI":"10.1093\/bioinformatics\/btm344","article-title":"A review of feature selection techniques in bioinformatics","volume":"23","author":"Saeys","year":"2007","journal-title":"Bioinformatics"},{"issue":"7","key":"10.1016\/j.jhydrol.2021.126506_b0400","doi-asserted-by":"crossref","first-page":"2031","DOI":"10.1007\/s12665-011-1428-7","article-title":"Flow estimations for the Sohu Stream using artificial neural networks","volume":"66","author":"Sattari","year":"2012","journal-title":"Environ. Earth Sci."},{"issue":"2","key":"10.1016\/j.jhydrol.2021.126506_b0405","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1080\/07438141.2012.678927","article-title":"Application of a data mining approach to derive operating rules for the Eleviyan irrigation reservoir","volume":"28","author":"Sattari","year":"2012","journal-title":"Lake Reservoir Manage."},{"issue":"2","key":"10.1016\/j.jhydrol.2021.126506_b0410","doi-asserted-by":"crossref","first-page":"603","DOI":"10.5194\/hess-25-603-2021","article-title":"Comparative analysis of kernel-based versus ANN and deep learning methods in monthly reference evapotranspiration estimation","volume":"25","author":"Sattari","year":"2021","journal-title":"Hydrol. Earth Syst. Sci."},{"issue":"6","key":"10.1016\/j.jhydrol.2021.126506_b0415","doi-asserted-by":"crossref","first-page":"2649","DOI":"10.1016\/j.apm.2011.09.048","article-title":"Performance evaluation of artificial neural network approaches in forecasting reservoir inflow","volume":"36","author":"Sattari","year":"2012","journal-title":"Appl. Math. Model."},{"issue":"3","key":"10.1016\/j.jhydrol.2021.126506_b0420","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1134\/S0097807813030123","article-title":"M5 model tree application in daily river flow forecasting in Sohu Stream, Turkey","volume":"40","author":"Sattari","year":"2013","journal-title":"Water Resour."},{"issue":"7","key":"10.1016\/j.jhydrol.2021.126506_b0425","doi-asserted-by":"crossref","first-page":"2611","DOI":"10.5194\/hess-20-2611-2016","article-title":"Machine learning methods for empirical streamflow simulation: a comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds","volume":"20","author":"Shortridge","year":"2016","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"10.1016\/j.jhydrol.2021.126506_b0430","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.neunet.2018.12.002","article-title":"Deep learning in spiking neural networks","volume":"111","author":"Tavanaei","year":"2019","journal-title":"Neural Networks"},{"key":"10.1016\/j.jhydrol.2021.126506_b0435","unstructured":"The pandas development team. 2010. Data structures for statistical computing in python, McKinney, Proceedings of the 9th Python in Science Conference, Volume 445, 2010. 10.25080\/Majora-92bf1922-00a."},{"key":"10.1016\/j.jhydrol.2021.126506_b0440","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2019.124435","article-title":"Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey Wolf optimization (GWO) algorithm","volume":"582","author":"Tikhamarine","year":"2020","journal-title":"J. Hydrol."},{"key":"10.1016\/j.jhydrol.2021.126506_b0450","doi-asserted-by":"crossref","first-page":"1130","DOI":"10.1016\/j.jhydrol.2015.06.008","article-title":"Flood hazard risk assessment model based on random forest","volume":"527","author":"Wang","year":"2015","journal-title":"J. Hydrol."},{"key":"10.1016\/j.jhydrol.2021.126506_b0455","unstructured":"Wen, Q., Gao, J., Song, X., Sun, L., Xu, H., & Zhu, S. (2018). RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series. http:\/\/arxiv.org\/abs\/1812.01767."},{"key":"10.1016\/j.jhydrol.2021.126506_b0460","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1080\/02723646.1981.10642213","article-title":"On the validation of models","volume":"2","author":"Willmott","year":"1981","journal-title":"Phys. Geogr."},{"key":"10.1016\/j.jhydrol.2021.126506_b0465","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1175\/1520-0477(1982)063<1309:SCOTEO>2.0.CO;2","article-title":"Some comments on the evaluation of model performance","volume":"63","author":"Willmott","year":"1982","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"10.1016\/j.jhydrol.2021.126506_b0470","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2020.125206","article-title":"A physical process and machine learning combined hydrological model for daily streamflow simulations of large watersheds with limited observation data","volume":"590","author":"Yang","year":"2020","journal-title":"J. Hydrol."},{"key":"10.1016\/j.jhydrol.2021.126506_b0475","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1016\/j.jhydrol.2016.09.035","article-title":"Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq","volume":"542","author":"Yaseen","year":"2016","journal-title":"J. Hydrol."},{"key":"10.1016\/j.jhydrol.2021.126506_b0480","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.jhydrol.2018.10.020","article-title":"Complementary data-intelligence model for river flow simulation","volume":"567","author":"Yaseen","year":"2018","journal-title":"J. Hydrol."},{"key":"10.1016\/j.jhydrol.2021.126506_b0490","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2020.124819","article-title":"Forecasting of water level in multiple temperate lakes using machine learning models","volume":"585","author":"Zhu","year":"2020","journal-title":"J. Hydrol."}],"container-title":["Journal of Hydrology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0022169421005539?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0022169421005539?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T15:20:32Z","timestamp":1758295232000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0022169421005539"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9]]},"references-count":90,"alternative-id":["S0022169421005539"],"URL":"https:\/\/doi.org\/10.1016\/j.jhydrol.2021.126506","relation":{},"ISSN":["0022-1694"],"issn-type":[{"value":"0022-1694","type":"print"}],"subject":[],"published":{"date-parts":[[2021,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Artificial intelligence modelling integrated with Singular Spectral analysis and Seasonal-Trend decomposition using Loess approaches for streamflow predictions","name":"articletitle","label":"Article Title"},{"value":"Journal of Hydrology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jhydrol.2021.126506","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2021 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"126506"}}