Hybrid forecasting model for non-stationary daily runoff series: A case study in the Han River Basin, China
Tài liệu tham khảo
Aggarwal, 2012, Stage and discharge forecasting by SVM and ANN techniques, Water Resour. Manage., 26, 3705, 10.1007/s11269-012-0098-x
Al-Musaylh, 2018, Two-phase particle swarm optimized-support vector regression hybrid model integrated with improved empirical mode decomposition with adaptive noise for multiple-horizon electricity demand forecasting, Appl. Energy, 217, 422, 10.1016/j.apenergy.2018.02.140
Amiri, 2015, Forecasting daily river flows using nonlinear time series models, J. Hydrol., 527, 1054, 10.1016/j.jhydrol.2015.05.048
Bai, 2016, Daily reservoir inflow forecasting using multiscale deep feature learning with hybrid models, J. Hydrol., 532, 193, 10.1016/j.jhydrol.2015.11.011
Bittelli, 2010, Development and testing of a physically based, three-dimensional model of surface and subsurface hydrology, Adv. Water Resour., 33, 106, 10.1016/j.advwatres.2009.10.013
Cheng, 2015, A social learning particle swarm optimization algorithm for scalable optimization, Inf. Sci., 291, 43, 10.1016/j.ins.2014.08.039
Ciresan, 2010, Deep, big, simple neural nets for handwritten digit recognition, Neural Comput., 22, 3207, 10.1162/NECO_a_00052
Collobert, 2011, Natural language processing (almost) from scratch, J. Mach. Learn. Res., 12, 2493
Dragomiretskiy, 2014, Variational mode decomposition, IEEE Trans. Signal Process., 62, 531, 10.1109/TSP.2013.2288675
Fan, 2016, Probabilistic prediction for monthly streamflow through coupling stepwise cluster analysis and quantile regression methods, Water Resour. Manage., 30, 5313, 10.1007/s11269-016-1489-1
Fan, 2017, Development of PCA-based cluster quantile regression (PCA-CQR) framework for streamflow prediction: application to the Xiangxi riverwatershed, China, Appl. Soft Comput., 51, 280, 10.1016/j.asoc.2016.11.039
Gan, 2014, A comprehensive evaluation of various sensitivity analysis methods: a case study with a hydrological model, Environ. Modell. Software, 51, 269, 10.1016/j.envsoft.2013.09.031
Geng, 2018, A new deep belief network based on RBM with glial chains, Inf. Sci., 463, 294, 10.1016/j.ins.2018.06.043
Gopalan, 2018, An effective storage function model for an urban watershed in terms of hydrograph reproducibility and Akaike information criterion, J. Hydrol., 563, 657, 10.1016/j.jhydrol.2018.06.035
Hadi, 2018, Streamflow forecasting using four wavelet transformation combinations approaches with data-driven models: a comparative study, Water Resour. Manage., 32, 4661, 10.1007/s11269-018-2077-3
He, 2019, Daily runoff forecasting using a hybrid model based on variational mode decomposition and deep neural networks, Water Resour. Manage., 33, 1571, 10.1007/s11269-019-2183-x
Hinton, 2012, Deep neural networks for acoustic modeling in speech recognition, IEEE Signal Process Mag., 29, 82, 10.1109/MSP.2012.2205597
Hinton, 2002, Training products of experts by minimizing contrastive divergence, Neural Comput., 14, 1771, 10.1162/089976602760128018
Hinton, 2006, Reducing the dimensionality of data with neural networks, Science, 313, 504, 10.1126/science.1127647
Huang, 2016, Mechanical fault diagnosis of high voltage circuit breakers based on variational mode decomposition and multi-layer classifier, Sensors, 16, 10.3390/s16111887
Huang, 2014, Monthly streamflow prediction using modified EMD-based support vector machine, J. Hydrol., 511, 764, 10.1016/j.jhydrol.2014.01.062
Kang, 2018, Efficient synthesis of antenna pattern using improved PSO for spaceborne SAR performance and imaging in presence of element failure, IEEE Sens. J., 18, 6576, 10.1109/JSEN.2018.2850920
Krizhevsky, 2017, ImageNet classification with deep convolutional neural networks, Commun. ACM, 60, 84, 10.1145/3065386
Kumar, 2015, Reservoir inflow forecasting using ensemble models based on neural networks, wavelet analysis and bootstrap method, Water Resour. Manage., 29, 4863, 10.1007/s11269-015-1095-7
Lahmiri, 2015, Long memory in international financial markets trends and short movements during 2008 financial crisis based on variational mode decomposition and detrended fluctuation analysis, Phys. A-Stat. Mech. Appl., 437, 130, 10.1016/j.physa.2015.05.067
Lahmiri, 2015, Physiological signal denoising with variational mode decomposition and weighted reconstruction after DWT thresholding, 806
Lee, 2012, Variational assimilation of streamflow into operational distributed hydrologic models: effect of spatiotemporal scale of adjustment, Hydrol. Earth Syst. Sci., 16, 2233, 10.5194/hess-16-2233-2012
Liu, 2018, Smart multi-step deep learning model for wind speed forecasting based on variational mode decomposition, singular spectrum analysis, LSTM network and ELM, Energy Convers. Manage., 159, 54, 10.1016/j.enconman.2018.01.010
Majumder, 2018, Variational mode decomposition based low rank robust kernel extreme learning machine for solar irradiation forecasting, Energy Convers. Manage., 171, 787, 10.1016/j.enconman.2018.06.021
McInerney, 2017, Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modeling heteroscedastic residual errors, Water Resour. Res., 53, 2199, 10.1002/2016WR019168
Moeeni, 2017, Stochastic model stationarization by eliminating the periodic term and its effect on time series prediction, J. Hydrol., 547, 348, 10.1016/j.jhydrol.2017.02.012
Myronidis, 2018, Streamflow and hydrological drought trend analysis and forecasting in Cyprus, Water Resour. Manage., 32, 1759, 10.1007/s11269-018-1902-z
Naik, 2019, A multi-objective wind speed and wind power prediction interval forecasting using variational modes decomposition based Multi-kernel robust ridge regression, Renew. Energy, 136, 701, 10.1016/j.renene.2019.01.006
Napolitano, 2011, Impact of EMD decomposition and random initialisation of weights in ANN hindcasting of daily stream flow series: an empirical examination, J. Hydrol., 406, 199, 10.1016/j.jhydrol.2011.06.015
Noori, 2011, Assessment of input variables determination on the SVM model performance using PCA, Gamma test, and forward selection techniques for monthly stream flow prediction, J. Hydrol., 401, 177, 10.1016/j.jhydrol.2011.02.021
Partington, 2012, Evaluation of outputs from automated baseflow separation methods against simulated baseflow from a physically based, surface water-groundwater flow model, J. Hydrol., 458, 28, 10.1016/j.jhydrol.2012.06.029
Sankaran, 2016, Analyzing the hydroclimatic teleconnections of summer monsoon rainfall in Kerala, India, using multivariate empirical mode decomposition and time-dependent intrinsic correlation, IEEE Geosci. Remote Sensing Lett., 13, 1221, 10.1109/LGRS.2016.2577598
Sharghi, 2018, Emotional ANN (EANN) and wavelet-ANN (WANN) approaches for markovian and seasonal based modeling of rainfall-runoff process, Water Resour. Manage., 32, 10.1007/s11269-018-2000-y
Shi, 2018, Hourly day-ahead wind power prediction using the hybrid model of variational model decomposition and long short-term memory, Energies, 11, 20, 10.3390/en11113227
Sun, 2016, A carbon price forecasting model based on variational mode decomposition and spiking neural networks, Energies, 9, 10.3390/en9010054
Sze, 2017, Efficient processing of deep neural networks: a tutorial and survey, Proc. IEEE, 105, 2295, 10.1109/JPROC.2017.2761740
Wang, 2009, A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series, J. Hydrol., 374, 294, 10.1016/j.jhydrol.2009.06.019
Wang, 2013, Improved annual rainfall-runoff forecasting using PSO-SVM model based on EEMD, J. Hydroinf., 15, 1377, 10.2166/hydro.2013.134
Wen, 2019, Two-phase extreme learning machines integrated with the complete ensemble empirical mode decomposition with adaptive noise algorithm for multi-scale runoff prediction problems, J. Hydrol., 570, 167, 10.1016/j.jhydrol.2018.12.060
Yaseen, 2015, Artificial intelligence based models for stream-flow forecasting: 2000–2015, J. Hydrol., 530, 829, 10.1016/j.jhydrol.2015.10.038
Yin, 2015, A propagating mode extraction algorithm for microwave waveguide using variational mode decomposition, Meas. Sci. Technol., 26, 10.1088/0957-0233/26/9/095009
Yoon, 2011, A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer, J. Hydrol., 396, 128, 10.1016/j.jhydrol.2010.11.002
Yu, 2018, Forward prediction of runoff data in data-scarce basins with an improved ensemble empirical mode decomposition (EEMD) model, Water, 10, 10.3390/w10040388
Zhang, 2019, Wind power prediction based on variational mode decomposition multi-frequency combinations, J. Modern Power Syst. Clean Energy, 7, 281, 10.1007/s40565-018-0471-8
Zhang, 2015, Are hybrid models integrated with data preprocessing techniques suitable for monthly streamflow forecasting? Some experiment evidences, J. Hydrol., 530, 137, 10.1016/j.jhydrol.2015.09.047
Zheng, 2013, Hanjiang River Basin precipitation multiple time scales characteristics and with circulation factors correlation analysis, Adv. Earth Sci., 28, 618
Zhou, 2017, Systematic impact assessment on inter-basin water transfer projects of the Hanjiang River Basin in China, J. Hydrol., 553, 584, 10.1016/j.jhydrol.2017.08.039
Zhou, 2019, Explore an evolutionary recurrent ANFIS for modelling multi-step-ahead flood forecasts, J. Hydrol., 570, 343, 10.1016/j.jhydrol.2018.12.040
Zhu, 2016, Streamflow estimation by support vector machine coupled with different methods of time series decomposition in the upper reaches of Yangtze River, China, Environ. Earth Sci., 75, 10.1007/s12665-016-5337-7
Zrira, 2018, Discriminative deep belief network for indoor environment classification using global visual features, Cognit. Comput., 10, 437, 10.1007/s12559-017-9534-9