Short-term streamflow time series prediction model by machine learning tool based on data preprocessing technique and swarm intelligence algorithm

ABSTRACT Accurate streamflow prediction information is of great importance for water resource planning and management. The goal of this research is to develop a hybrid model for forecasting short-term runoff time series, where the variational mode decomposition (VMD) is first used to decompose the original nonlinear natural streamflow into numerous subcomponents with different frequencies and resolutions. Second, the extreme learning machine (ELM) is used to excavate the complicated input–output relationship hidden in each subcomponent, and the emerging sine cosine algorithm (SCA) is used to determine the suitable network parameter for each ELM model. Finally, the forecasting results of all the modelled subcomponents are summarized to form the forecasting result for original streamflow. Based on several statistical evaluation measures, the feasibility of the hybrid method is investigated in runoff forecasting for Danjiangkou Reservoir in China. The results indicate that the hybrid method can produce superior forecasting results compared to several control methods, providing accurate streamflow prediction information for operators.

[1]  Wen-jing Niu,et al.  Multireservoir system operation optimization by hybrid quantum-behaved particle swarm optimization and heuristic constraint handling technique , 2020 .

[2]  Kwok-Wing Chau,et al.  Data-driven models for monthly streamflow time series prediction , 2010, Eng. Appl. Artif. Intell..

[3]  Xin Wang,et al.  Optimized VMD-Wavelet Packet Threshold Denoising based on Cross-Correlation Analysis , 2018 .

[4]  Shiping Wen,et al.  Sliding mode control of neural networks via continuous or periodic sampling event-triggering algorithm , 2020, Neural Networks.

[5]  Xuebin Xu,et al.  Novel Method Based on Variational Mode Decomposition and a Random Discriminative Projection Extreme Learning Machine for Multiple Power Quality Disturbance Recognition , 2019, IEEE Transactions on Industrial Informatics.

[6]  Feng Li,et al.  Partial Discharge Fault Diagnosis Based on Multi-Scale Dispersion Entropy and a Hypersphere Multiclass Support Vector Machine , 2019, Entropy.

[7]  Kwok-wing Chau,et al.  Data-driven input variable selection for rainfall-runoff modeling using binary-coded particle swarm optimization and Extreme Learning Machines , 2015 .

[8]  Seyedali Mirjalili,et al.  SCA: A Sine Cosine Algorithm for solving optimization problems , 2016, Knowl. Based Syst..

[9]  Alex J. Cannon,et al.  Improving gridded snow water equivalent products in British Columbia, Canada: multi-source data fusion by neural network models , 2017 .

[10]  K. W. Chau,et al.  River stage prediction based on a distributed support vector regression , 2008 .

[11]  Kwok-Wing Chau,et al.  Prediction of rainfall time series using modular soft computingmethods , 2013, Eng. Appl. Artif. Intell..

[12]  James C. Bennett,et al.  Quantifying predictive uncertainty of streamflow forecasts based on a Bayesian joint probability model , 2015 .

[13]  Evaluation of Economic and Hydrologic Impacts of Unified Water Flow Regulation in the Yellow River Basin , 2009 .

[14]  Shiping Wen,et al.  Global exponential synchronization of delayed memristive neural networks with reaction-diffusion terms , 2019, Neural Networks.

[15]  Xinan Yin,et al.  OPTIMIZING ENVIRONMENTAL FLOWS BELOW DAMS , 2012 .

[16]  Paulin Coulibaly,et al.  Improving Daily Reservoir Inflow Forecasts with Model Combination , 2005 .

[17]  Sen Wang,et al.  Operation rule derivation of hydropower reservoir by k-means clustering method and extreme learning machine based on particle swarm optimization , 2019, Journal of Hydrology.

[18]  Qiang Huang,et al.  Simulation with RBF Neural Network Model for Reservoir Operation Rules , 2010 .

[19]  Fu Chao Liu,et al.  Hybrid forecasting model for non-stationary daily runoff series: A case study in the Han River Basin, China , 2019, Journal of Hydrology.

[20]  Shiping Wen,et al.  Passivity and passification of memristive recurrent neural networks with multi-proportional delays and impulse , 2020, Appl. Math. Comput..

[21]  Chuntian Cheng,et al.  Optimization of hydropower reservoirs operation balancing generation benefit and ecological requirement with parallel multi-objective genetic algorithm , 2018, Energy.

[22]  Salah Kamel,et al.  Efficient optimization technique for multiple DG allocation in distribution networks , 2020, Appl. Soft Comput..

[23]  Chuntian Cheng,et al.  Comparison of three global optimization algorithms for calibration of the Xinanjiang model parameters , 2013 .

[24]  Chuan Li,et al.  Reservoir Inflow Forecast Using a Clustered Random Deep Fusion Approach in the Three Gorges Reservoir, China , 2018, Journal of Hydrologic Engineering.

[25]  J. Stedinger,et al.  Multisite ARMA(1,1) and Disaggregation Models for Annual Streamflow Generation , 1985 .

[26]  F. Chang,et al.  Synergistic gains from the multi-objective optimal operation of cascade reservoirs in the Upper Yellow River basin , 2015 .

[27]  Chuntian Cheng,et al.  Forecasting reservoir monthly runoff via ensemble empirical mode decomposition and extreme learning machine optimized by an improved gravitational search algorithm , 2019, Appl. Soft Comput..

[28]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[29]  Kusum Deep,et al.  Improved sine cosine algorithm with crossover scheme for global optimization , 2019, Knowl. Based Syst..

[30]  Chao Ma,et al.  Short-term optimal operation of Three-gorge and Gezhouba cascade hydropower stations in non-flood season with operation rules from data mining , 2013 .

[31]  Yanbin Yuan,et al.  Multi-objective optimal power flow based on improved strength Pareto evolutionary algorithm , 2017 .

[32]  Aaron C. Zecchin,et al.  An Adaptive Convergence-Trajectory Controlled Ant Colony Optimization Algorithm With Application to Water Distribution System Design Problems , 2017, IEEE Transactions on Evolutionary Computation.

[33]  L. Karthikeyan,et al.  Predictability of nonstationary time series using wavelet and EMD based ARMA models , 2013 .

[34]  Ali Akbar Abdoos,et al.  Combined VMD-SVM based feature selection method for classification of power quality events , 2016, Appl. Soft Comput..

[35]  Ting Zhou,et al.  Operating Rules Derivation of Jinsha Reservoirs System with Parameter Calibrated Support Vector Regression , 2014, Water Resources Management.

[36]  François Anctil,et al.  A soil moisture index as an auxiliary ANN input for stream flow forecasting , 2004 .

[37]  Zhigang Zeng,et al.  Passivity analysis of delayed reaction-diffusion memristor-based neural networks , 2019, Neural Networks.

[38]  Guowei Cai,et al.  Harmonic Detection for Power Grids Using Adaptive Variational Mode Decomposition , 2019, Energies.

[39]  P. E. O'connell,et al.  River flow forecasting through conceptual models part III - The Ray catchment at Grendon Underwood , 1970 .

[40]  X. Kong,et al.  Seismic fragility for high CFRDs based on deformation and damage index through incremental dynamic analysis , 2018 .

[41]  Hao Zhong,et al.  Vibration trend measurement for a hydropower generator based on optimal variational mode decomposition and an LSSVM improved with chaotic sine cosine algorithm optimization , 2018, Measurement Science and Technology.

[42]  Chuntian Cheng,et al.  A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series , 2009 .

[43]  Bernard Bobée,et al.  Daily reservoir inflow forecasting using artificial neural networks with stopped training approach , 2000 .

[44]  Chengshi Tian,et al.  A novel two-stage forecasting model based on error factor and ensemble method for multi-step wind power forecasting , 2019, Applied Energy.

[45]  P. E. O'Connell,et al.  River flow forecasting through conceptual models part II - The Brosna catchment at Ferbane , 1970 .

[46]  Kwok-wing Chau,et al.  A hybrid adaptive time-delay neural network model for multi-step-ahead prediction of sunspot activity , 2006 .

[47]  Daniel P. Loucks,et al.  Artificial Neural Network Models of Watershed Nutrient Loading , 2012, Water Resources Management.

[48]  Q. Shao,et al.  Assessing the effects of adaptation measures on optimal water resources allocation under varied water availability conditions , 2018 .

[49]  Xiaoyong Liu,et al.  Parameter optimization of support vector regression based on sine cosine algorithm , 2018, Expert Syst. Appl..

[50]  Xiang Zeng,et al.  Improved dynamic programming for parallel reservoir system operation optimization , 2019, Advances in Water Resources.

[51]  Guang-Bin Huang,et al.  Convex incremental extreme learning machine , 2007, Neurocomputing.

[52]  Huaiwei Sun,et al.  Empirical investigation on modeling solar radiation series with ARMA–GARCH models , 2015 .

[53]  Jery R. Stedinger,et al.  Reservoir optimization using sampling SDP with ensemble streamflow prediction (ESP) forecasts , 2001 .

[54]  Soroosh Sorooshian,et al.  Simulating California reservoir operation using the classification and regression‐tree algorithm combined with a shuffled cross‐validation scheme , 2015 .

[55]  Chuntian Cheng,et al.  Calibration of Xinanjiang model parameters using hybrid genetic algorithm based fuzzy optimal model , 2012 .

[56]  J. Herman,et al.  Balancing Flood Risk and Water Supply in California: Policy Search Integrating Short‐Term Forecast Ensembles With Conjunctive Use , 2018, Water Resources Research.

[57]  Degao Zou,et al.  Response to the discussion on “Seismic reliability assessment of earth-rockfill dam slopes considering strain-softening of rockfill based on generalized probability density evolution method” , 2018, Soil Dynamics and Earthquake Engineering.

[58]  Zhigang Zeng,et al.  Sparse fully convolutional network for face labeling , 2019, Neurocomputing.

[59]  Diego Oliva,et al.  An improved Opposition-Based Sine Cosine Algorithm for global optimization , 2017, Expert Syst. Appl..

[60]  Subimal Ghosh,et al.  Prediction of extreme rainfall event using weather pattern recognition and support vector machine classifier , 2013, Theoretical and Applied Climatology.

[61]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[62]  Wen-jing Niu,et al.  Monthly runoff time series prediction by variational mode decomposition and support vector machine based on quantum-behaved particle swarm optimization , 2020 .

[63]  Weiwei Liu,et al.  Generating Realistic Videos From Keyframes With Concatenated GANs , 2019, IEEE Transactions on Circuits and Systems for Video Technology.

[64]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[65]  François Anctil,et al.  Improvement of rainfall-runoff forecasts through mean areal rainfall optimization , 2006 .

[66]  Dominique Zosso,et al.  Variational Mode Decomposition , 2014, IEEE Transactions on Signal Processing.

[67]  Shiping Wen,et al.  Synchronization of memristive neural networks with leakage delay and parameters mismatch via event-triggered control , 2019, Neural Networks.

[68]  Dianhui Wang,et al.  Extreme learning machines: a survey , 2011, Int. J. Mach. Learn. Cybern..

[69]  Chuntian Cheng,et al.  Annual Streamflow Time Series Prediction Using Extreme Learning Machine Based on Gravitational Search Algorithm and Variational Mode Decomposition , 2020 .

[70]  Chuntian Cheng,et al.  Linking Nelder–Mead Simplex Direct Search Method into Two-Stage Progressive Optimality Algorithm for Optimal Operation of Cascade Hydropower Reservoirs , 2020 .

[71]  K. Chau,et al.  Mathematical model of water quality rehabilitation with rainwater utilisation: a case study at Haigang , 2006 .

[72]  Mark A. Turnquist,et al.  Optimal Recovery from Disruptions in Water Distribution Networks , 2016, Comput. Aided Civ. Infrastructure Eng..

[73]  Pan Liu,et al.  Optimal design of seasonal flood limited water levels and its application for the Three Gorges Reservoir , 2015 .

[74]  Jingjing Xie,et al.  Daily reservoir inflow forecasting using multiscale deep feature learning with hybrid models , 2016 .

[75]  Skeletonizing Pipes in Series within Urban Water Distribution Systems Using a Transient-Based Method , 2019, Journal of Hydraulic Engineering.

[76]  Alex J. Cannon,et al.  Daily streamflow forecasting by machine learning methods with weather and climate inputs , 2012 .

[77]  Zhen-Guo Song,et al.  Peak Operation Problem Solving for Hydropower Reservoirs by Elite-Guide Sine Cosine Algorithm with Gaussian Local Search and Random Mutation , 2019, Energies.

[78]  Zhigang Zeng,et al.  CLU-CNNs: Object detection for medical images , 2019, Neurocomputing.

[79]  Xu Chen,et al.  An opposition-based sine cosine approach with local search for parameter estimation of photovoltaic models , 2019, Energy Conversion and Management.

[80]  Chuntian Cheng,et al.  Using support vector machines for long-term discharge prediction , 2006 .

[81]  Kwok-wing Chau,et al.  Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition , 2015, Water Resources Management.

[82]  Zaher Mundher Yaseen,et al.  An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction , 2019, Journal of Hydrology.

[83]  Jery R. Stedinger,et al.  Probabilities for ensemble forecasts reflecting climate information , 2010 .

[84]  Aranildo R. Lima,et al.  Forecasting daily streamflow using online sequential extreme learning machines , 2016 .

[85]  Yuanyuan Liu,et al.  Optimal Operation of Multi-reservoir Systems Considering Time-lags of Flood Routing , 2015, Water Resources Management.

[86]  Tao Sun,et al.  Eco-compensation standards for sustaining high flow events below hydropower plants , 2018 .

[87]  Aranildo R. Lima,et al.  Variable complexity online sequential extreme learning machine, with applications to streamflow prediction , 2017 .

[88]  Soroosh Sorooshian,et al.  Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information , 2017 .

[89]  Kai Wang,et al.  Multi-step short-term wind speed forecasting approach based on multi-scale dominant ingredient chaotic analysis, improved hybrid GWO-SCA optimization and ELM , 2019, Energy Conversion and Management.

[90]  Sen Wang,et al.  An effective three-stage hybrid optimization method for source-network-load power generation of cascade hydropower reservoirs serving multiple interconnected power grids , 2020 .

[91]  K. Chau,et al.  Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition. , 2015, Environmental research.

[92]  Wen-jing Niu,et al.  Ecological operation of cascade hydropower reservoirs by elite-guide gravitational search algorithm with Lévy flight local search and mutation , 2020 .

[93]  Pradipta Kishore Dash,et al.  Hybrid Variational Mode Decomposition and evolutionary robust kernel extreme learning machine for stock price and movement prediction on daily basis , 2019, Appl. Soft Comput..

[94]  Aboul Ella Hassanien,et al.  ASCA-PSO: Adaptive sine cosine optimization algorithm integrated with particle swarm for pairwise local sequence alignment , 2018, Expert Syst. Appl..

[95]  Ravi Kumar Jatoth,et al.  Hybridizing sine cosine algorithm with differential evolution for global optimization and object tracking , 2018, Appl. Soft Comput..

[96]  Yang Zhou,et al.  Verification of stochastic seismic analysis method and seismic performance evaluation based on multi-indices for high CFRDs , 2020 .

[97]  Soroosh Sorooshian,et al.  Evaluating the streamflow simulation capability of PERSIANN-CDR daily rainfall products in two river basins on the Tibetan Plateau , 2016 .

[98]  Xiangang Peng,et al.  A novel wind speed forecasting based on hybrid decomposition and online sequential outlier robust extreme learning machine , 2019, Energy Conversion and Management.