Applying wavelet transformation and artificial neural networks to develop forecasting-based reservoir operating rule curves
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Seyed Mohammad Ashrafi | Arash Adib | Ehsan Mostaghimzadeh | S. M. Ashrafi | A. Adib | Ehsan Mostaghimzadeh
[1] Kumaraswamy Ponnambalam,et al. Adaptive forecast-based real-time optimal reservoir operations: application to Lake Urmia , 2019, Journal of Hydroinformatics.
[2] Xiao-Lin Wang,et al. A new approach of obtaining reservoir operation rules: Artificial immune recognition system , 2011, Expert Syst. Appl..
[3] Honggang Zhang,et al. A reservoir flood forecasting and control system for China / Un système chinois de prévision et de contrôle de crue en barrage , 2004 .
[4] S. Schneider,et al. Climate Change 2001: Synthesis Report: A contribution of Working Groups I, II, and III to the Third Assessment Report of the Intergovernmental Panel on Climate Change , 2001 .
[5] Zbigniew R. Struzik,et al. The Haar Wavelet Transform in the Time Series Similarity Paradigm , 1999, PKDD.
[6] Yong Peng,et al. The application of ensemble precipitation forecasts to reservoir operation , 2018, Water Supply.
[7] R. Deo,et al. Very short‐term reactive forecasting of the solar ultraviolet index using an extreme learning machine integrated with the solar zenith angle , 2017, Environmental research.
[8] Adebayo Adeloye,et al. Inflow forecasting using Artificial Neural Networks for reservoir operation , 2016 .
[9] Chuntian Cheng,et al. Heuristic Methods for Reservoir Monthly Inflow Forecasting: A Case Study of Xinfengjiang Reservoir in Pearl River, China , 2015 .
[10] D. Legates,et al. Evaluating the use of “goodness‐of‐fit” Measures in hydrologic and hydroclimatic model validation , 1999 .
[11] N. Voisin,et al. Inferred inflow forecast horizons guiding reservoir release decisions across the United States , 2020 .
[12] Chao Deng,et al. Identifying Explicit Formulation of Operating Rules for Multi-Reservoir Systems Using Genetic Programming , 2014, Water Resources Management.
[13] S. M. Ashrafi. Investigating Pareto Front Extreme Policies Using Semi-distributed Simulation Model for Great Karun River Basin , 2019 .
[14] T. Chai,et al. Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature , 2014 .
[15] A. Shamseldin. Application of a neural network technique to rainfall-runoff modelling , 1997 .
[16] Omid Bozorg Haddad,et al. Adaptive Reservoir Operation Rules Under Climatic Change , 2015, Water Resources Management.
[17] Vahid Nourani,et al. Forecasting Daily Precipitation Using Hybrid Model of Wavelet-Artificial Neural Network and Comparison with Adaptive Neurofuzzy Inference System (Case Study: Verayneh Station, Nahavand) , 2014 .
[18] Aris P. Georgakakos,et al. Assessment of reservoir system variable forecasts , 2015 .
[19] John H. Holland,et al. Genetic Algorithms and the Optimal Allocation of Trials , 1973, SIAM J. Comput..
[20] Alireza B. Dariane,et al. Optimization of multireservoir systems operation using modified direct search genetic algorithm. , 2009 .
[21] Jie Li,et al. Deriving reservoir operation rule based on Bayesian deep learning method considering multiple uncertainties , 2019 .
[22] D. E. Rheinheimer,et al. Parameter uncertainty analysis of reservoir operating rules based on implicit stochastic optimization. , 2014 .
[23] Soroosh Sorooshian,et al. Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information , 2017 .
[24] Youngkyu Jin,et al. Comparative Effectiveness of Reservoir Operation Applying Hedging Rules Based on Available Water and Beginning Storage to Cope with Droughts , 2019, Water Resources Management.
[25] Alireza B. Dariane,et al. Coupled Operating Rules for Optimal Operation of Multi-Reservoir Systems , 2017, Water Resources Management.
[26] Slobodan P. Simonovic,et al. Integrated Reservoir Management System for Adaptation to Climate Change Impacts in the Upper Thames River Basin , 2009 .
[27] Shinjiro Kanae,et al. Improved Forecasting of Extreme Monthly Reservoir Inflow Using an Analogue-Based Forecasting Method: A Case Study of the Sirikit Dam in Thailand , 2018, Water.
[29] Ali Haghighi,et al. Simultaneous Optimization of Operating Rules and Rule Curves for Multireservoir Systems Using a Self-Adaptive Simulation-GA Model , 2016 .
[30] Xiao Wang,et al. A Forecast-Based Operation(FBO) Mode for Reservoir Flood Control Using Forecast Cumulative Net Rainfall , 2019, Water Resources Management.
[31] 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.
[32] Jhih-Shyang Shih,et al. Water‐Supply Operations during Drought: Continuous Hedging Rule , 1994 .
[33] Pan Liu,et al. Deriving Optimal Refill Rules for Multi-Purpose Reservoir Operation , 2011 .
[34] Andrew Metcalfe,et al. Wavelet-Based Rainfall-Stream Flow Models for the Southeast Murray Darling Basin , 2014 .
[35] Chuntian Cheng,et al. A comparison of performance of several artificial intelligence , 2009 .
[36] He Li,et al. Hybrid Two-Stage Stochastic Methods Using Scenario-Based Forecasts for Reservoir Refill Operations , 2018 .
[37] W. Pitts,et al. A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.
[38] Pan Yang,et al. Fuzzy Inference System for Robust Rule-Based Reservoir Operation under Nonstationary Inflows , 2017 .
[39] Pan Liu,et al. Identifying changing patterns of reservoir operating rules under various inflow alteration scenarios , 2017 .
[40] Stéphane Mallat,et al. A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..
[42] S. M. Ashrafi,et al. Hydrological Assessment of Daily Satellite Precipitation Products over a Basin in Iran , 2016 .
[43] Gustavo Barbosa Lima da Silva,et al. Daily streamflow forecasting using a wavelet transform and artificial neural network hybrid models , 2014 .
[44] Othman Jaafar,et al. Forecasting hydrological parameters for reservoir system utilizing artificial intelligent models and exploring their influence on operation performance , 2019, Knowl. Based Syst..
[45] Seyed Mohammad Ashrafi,et al. Developing Self-adaptive Melody Search Algorithm for Optimal Operation of Multi-reservoir Systems , 2017 .
[46] L. Eisel. Chance constrained reservoir model , 1972 .
[47] Zhen Lin,et al. Choosing SNPs using feature selection , 2005, 2005 IEEE Computational Systems Bioinformatics Conference (CSB'05).
[48] P. Krause,et al. COMPARISON OF DIFFERENT EFFICIENCY CRITERIA FOR HYDROLOGICAL MODEL ASSESSMENT , 2005 .
[49] G. K. Young. Finding Reservoir Operating Rules , 1967 .
[50] N. Bhaskar,et al. Derivation of monthly reservoir release policies , 1980 .
[51] Jan Adamowski,et al. Wavelet‐based multiscale performance analysis: An approach to assess and improve hydrological models , 2014 .
[52] Daniel P. Loucks,et al. Some Comments on Linear Decision Rules and Chance Constraints , 1970 .
[53] C. Revelle,et al. The Linear Decision Rule in Reservoir Management and Design: 1, Development of the Stochastic Model , 1969 .
[54] Steffi Naumann,et al. Short-Term Reservoir Optimization for Flood Mitigation under Meteorological and Hydrological Forecast Uncertainty , 2015, Water Resources Management.