[1] Haddad, O. B.; Afshar, A.; Mariño, M. A. Honey-Bees Mating Optimization (HBMO) Algorithm: A New Heuristic Approach for Water Resources Optimization. Water Resour. Manag. 2006, 20, 661–680, doi:10.1007/s11269-005-9001-3.
[2] Qi, Y.; Bao, L.; Sun, Y.; Luo, J.; Miao, Q. A Memetic Multi-objective Immune Algorithm for Reservoir Flood Control Operation. Water Resour. Manag. 2016, 30, 2957–2977, doi:10.1007/s11269-016-1317-7.
[3] Haddad, O.; Afshar, A.; Mariño, M. Multireservoir optimisation in discrete and continuous domains. Proc. 2011.
[4] Elzwayie, A.; Afan, H. A.; Allawi, M. F.; El-Shafie, A. Heavy metal monitoring, analysis and prediction in lakes and rivers: state of the art. Environ. Sci. Pollut. Res. 2017, 24, 12104–12117, doi:10.1007/s11356-017-8715-0.
[5] Allawi, M. F.; Othman, F. B.; Afan, H. A.; Ahmed, A. N.; Hossain, M. S.; Fai, C. M.; El-Shafie, A. Reservoir evaporation prediction modeling based on artificial intelligence methods. Water (Switzerland) 2019, doi:10.3390/w11061226.
[6] Chen, L.; McPhee, J.; Yeh, W. W.-G. A diversified multiobjective GA for optimizing reservoir rule curves. Adv. Water Resour. 2007, 30, 1082–1093, doi:10.1016/j.advwatres.2006.10.001.
[7] Osman, A.; Afan, H. A.; Allawi, M. F.; Jaafar, O.; Noureldin, A.; Hamzah, F. M.; Ahmed, A. N.; El-shafie, A. Adaptive Fast Orthogonal Search (FOS) algorithm for forecasting streamflow. J. Hydrol. 2020, 586, 124896, doi:10.1016/j.jhydrol.2020.124896.
[8] Kim, Y.-O.; Eum, H.-I.; Lee, E.-G.; Ko, I. H. Optimizing Operational Policies of a Korean Multireservoir System Using Sampling Stochastic Dynamic Programming with Ensemble Streamflow Prediction. J. Water Resour. Plan. Manag. 2007, 133, 4–14, doi:10.1061/(ASCE)0733-9496(2007)133:1(4).
[9] Archibald, T. W.; McKinnon, K. I. M.; Thomas, L. C. Modeling the operation of multireservoir systems using decomposition and stochastic dynamic programming. Nav. Res. Logist. 2006, 53, 217–225, doi:10.1002/nav.20134.
[10] Sedki, A.; Ouazar, D. Hybrid particle swarm optimization and differential evolution for optimal design of water distribution systems. Adv. Eng. Informatics 2012, 26, 582–591, doi:10.1016/j.aei.2012.03.007.
[11] Clarke, J.; McLay, L.; McLeskey, J. T. Comparison of genetic algorithm to particle swarm for constrained simulation-based optimization of a geothermal power plant. Adv. Eng. Informatics 2014, 28, 81–90, doi:10.1016/j.aei.2013.12.003.
[12] Nagesh Kumar, D.; Janga Reddy, M. Multipurpose Reservoir Operation Using Particle Swarm Optimization. J. Water Resour. Plan. Manag. 2007, 133, 192–201, doi:10.1061/(ASCE)0733-9496(2007)133:3(192).
[13] Ashofteh, P.-S.; Haddad, O. B.; Loáiciga, H. A. Evaluation of Climatic-Change Impacts on Multiobjective Reservoir Operation with Multiobjective Genetic Programming. J. Water Resour. Plan. Manag. 2015, 141, 4015030, doi:10.1061/(ASCE)WR.1943-5452.0000540.
[14] Chiu, Y.-C.; Chang, L.-C.; Chang, F.-J. Using a hybrid genetic algorithm–simulated annealing algorithm for fuzzy programming of reservoir operation. Hydrol. Process. 2007, 21, 3162–3172, doi:10.1002/hyp.6539.
[15] Momtahen, S.; Dariane, A. B. Direct Search Approaches Using Genetic Algorithms for Optimization of Water Reservoir Operating Policies. J. Water Resour. Plan. Manag. 2007, 133, 202–209, doi:10.1061/(ASCE)0733-9496(2007)133:3(202).
[16] Ashofteh, P.; Haddad, O. Evaluation of climatic-change impacts on multiobjective reservoir operation with multiobjective genetic programming. J. Water 2015.
[17] Chen, S.; Shao, D.; Li, X.; Lei, C. Simulation-Optimization Modeling of Conjunctive Operation of Reservoirs and Ponds for Irrigation of Multiple Crops Using an Improved Artificial Bee Colony Algorithm. Water Resour. Manag. 2016, 30, 2887–2905, doi:10.1007/s11269-016-1277-y.
[18] Ghimire, S.; Deo, R. C.; Downs, N. J.; Raj, N. Self-adaptive differential evolutionary extreme learning machines for long-term solar radiation prediction with remotely-sensed MODIS satellite and Reanalysis atmospheric products in solar-rich cities. Remote Sens. Environ. 2018, 212, 176–198, doi:10.1016/j.rse.2018.05.003.
[19] Hosseini-Moghari, S.; Morovati, R.; Moghadas, M. Optimum operation of reservoir using two evolutionary algorithms: imperialist competitive algorithm (ICA) and cuckoo optimization algorithm (COA). Water Resour. 2015.
[20] Hofmeyer, H.; Davila Delgado, J. M. Automated design studies: Topology versus One-Step Evolutionary Structural Optimisation. Adv. Eng. Informatics 2013, 27, 427–443, doi:10.1016/j.aei.2013.03.003.
[21] Chen, C.-H.; Yao, T.-K.; Kuo, C.-M.; Chen, C.-Y. RETRACTED: Evolutionary design of constructive multilayer feedforward neural network. J. Vib. Control 2013, 19, 2413–2420, doi:10.1177/1077546312456726.
[22] Allawi, M. F.; Jaafar, O.; Mohamad Hamzah, F.; Koting, S. B.; Mohd, N. S. B.; El-Shafie, A. Forecasting hydrological parameters for reservoir system utilizing artificial intelligent models and exploring their influence on operation performance. Knowledge-Based Syst. 2019, 163, 907–926, doi:10.1016/J.KNOSYS.2018.10.013.
[23] Allawi, M. F.; Jaafar, O.; Mohamad Hamzah, F.; Ehteram, M.; Hossain, M. S.; El-Shafie, A. Operating a reservoir system based on the shark machine learning algorithm. Environ. Earth Sci. 2018, 77, 366, doi:10.1007/s12665-018-7546-8.
[24] Allawi, M. F.; Hussain, I. R.; Salman, M. I.; El-Shafie, A. Monthly inflow forecasting utilizing advanced artificial intelligence methods: a case study of Haditha Dam in Iraq. Stoch. Environ. Res. Risk Assess. 2021 2021, 1–20, doi:10.1007/S00477-021-02052-7.
[25] Allawi, M. F.; Jaafar, O.; Ehteram, M.; Mohamad Hamzah, F.; El-Shafie, A. Synchronizing Artificial Intelligence Models for Operating the Dam and Reservoir System. Water Resour. Manag. 2018, 1–17, doi:10.1007/s11269-018-1996-3.
[26] Muttil, N.; Chau, K. W. Neural network and genetic programming for modelling coastal algal blooms. Int. J. Environ. Pollut. 2006, 28, doi:10.1504/IJEP.2006.011208.
[27] Allawi, M. F.; Jaafar, O.; Mohamad Hamzah, F.; Mohd, N. S.; Deo, R. C.; El-Shafie, A. Reservoir inflow forecasting with a modified coactive neuro-fuzzy inference system: a case study for a semi-arid region. Theor. Appl. Climatol. 2017, doi:10.1007/s00704-017-2292-5.
[28] Allawi, M. F.; Othman, F. B.; Afan, H. A.; Ahmed, A. N.; Hossain, M. S.; Fai, C. M.; El-Shafie, A.; Allawi, M. F.; Binti Othman, F.; Afan, H. A.; Ahmed, A. N.; Hossain, M. S.; Fai, C. M.; El-Shafie, A. Reservoir Evaporation Prediction Modeling Based on Artificial Intelligence Methods. Water 2019, Vol. 11, Page 1226 2019, 11, 1226, doi:10.3390/W11061226.
[29] Lee, Y.-S.; Tong, L.-I. Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming. Knowledge-Based Syst. 2011, 24, 66–72, doi:10.1016/J.KNOSYS.2010.07.006.
[30] Wang, W.; Rivard, H.; Zmeureanu, R. An object-oriented framework for simulation-based green building design optimization with genetic algorithms. Adv. Eng. Informatics 2005, 19, 5–23, doi:10.1016/j.aei.2005.03.002.
[31] Eray, O.; Mert, C.; Kisi, O. Comparison of multi-gene genetic programming and dynamic evolving neural-fuzzy inference system in modeling pan evaporation. Hydrol. Res. 2017, nh2017076, doi:10.2166/nh.2017.076.
[32] Wafae, E. H.; Driss, O.; Bouziane, A.; Hasnaoui, M. D. Genetic Algorithm applied to reservoir operation optimization with emphasis on the Moroccan context. In 2016 3rd International Conference on Logistics Operations Management (GOL); IEEE, 2016; pp. 1–4.
[33] El-Shafie, A. H.; El-Manadely, M. S. An integrated neural network stochastic dynamic programming model for optimizing the operation policy of Aswan High Dam. Hydrol. Res. 2010, 42, 50, doi:10.2166/nh.2010.043.
[34] Hashimoto, T.; Stedinger, J. R.; Loucks, D. P. Reliability, resiliency, and vulnerability criteria for water resource system performance evaluation. Water Resour. Res. 1982, 18, 14–20, doi:10.1029/WR018i001p00014.