Abstract
Efficacious operation for dam and reservoir system could guarantee not only a defenselessness policy against natural hazard but also identify rule to meet the water demand. Successful operation of dam and reservoir systems to ensure optimal use of water resources could be unattainable without accurate and reliable simulation models. According to the highly stochastic nature of hydrologic parameters, developing accurate predictive model that efficiently mimic such a complex pattern is an increasing domain of research. During the last two decades, artificial intelligence (AI) techniques have been significantly utilized for attaining a robust modeling to handle different stochastic hydrological parameters. AI techniques have also shown considerable progress in finding optimal rules for reservoir operation. This review research explores the history of developing AI in reservoir inflow forecasting and prediction of evaporation from a reservoir as the major components of the reservoir simulation. In addition, critical assessment of the advantages and disadvantages of integrated AI simulation methods with optimization methods has been reported. Future research on the potential of utilizing new innovative methods based AI techniques for reservoir simulation and optimization models have also been discussed. Finally, proposal for the new mathematical procedure to accomplish the realistic evaluation of the whole optimization model performance (reliability, resilience, and vulnerability indices) has been recommended.
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References
Abghari H, Ahmadi H, Besharat S, Rezaverdinejad V (2012) Prediction of daily Pan evaporation using wavelet neural networks. Water Resour Manag 26:3639–3652. https://doi.org/10.1007/s11269-012-0096-z
Affenzeller M (2009) Genetic algorithms and genetic programming: modern concepts and practical applications. CRC Press
Afshar MH (2012) Large scale reservoir operation by constrained particle swarm optimization algorithms. J Hydro-environment Res 6:75–87. https://doi.org/10.1016/j.jher.2011.04.003
Ahmad A, Razali SFM, Mohamed ZS, El-shafie A (2016) The application of artificial bee colony and gravitational search algorithm in reservoir optimization. Water Resour Manag 30:2497–2516. https://doi.org/10.1007/s11269-016-1304-z
Ahmadi M, Bozorg Haddad O, Mariño MA (2014) Extraction of flexible multi-objective real-time reservoir operation rules. Water Resour Manag 28:131–147. https://doi.org/10.1007/s11269-013-0476-z
Ahmadianfar I, Adib A, Salarijazi M (2016) Optimizing multireservoir operation: hybrid of bat algorithm and differential evolution. J Water Resour Plan Manag 142:5015010. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000606
Allawi MF, El-Shafie A (2016) Utilizing RBF-NN and ANFIS methods for multi-lead ahead prediction model of evaporation from reservoir. Water Resour Manag 30:4773–4788. https://doi.org/10.1007/s11269-016-1452-1
Asgari H-R, Bozorg Haddad O, Pazoki M, Loáiciga HA (2016) Weed optimization algorithm for optimal reservoir operation. J Irrig Drain Eng 142:4015055. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000963
Ashofteh P-S, Haddad OB, Loáiciga HA (2015) Evaluation of climatic-change impacts on multiobjective reservoir operation with multiobjective genetic programming. J Water Resour Plan Manag 141:4015030. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000540
Awan JA, Bae D (2013) Application of adaptive neuro-fuzzy inference system for dam inflow prediction using long-range weather forecast. In: Eighth International Conference on Digital Information Management (ICDIM 2013) IEEE, pp 247–251
Azizipour M, Ghalenoei V, Afshar MH, Solis SS (2016) Optimal operation of hydropower reservoir systems using weed optimization algorithm. Water Resour Manag 30:3995–4009. https://doi.org/10.1007/s11269-016-1407-6
BAE D-H, DM JEONG, KIM G (2007) Monthly dam inflow forecasts using weather forecasting information and neuro-fuzzy technique. Hydrol Sci J 52:99–113. https://doi.org/10.1623/hysj.52.1.99
Bahrami M, Bozorg-Haddad O, Chu X (2018) Application of cat swarm optimization algorithm for optimal reservoir operation. J Irrig Drain Eng 144:4017057. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001256
Bai Y, Chen Z, Xie J, Li C (2016a) Daily reservoir inflow forecasting using multiscale deep feature learning with hybrid models. J Hydrol 532:193–206. https://doi.org/10.1016/j.jhydrol.2015.11.011
Bai Y, Xie J, Wang X, Li C (2016b) Model fusion approach for monthly reservoir inflow forecasting. J Hydroinf 18:634–650. https://doi.org/10.2166/hydro.2016.141
Baltar AM, Fontane DG (2008) Use of multiobjective particle swarm optimization in water resources management. J Water Resour Plan Manag 134:257–265. https://doi.org/10.1061/(ASCE)0733-9496(2008)134:3(257)
Baydaroğlu Ö, Koçak K (2014) SVR-based prediction of evaporation combined with chaotic approach. J Hydrol 508:356–363. https://doi.org/10.1016/j.jhydrol.2013.11.008
Bozorg-Haddad O, Karimirad I, Seifollahi-Aghmiuni S, Loáiciga HA (2015) Development and application of the bat algorithm for optimizing the operation of reservoir systems. J Water Resour Plan Manag 141:4014097. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000498
Bozorg-Haddad O, Janbaz M, Loáiciga H (2016a) Application of the gravity search algorithm to multi-reservoir operation optimization. Adv Water Resour 98:173–185. https://doi.org/10.1016/J.ADVWATRES.2016.11.001
Bozorg-Haddad O, Zarezadeh-Mehrizi M, Abdi-Dehkordi M, Loáiciga HA, Mariño MA (2016b) A self-tuning ANN model for simulation and forecasting of surface flows. Water Resour Manag 30:2907–2929. https://doi.org/10.1007/s11269-016-1301-2
Budu K (2014) Comparison of wavelet-based ANN and regression models for reservoir inflow forecasting. J Hydrol Eng 19:1385–1400. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000892
Carson Y, Maria A (1997) Simulation optimization. In: Proceedings of the 29th conference on Winter simulation - WSC ‘97. ACM Press, New York, New York, USA, pp 118–126
Chang L-C, Chang F-J (2001) Intelligent control for modelling of real-time reservoir operation. Hydrol Process 15:1621–1634. https://doi.org/10.1002/hyp.226
Chang F-J, Lai J-S, Kao L-S (2003) Optimization of operation rule curves and flushing schedule in a reservoir. Hydrol Process 17:1623–1640. https://doi.org/10.1002/hyp.1204
Chang L-C, Chang F-J, Wang K-W, Dai S-Y (2010) Constrained genetic algorithms for optimizing multi-use reservoir operation. J Hydrol 390:66–74. https://doi.org/10.1016/j.jhydrol.2010.06.031
Chen L, McPhee J, Yeh WW-G (2007) A diversified multiobjective GA for optimizing reservoir rule curves. Adv Water Resour 30:1082–1093. https://doi.org/10.1016/j.advwatres.2006.10.001
Chen S, Shao D, Li X, Lei C (2016) 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 30:2887–2905. https://doi.org/10.1007/s11269-016-1277-y
Cheng C-T, Feng Z-K, Niu W-J, Liao S-L (2015) Heuristic methods for reservoir monthly inflow forecasting: a case study of Xinfengjiang reservoir in Pearl River, China. Water 7:4477–4495. https://doi.org/10.3390/w7084477
Chiamsathit C, Adeloye AJ, Bankaru-Swamy S (2016) Inflow forecasting using artificial neural networks for reservoir operation. Proc Int Assoc Hydrol Sci 373:209–214. https://doi.org/10.5194/piahs-373-209-2016
Collobert R, Bengio S (2001) SVMTorch: support vector machines for large-scale regression problems. J Mach Learn Res 1:143–160
Coulibaly P, Anctil F, Bobee B (1999) Hydrological forecasting with artificial neural networks: the state of the art. Engineering 26:293–304. https://doi.org/10.1139/l98-069
Coulibaly P, Anctil F, Bobée B (2000) Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. J Hydrol 230:244–257. https://doi.org/10.1016/S0022-1694(00)00214-6
Coulibaly P, Anctil F, Aravena R, Bobée B (2001) Artificial neural network modeling of water table depth fluctuations. Water Resour Res 37:885–896. https://doi.org/10.1029/2000WR900368
Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines: and other kernel-based learning methods. Cambridge University Press
Dawson CW, Wilby RL (2001) Hydrological modelling using artificial neural networks. Prog Phys Geogr 25:80–108. https://doi.org/10.1177/030913330102500104
Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B 26:29–41. https://doi.org/10.1109/3477.484436
Drucker H, Burges CJC, Kaufman L et al (1996) Support vector regression machines. In: Proc. 9th Int. Conf. Neural Inf. Process. Syst. MIT Press, Cambridge, pp 155–161
Ehteram M, Allawi MF, Karami H, Mousavi SF, Emami M, el-Shafie A, Farzin S (2017a) Optimization of chain-reservoirs’ operation with a new approach in artificial intelligence. Water Resour Manag 31:2085–2104. https://doi.org/10.1007/s11269-017-1625-6
Ehteram M, Karami H, Mousavi S-F, el-Shafie A, Amini Z (2017b) Optimizing dam and reservoirs operation based model utilizing shark algorithm approach. Knowledge-Based Syst 122:26–38. https://doi.org/10.1016/j.knosys.2017.01.026
Elizaga NB, Maravillas EA, Gerardo BD (2014) Regression-based inflow forecasting model using exponential smoothing time series and backpropagation methods for Angat dam. In: 2014 international conference on humanoid, nanotechnology, information technology, communication and control, environment and management (HNICEM). IEEE, pp 1–6
El-Shafie A, Noureldin A (2011) Generalized versus non-generalized neural network model for multi-lead inflow forecasting at Aswan high dam. Hydrol Earth Syst Sci 15:841–858. https://doi.org/10.5194/hess-15-841-2011
El-Shafie A, Taha MR, Noureldin A (2007) A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam. Water Resour Manag 21:533–556. https://doi.org/10.1007/s11269-006-9027-1
El-Shafie A, Abdin AE, Noureldin A, Taha MR (2009) Enhancing inflow forecasting model at Aswan high dam utilizing radial basis neural network and upstream monitoring stations measurements. Water Resour Manag 23:2289–2315. https://doi.org/10.1007/s11269-008-9382-1
Fayaed SS, El-Shafie A, Jaafar O (2013) Reservoir-system simulation and optimization techniques. Stoch Environ Res Risk Assess 27:1751–1772. https://doi.org/10.1007/s00477-013-0711-4
Fernando DAK, Jayawardena AW (1998) Runoff forecasting using RBF networks with OLS algorithm. J Hydrol Eng 3:203–209. https://doi.org/10.1061/(ASCE)1084-0699(1998)3:3(203)
Fogel, Lawrence J and Owens, Alvin J and Walsh M. (1966) Artificial Intelligence through simulated evolution—Lawrence Jerome Fogel, Alvin J. Owens, Michael John Walsh - Google Books
Fugal DL (2009) Conceptual wavelets in digital signal processing: an in-depth, practical approach for the non-mathematician. Space & Signals Technical Pub
Garousi-Nejad I, Bozorg-Haddad O, Loáiciga HA (2016a) Modified firefly algorithm for solving multireservoir operation in continuous and discrete domains. J Water Resour Plan Manag 142:4016029. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000644
Garousi-Nejad I, Bozorg-Haddad O, Loáiciga HA, Mariño MA (2016b) Application of the firefly algorithm to optimal operation of reservoirs with the purpose of irrigation supply and hydropower production. J Irrig Drain Eng 142:4016041. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001064
Grossmann A, Morlet J (1984) Decomposition of hardy functions into square integrable wavelets of constant shape. SIAM J Math Anal 15:723–736. https://doi.org/10.1137/0515056
Haykin S (1999) Multilayer perceptrons. Neural networks a Compr Found
Hidalgo IG, Barbosa PSF, Francato AL, Luna I, Correia PB, Pedro PSM (2015) Management of inflow forecasting studies. Water Pract Technol 10:402. https://doi.org/10.2166/wpt.2015.050
Higgins JM, Brock WG (1999) Overview of reservoir release improvements at 20 TVA dams. J Energy Eng 125:1–17. https://doi.org/10.1061/(ASCE)0733-9402(1999)125:1(1)
Hınçal O, Altan-Sakarya AB, Metin Ger A (2011) Optimization of multireservoir systems by genetic algorithm. Water Resour Manag 25:1465–1487. https://doi.org/10.1007/s11269-010-9755-0
Holland JH, John H (1975) Adaptation in natural and artificial systems : an introductory analysis with applications to biology, control, and artificial intelligence. University of Michigan Press
Hossain MS, El-shafie A (2014a) Performance analysis of artificial bee colony (ABC) algorithm in optimizing release policy of Aswan high dam. Neural Comput Appl 24:1199–1206. https://doi.org/10.1007/s00521-012-1309-3
Hossain MS, El-Shafie A (2014b) Evolutionary techniques versus swarm intelligences: application in reservoir release optimization. Neural Comput Appl 24:1583–1594. https://doi.org/10.1007/s00521-013-1389-8
Hossain MS, El-Shafie A, Wan Mohtar WHM (2015) Application of intelligent optimization techniques and investigating the effect of reservoir size in calibrating the reservoir operating policy. Water Policy 17:wp2015023. https://doi.org/10.2166/wp.2015.023
Hosseini-Moghari S-M, Morovati R, Moghadas M, Araghinejad S (2015) Optimum operation of reservoir using two evolutionary algorithms: imperialist competitive algorithm (ICA) and cuckoo optimization algorithm (COA). Water Resour Manag 29:3749–3769. https://doi.org/10.1007/s11269-015-1027-6
Izadbakhsh MA, Javadikia H (2014) Application of hybrid FFNN-genetic algorithm for predicting evaporation in storage dam reservoirs. Agric Commun 2:57–62
Jothiprakash V, Kote AS (2011) Effect of pruning and smoothing while using M5 model tree technique for reservoir inflow prediction. J Hydrol Eng 16:563–574. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000342
Jothiprakash V, Magar RB (2012) Multi-time-step ahead daily and hourly intermittent reservoir inflow prediction by artificial intelligent techniques using lumped and distributed data. J Hydrol 450:293–307. https://doi.org/10.1016/j.jhydrol.2012.04.045
Karaboga D (2005) An idea based on honey bee swarm for numerical optimizatioN (Technical report-TR06, October, 2005). Univ Press Erciyes
Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214:108–132. https://doi.org/10.1016/j.amc.2009.03.090
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95 - International Conference on Neural Networks. IEEE, pp 1942–1948
Kerachian R, Karamouz M (2007) A stochastic conflict resolution model for water quality management in reservoir–river systems. Adv Water Resour 30:866–882. https://doi.org/10.1016/j.advwatres.2006.07.005
Keskin ME, Terzi Ö (2006) Artificial neural network models of daily Pan evaporation. J Hydrol Eng 11:65–70. https://doi.org/10.1061/(ASCE)1084-0699(2006)11:1(65)
Keskin ME, Terzi Ö, Taylan D (2004) Fuzzy logic model approaches to daily pan evaporation estimation in western Turkey / Estimation de l’évaporation journalière du bac dans l’Ouest de la Turquie par des modèles à base de logique floue. Hydrol Sci J 49. https://doi.org/10.1623/hysj.49.6.1001.55718
Khan NM, Babel MS, Tingsanchali T, Clemente RS, Luong HT (2012) Reservoir optimization-simulation with a sediment evacuation model to minimize irrigation deficits. Water Resour Manag 26:3173–3193. https://doi.org/10.1007/s11269-012-0066-5
Klir G, Yuan B (1995) Fuzzy sets and fuzzy logic
Koza JR (1992) Genetic programming : on the programming of computers by means of natural selection. MIT Press
Kreinovich V, Mukaidono M (2000) Intervals (pairs of fuzzy values), triples, etc.: can we thus get an arbitrary ordering? In: Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063). IEEE, pp 234–238
Kumar S, Tiwari MK, Chatterjee C, Mishra A (2015) Reservoir inflow forecasting using ensemble models Based on neural networks, wavelet analysis and bootstrap method. Water Resour Manag 29:4863–4883. https://doi.org/10.1007/s11269-015-1095-7
Labat D (2005) Recent advances in wavelet analyses: part 1. A review of concepts. J Hydrol 314:275–288. https://doi.org/10.1016/j.jhydrol.2005.04.003
Li W, Sankarasubramanian A (2012) Reducing hydrologic model uncertainty in monthly streamflow predictions using multimodel combination. Water Resour Res 48:n/a-n/a. https://doi.org/10.1029/2011WR011380
Li X-G, Wei X (2008) An improved genetic algorithm-simulated annealing hybrid algorithm for the optimization of multiple reservoirs. Water Resour Manag 22:1031–1049. https://doi.org/10.1007/s11269-007-9209-5
Li P-H, Kwon H-H, Sun L, Lall U, Kao JJ (2009) A modified support vector machine based prediction model on streamflow at the Shihmen reservoir, Taiwan. Int J Climatol 30:1256–1268. https://doi.org/10.1002/joc.1954
Li F-F, Wei J-H, Fu X-D, Wan X-Y (2012) An effective approach to long-term optimal operation of large-scale reservoir systems: case study of the three gorges system. Water Resour Manag 26:4073–4090. https://doi.org/10.1007/s11269-012-0131-0
Li C, Bai Y, Zeng B (2016) Deep feature learning architectures for daily reservoir inflow forecasting. Water Resour Manag 30:5145–5161. https://doi.org/10.1007/s11269-016-1474-8
Liao X, Zhou J, Ouyang S et al (2014) Multi-objective artificial bee colony algorithm for long-term scheduling of hydropower system: a case study of China. Water Util J 7:13–23
LIN J-Y, CHENG C-T, CHAU K-W (2006) Using support vector machines for long-term discharge prediction. Hydrol Sci J 51:599–612. https://doi.org/10.1623/hysj.51.4.599
Lin G-F, Chen G-R, Huang P-Y, Chou Y-C (2009) Support vector machine-based models for hourly reservoir inflow forecasting during typhoon-warning periods. J Hydrol 372:17–29. https://doi.org/10.1016/j.jhydrol.2009.03.032
Lohani AK, Kumar R, Singh RD (2012) Hydrological time series modeling: a comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques. J Hydrol 442:23–35. https://doi.org/10.1016/j.jhydrol.2012.03.031
Luger GF (2005) Artificial intelligence: structures and strategies for complex problem solving. Addison-Wesley, London
Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ Model Softw 15:101–124. https://doi.org/10.1016/S1364-8152(99)00007-9
Mayer A, Muñoz-Hernandez A (2009) Integrated water resources optimization models: an assessment of a multidisciplinary tool for sustainable water resources management strategies. Geogr Compass 33:1176–1195. https://doi.org/10.1111/j.1749-8198.2009.00239.x
Mays LW (1989) Hydrosystems engineering simulation vs. optimization: why not both? IAHS 225–231
Ming B, Chang J, Huang Q, Wang YM, Huang SZ (2015) Optimal operation of multi-reservoir system Based-on cuckoo search algorithm. Water Resour Manag 29:5671–5687. https://doi.org/10.1007/s11269-015-1140-6
Moeeni H, Bonakdari H (2016) Forecasting monthly inflow with extreme seasonal variation using the hybrid SARIMA-ANN model. Stoch Environ Res Risk Assess 31:1997–2010. https://doi.org/10.1007/s00477-016-1273-z
Moghaddamnia A, Ghafari M, Piri J, Han D (2009a) Evaporation estimation using support vector machines technique. Int. J Eng Appl Sci 5:415–423
Moghaddamnia A, Ghafari Gousheh M, Piri J, Amin S, Han D (2009b) Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Adv Water Resour 32:88–97. https://doi.org/10.1016/j.advwatres.2008.10.005
Momtahen S, Dariane AB (2007) Direct search approaches using genetic algorithms for optimization of water reservoir operating policies. J Water Resour Plan Manag 133:202–209. https://doi.org/10.1061/(ASCE)0733-9496(2007)133:3(202)
Mousavi SJ, Shourian M (2010) Capacity optimization of hydropower storage projects using particle swarm optimization algorithm. J Hydroinf 12:275–291. https://doi.org/10.2166/hydro.2009.039
Muluye GY, Coulibaly P (2007) Seasonal reservoir inflow forecasting with low-frequency climatic indices: a comparison of data-driven methods. Hydrol Sci J 52:508–522. https://doi.org/10.1623/hysj.52.3.508
Nagesh Kumar D, Janga Reddy M (2007) Multipurpose reservoir operation using particle swarm optimization. J Water Resour Plan Manag 133:192–201. https://doi.org/10.1061/(ASCE)0733-9496(2007)133:3(192)
Noori R, Karbassi AR, Moghaddamnia A, Han D, Zokaei-Ashtiani MH, Farokhnia A, Gousheh MG (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 401:177–189. https://doi.org/10.1016/j.jhydrol.2011.02.021
Nourani V, Sayyah Fard M (2012) Sensitivity analysis of the artificial neural network outputs in simulation of the evaporation process at different climatologic regimes. Adv Eng Softw 47:127–146. https://doi.org/10.1016/J.ADVENGSOFT.2011.12.014
Nourani V, Hosseini Baghanam A, Adamowski J, Kisi O (2014) Applications of hybrid wavelet–artificial intelligence models in hydrology: a review. J Hydrol 514:358–377. https://doi.org/10.1016/j.jhydrol.2014.03.057
Reddy MJ, Nagesh Kumar D (2007) Multi-objective particle swarm optimization for generating optimal trade-offs in reservoir operation. Hydrol Process 21:2897–2909. https://doi.org/10.1002/hyp.6507
SaberChenari K, Abghari H, Tabari H (2016) Application of PSO algorithm in short-term optimization of reservoir operation. Environ Monit Assess 188:667. https://doi.org/10.1007/s10661-016-5689-1
Salas J (1980) Applied modeling of hydrologic time series
Sang Y-F (2013) A review on the applications of wavelet transform in hydrology time series analysis. Atmos Res 122:8–15. https://doi.org/10.1016/j.atmosres.2012.11.003
Shi-Mei Choong PAE-S and DWMWHM (2016) An application of artificial bee colony algorithm for reservoir optimization: a case study of Chenderoh dam, Malaysia. 3:227–231. https://doi.org/10.15242/IJAAEE.U0516306
Tabari H, Marofi S, Sabziparvar A-A (2010) Estimation of daily pan evaporation using artificial neural network and multivariate non-linear regression. Irrig Sci 28:399–406. https://doi.org/10.1007/s00271-009-0201-0
Tan SBK, Shuy EB, Chua LHC (2007) Modelling hourly and daily open-water evaporation rates in areas with an equatorial climate. Hydrol Process 21:486–499. https://doi.org/10.1002/hyp.6251
Task A, Neural A (2000) Artificial neural networks in hydrology. By ASCE Task Comm Appl Artif Neural Networks Hydrol 5:124–137. https://doi.org/10.1061/(ASCE)1084-0699(2000)5:2(124)
Terzi Ö (2013) Daily pan evaporation estimation using gene expression programming and adaptive neural-based fuzzy inference system. Neural Comput Appl 23:1035–1044. https://doi.org/10.1007/s00521-012-1027-x
Tezel G, Buyukyildiz M (2016) Monthly evaporation forecasting using artificial neural networks and support vector machines. Theor Appl Climatol 124:69–80. https://doi.org/10.1007/s00704-015-1392-3
Valipour M (2015) Long-term runoff study using SARIMA and ARIMA models in the United States. Meteorol Appl 22:592–598. https://doi.org/10.1002/met.1491
Valipour M, Banihabib ME, Behbahani SMR (2012) Monthly inflow forecasting using autoregressive artificial neural network. J Appl Sci 12:2139–2147. https://doi.org/10.3923/jas.2012.2139.2147
Valipour M, Banihabib ME, Behbahani SMR (2013) Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir. J Hydrol 476:433–441. https://doi.org/10.1016/j.jhydrol.2012.11.017
Vapnik VN, (1995) The nature of statistical learning theory. Springer
Wang W, Jin J, Li Y (2009) Prediction of inflow at three gorges dam in Yangtze River with wavelet network model. Water Resour Manag 23:2791–2803. https://doi.org/10.1007/s11269-009-9409-2
Wang W, Nie X, Qiu L (2010) Support vector machine with particle swarm optimization for reservoir annual inflow forecasting. In: 2010 International Conference on Artificial Intelligence and Computational Intelligence IEEE, pp 184–188
Wang K-W, Chang L-C, Chang F-J (2011) Multi-tier interactive genetic algorithms for the optimization of long-term reservoir operation. Adv Water Resour 34:1343–1351. https://doi.org/10.1016/j.advwatres.2011.07.004
Wehrens R, Buydens LMC, Wehrens R, Buydens LMC (2000) Classical and nonclassical optimization methods. In: Encyclopedia of Analytical Chemistry. John Wiley & Sons, Ltd, Chichester, UK
Wu CL, Chau KW, Li YS (2009) Predicting monthly streamflow using data-driven models coupled with data-preprocessing techniques. Water Resour Res 45:1–23. https://doi.org/10.1029/2007WR006737
Wurbs RA (2005) Comparative Evaluation of Generalized River/Reservoir System Models
Yazdi J, Salehi Neyshabouri SAA (2012) Optimal design of flood-control multi-reservoir system on a watershed scale. Nat Hazards 63:629–646. https://doi.org/10.1007/s11069-012-0169-6
Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
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This work was supported by a research grant coded TRGS/1/2015/UKM/02/5/1 Universiti Kebangsaan Malaysia. The authors would like to thank so much the Ministry of Higher Education, FRGS/1/2016/STG06/UKM/02/1 and the University of Malaya Research Grant (UMRG) coded RP025A-18SUS.
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Allawi, M.F., Jaafar, O., Mohamad Hamzah, F. et al. Review on applications of artificial intelligence methods for dam and reservoir-hydro-environment models. Environ Sci Pollut Res 25, 13446–13469 (2018). https://doi.org/10.1007/s11356-018-1867-8
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DOI: https://doi.org/10.1007/s11356-018-1867-8