Review on applications of artificial intelligence methods for dam and reservoir-hydro-environment models

  • Mohammed Falah Allawi
  • Othman Jaafar
  • Firdaus Mohamad Hamzah
  • Sharifah Mastura Syed Abdullah
  • Ahmed El-shafie
Review Article
  • 49 Downloads

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.

Keywords

Hydrological parameters Predictive models Optimization models Environmental 

Notes

Acknowledgements

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.

Compliance with ethical standards

Conflicts of interest

The authors declare that they have no conflict of interest.

References

  1. 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 CrossRefGoogle Scholar
  2. Affenzeller M (2009) Genetic algorithms and genetic programming: modern concepts and practical applications. CRC PressGoogle Scholar
  3. 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 CrossRefGoogle Scholar
  4. 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 CrossRefGoogle Scholar
  5. 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 CrossRefGoogle Scholar
  6. 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 CrossRefGoogle Scholar
  7. 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 CrossRefGoogle Scholar
  8. 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 CrossRefGoogle Scholar
  9. 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 CrossRefGoogle Scholar
  10. 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 247–251Google Scholar
  11. 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 CrossRefGoogle Scholar
  12. 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 CrossRefGoogle Scholar
  13. 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 CrossRefGoogle Scholar
  14. 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 CrossRefGoogle Scholar
  15. 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 CrossRefGoogle Scholar
  16. 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) CrossRefGoogle Scholar
  17. 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 CrossRefGoogle Scholar
  18. 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 CrossRefGoogle Scholar
  19. 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 CrossRefGoogle Scholar
  20. 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 CrossRefGoogle Scholar
  21. 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 CrossRefGoogle Scholar
  22. 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 118–126Google Scholar
  23. 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 CrossRefGoogle Scholar
  24. 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 CrossRefGoogle Scholar
  25. 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 CrossRefGoogle Scholar
  26. 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 CrossRefGoogle Scholar
  27. 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 CrossRefGoogle Scholar
  28. 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 CrossRefGoogle Scholar
  29. 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 Google Scholar
  30. Collobert R, Bengio S (2001) SVMTorch: support vector machines for large-scale regression problems. J Mach Learn Res 1:143–160Google Scholar
  31. 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 Google Scholar
  32. 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 CrossRefGoogle Scholar
  33. 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 CrossRefGoogle Scholar
  34. Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines: and other kernel-based learning methods. Cambridge University PressGoogle Scholar
  35. Dawson CW, Wilby RL (2001) Hydrological modelling using artificial neural networks. Prog Phys Geogr 25:80–108.  https://doi.org/10.1177/030913330102500104 CrossRefGoogle Scholar
  36. 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 CrossRefGoogle Scholar
  37. 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 155–161Google Scholar
  38. 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 CrossRefGoogle Scholar
  39. 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 CrossRefGoogle Scholar
  40. 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 1–6Google Scholar
  41. 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 CrossRefGoogle Scholar
  42. 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 CrossRefGoogle Scholar
  43. 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 CrossRefGoogle Scholar
  44. 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 CrossRefGoogle Scholar
  45. 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) CrossRefGoogle Scholar
  46. 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 BooksGoogle Scholar
  47. Fugal DL (2009) Conceptual wavelets in digital signal processing: an in-depth, practical approach for the non-mathematician. Space & Signals Technical PubGoogle Scholar
  48. 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 CrossRefGoogle Scholar
  49. 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 CrossRefGoogle Scholar
  50. 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 CrossRefGoogle Scholar
  51. Haykin S (1999) Multilayer perceptrons. Neural networks a Compr FoundGoogle Scholar
  52. 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 CrossRefGoogle Scholar
  53. 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) CrossRefGoogle Scholar
  54. 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 CrossRefGoogle Scholar
  55. 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 PressGoogle Scholar
  56. 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 CrossRefGoogle Scholar
  57. 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 CrossRefGoogle Scholar
  58. 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 CrossRefGoogle Scholar
  59. 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 CrossRefGoogle Scholar
  60. Izadbakhsh MA, Javadikia H (2014) Application of hybrid FFNN-genetic algorithm for predicting evaporation in storage dam reservoirs. Agric Commun 2:57–62Google Scholar
  61. 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 CrossRefGoogle Scholar
  62. 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 CrossRefGoogle Scholar
  63. Karaboga D (2005) An idea based on honey bee swarm for numerical optimizatioN (Technical report-TR06, October, 2005). Univ Press ErciyesGoogle Scholar
  64. 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 Google Scholar
  65. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95 - International Conference on Neural Networks. IEEE, pp 1942–1948Google Scholar
  66. 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 CrossRefGoogle Scholar
  67. 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) CrossRefGoogle Scholar
  68. 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
  69. 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 CrossRefGoogle Scholar
  70. Klir G, Yuan B (1995) Fuzzy sets and fuzzy logicGoogle Scholar
  71. Koza JR (1992) Genetic programming : on the programming of computers by means of natural selection. MIT PressGoogle Scholar
  72. 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 234–238Google Scholar
  73. 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 CrossRefGoogle Scholar
  74. 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 CrossRefGoogle Scholar
  75. 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
  76. 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 CrossRefGoogle Scholar
  77. 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 CrossRefGoogle Scholar
  78. 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 CrossRefGoogle Scholar
  79. 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 CrossRefGoogle Scholar
  80. 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 7:13–23Google Scholar
  81. 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 CrossRefGoogle Scholar
  82. 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 CrossRefGoogle Scholar
  83. 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 CrossRefGoogle Scholar
  84. Luger GF (2005) Artificial intelligence: structures and strategies for complex problem solving. Addison-Wesley, LondonGoogle Scholar
  85. 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 CrossRefGoogle Scholar
  86. 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 CrossRefGoogle Scholar
  87. Mays LW (1989) Hydrosystems engineering simulation vs. optimization: why not both? IAHS 225–231Google Scholar
  88. 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 CrossRefGoogle Scholar
  89. 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 CrossRefGoogle Scholar
  90. Moghaddamnia A, Ghafari M, Piri J, Han D (2009a) Evaporation estimation using support vector machines technique. Int. J Eng Appl Sci 5:415–423Google Scholar
  91. 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 CrossRefGoogle Scholar
  92. 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) CrossRefGoogle Scholar
  93. 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 CrossRefGoogle Scholar
  94. 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 CrossRefGoogle Scholar
  95. 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) CrossRefGoogle Scholar
  96. 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 CrossRefGoogle Scholar
  97. 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 CrossRefGoogle Scholar
  98. 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 CrossRefGoogle Scholar
  99. 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 CrossRefGoogle Scholar
  100. 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 CrossRefGoogle Scholar
  101. Salas J (1980) Applied modeling of hydrologic time seriesGoogle Scholar
  102. 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 CrossRefGoogle Scholar
  103. 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
  104. 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 CrossRefGoogle Scholar
  105. 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 CrossRefGoogle Scholar
  106. 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) Google Scholar
  107. 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 CrossRefGoogle Scholar
  108. 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 CrossRefGoogle Scholar
  109. 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 CrossRefGoogle Scholar
  110. 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 CrossRefGoogle Scholar
  111. 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 CrossRefGoogle Scholar
  112. Vapnik VN, (1995) The nature of statistical learning theory. SpringerGoogle Scholar
  113. 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 CrossRefGoogle Scholar
  114. 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 184–188Google Scholar
  115. 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 CrossRefGoogle Scholar
  116. Wehrens R, Buydens LMC, Wehrens R, Buydens LMC (2000) Classical and nonclassical optimization methods. In: Encyclopedia of Analytical Chemistry. John Wiley & Sons, Ltd, Chichester, UKGoogle Scholar
  117. 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 CrossRefGoogle Scholar
  118. Wurbs RA (2005) Comparative Evaluation of Generalized River/Reservoir System ModelsGoogle Scholar
  119. 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 CrossRefGoogle Scholar
  120. Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353.  https://doi.org/10.1016/S0019-9958(65)90241-X CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Mohammed Falah Allawi
    • 1
  • Othman Jaafar
    • 1
  • Firdaus Mohamad Hamzah
    • 1
  • Sharifah Mastura Syed Abdullah
    • 2
  • Ahmed El-shafie
    • 3
  1. 1.Civil and Structural Engineering Department, Faculty of Engineering and Built EnvironmentUniversiti Kebangsaan MalaysiaBangiMalaysia
  2. 2.Institute of Climate Change (IPI)Universiti Kebangsaan Malaysia (UKM)BangiMalaysia
  3. 3.Department of Civil Engineering, Faculty of EngineeringUniversity of MalayaKuala LumpurMalaysia

Personalised recommendations