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Donyaii A, Sarraf A. Optimization of Reservoir Operation using a Bioinspired Metaheuristic Based on the COVID-19 Propagation Model. NMCE 2020; 5 (1) :15-28
URL: http://nmce.kntu.ac.ir/article-1-289-en.html
1- Ph.D. in Civil Engineering, Water Resources Expert, Golestan Regional Water Company, Gorgan, Iran.
2- Assistant Professor of Civil Engineering Department, Roudehen Branch, Islamic Azad University, Roudehen, Iran. , sarraf@riau.ac.ir
Abstract:   (955 Views)
Recently, global warming problems with rapid population growth and socio-economic development have intensified the demand for water and increased tensions on water supplies. This research evolves the Multi-Objective Coronavirus Optimization Algorithm (MOCVOA) to obtain operational optimum rules of Voshmgir Dam reservoir under the climate change conditions. The climatic variables downscaled and predicted by the Bias Correction Spatial Disaggregation (BCSD) method of MIROC-ESM model, was introduced into the Extreme Learning Machine (ELM) modelto evaluate the future runoff flowing into the reservoir. The model objective functions included minimizing vulnerability and enhancing reliability indices during baseline and climate change periods. Results revealed that under climate change conditions, the river flow would decrease by 0.17%, increase the temperature up to 2°C and decrease the rainfall by 23.8%, corresponding to the baseline period. Moreover, the extent of vulnerability index variations in the baseline and climate change conditions were also determined as 20-38% and 13-40%, respectively. The reliability index changes under the baseline and climate change conditions obtained were, 57-85% and 40-91%. Therefore, the vulnerability index was also measured at 33% and 30% for baseline and climate change conditions, respectively, with 80% of reliability index. Finally, the comparison of reservoir performance in meeting the water needs of downstream lands at the Pareto point of 80% reliability under both conditions indicated that the reservoir release rate would be more in line with the demand in the climate change conditions.
Full-Text [PDF 837 kb]   (466 Downloads)    
Type of Study: Research | Subject: Special
Received: 2020/07/1 | Revised: 2020/08/1 | Accepted: 2020/08/28

References
1. [1] Huntington, T.G., 2006, "Evidence for intensification of the global water cycle: review and synthesis", J. Hydrol. 319: 83-95. [DOI:10.1016/j.jhydrol.2005.07.003]
2. [2] Loaiciga, H.A., Valdes, J.B., Vogel, R., Garvey, J., Schwarz, H., 1996, "Global warming and the hydrologic cycle", J. Hydrol. 174: 83-127. [DOI:10.1016/0022-1694(95)02753-X]
3. [3] Muzik, I., 2001, "Sensitivity of hydrologic systems to climate change", Can. Water Resour. J. 26: 233-252. [DOI:10.4296/cwrj2602233]
4. [4] Boyer, C., Chaumont, D., Chartier, I., Roy, A.G., 2010, "Impact of climate change on the hydrology of St. Lawrence tributaries", J. Hydrol. 384: 65-83. [DOI:10.1016/j.jhydrol.2010.01.011]
5. [5] Bronstert, A., Kolokotronis, V., Schwandt, D., Straub, H., 2007, "Comparison and evaluation of regional climate scenarios for hydrological impact analysis: general scheme and application example", Int. J. Climatol. 27: 1579-1594. [DOI:10.1002/joc.1621]
6. [6] Jiang, T., Chen, Y.D., Xu, C.Y., Chen, X., Chen, X., Singh, V.P., 2007, "Comparison of hydrological impacts of climate change simulated by six hydrological models in the Dongjiang Basin, South China", J. Hydrol. 336: 316-333. [DOI:10.1016/j.jhydrol.2007.01.010]
7. [7] Georgakakos, A.P., Yao, H., Kistenmacher, M., Georgakakos, K.P., Graham, N.E., Cheng, F.Y., Spencer, C., Shamir, E., 2012, "Value of adaptive water resources management in Northern California under climatic variability and change", Reservoir management, J. Hydrol. 412: 34-46. [DOI:10.1016/j.jhydrol.2011.04.038]
8. [8] Hariri-Ardebili, M. A., Seyed-Kolbadi, S. M., V.E., Saouma, J. W. Salamon and Nuss, L. K. 2019, "Anatomy of the vibration characteristics in old arch dams by random field theory", Engineering Structures, 179(15): 460-475. doi.org/10.1016/j.engstruct.2018.10.082 [DOI:10.1016/j.engstruct.2018.10.082]
9. [9] Yang, X.L., Gao, W.S., Shi, Q.H., Chen, F., Chu, Q.Q., 2013, "Impact of climate change on the water requirement of summer maize in the Huang-Huai-Hai farming region", Agric. Water Manag. 124: 20-27. [DOI:10.1016/j.agwat.2013.03.017]
10. [10] Nam, W.H., Choi, J.Y., 2014, "Development of an irrigation vulnerability assessment model in agricultural reservoirs utilizing probability theory and reliability analysis", Agric. Water Manag. 142: 115-126. [DOI:10.1016/j.agwat.2014.05.009]
11. [11] Mirzabozorg, H., Hariri-Ardebili, M.A.,Heshmati, M. and Seyed-Kolbadi, S.M., 2014, "Structural safety evaluation of Karun III Dam and calibration of its finite element model using instrumentation and site observation, Case Studies in Structural Engineering", 1: 6-12. doi.org/10.1016/j.csse.2014.02.001 [DOI:10.1016/j.csse.2014.02.001]
12. [12] Loucks, D. P., and Van Beek, E., 2005, "Water resources systems planning and management: An introduction to methods, models and applications", UNESCO Publishing, Paris. MATLAB 9.0 [Computer software]. Natick, MA, MathWorks.
13. [13] Li, X., Guo, S.L., Liu, P., Chen, G.Y., 2010, "Dynamic control of flood limited water level for reservoir operation by considering inflow uncertainty", J. Hydrol. 391; 126- 134. [DOI:10.1016/j.jhydrol.2010.07.011]
14. [14] Liu, P., Guo, S.L., Xu, X.W., Chen, J.H., 2011b, "Derivation of aggregation-based joint operating rule curves for cascade hydropower reservoirs", Water Resour. Manage. 25: 3177-3200. [DOI:10.1007/s11269-011-9851-9]
15. [15] Liu, P., Li, L.P., Guo, S.L., Xiong, L.H., Zhang, W., Zhang, J.W., Xu, C.Y., 2015, "Optimal design of seasonal flood limited water levels and its application for the Three Gorges Reservoir". J. Hydrol. 527: 1045-1053. [DOI:10.1016/j.jhydrol.2015.05.055]
16. [16] Vedula, S., Kumar, D.N., 1996, "An integrated model for optimal reservoir operation for irrigation of multiple crops", Water Resour. Res. 32: 1101-1108. [DOI:10.1029/95WR03110]
17. [17] Mujumdar, P.P., Ramesh, T.S.V., 1997, "Real-time reservoir operation for irrigation', Water Resour. Res. 33: 1157-1164. [DOI:10.1029/96WR03907]
18. [18] Umamahesh, N.V., Sreenivasulu, P., 1997, "Two-phase stochastic dynamic programming model for optimal operation of irrigation reservoir", Water Resour. Manage 11: 395-406. [DOI:10.1023/A:1007914019102]
19. [19] Hajilal, M.S., Rao, N.H., Sarma, P.B.S., 1998, "Real time operation of reservoir based canal irrigation systems". Agric. Water Manag. 38: 103-122. [DOI:10.1016/S0378-3774(98)00061-4]
20. [20] Haddad, O.B., Moradi, M.J., Mirmomeni, M., Kholghi, M.K., Marino, M.A., 2009, 'Optimal cultivation rules in multi-crop irrigation areas', Irrig. Drain. 58: 38-49. [DOI:10.1002/ird.381]
21. [21] Consoli, S., Matarazzo, B., Pappalardo, N., 2008, "Operating rules of an irrigation purposes reservoir using multi-objective optimization", Water Resour. Manage 22: 551-564. [DOI:10.1007/s11269-007-9177-9]
22. [22] Teixeira, A.D.S., Marino, M.A., 2002, "Coupled reservoir operation-irrigation scheduling by dynamic programming", J. Irrig. Drain. Eng. 128: 63-73. [DOI:10.1061/(ASCE)0733-9437(2002)128:2(63)]
23. [23] Prasad, A.S., Umamahesh, N.V., Viswanath, G.K., 2013, "Short-term real-time reservoir operation for irrigation", J. Water Resour. Plann. Manage. 139: 149-158. [DOI:10.1061/(ASCE)WR.1943-5452.0000234]
24. [24] Reddy, M.J., Kumar, D.N., 2007, "Optimal reservoir operation for irrigation of multiple crops using elitist-mutated particle swarm optimization", Hydrol. Sci. J. 52; 686-701. [DOI:10.1623/hysj.52.4.686]
25. [25] Shnaydman, V.M., 1993, "The influence of climate variations on an irrigation water resources system performance strategy", Water Resour. Manage 7: 39-56. [DOI:10.1007/BF00872241]
26. [26] Georgiou, P.E., Papamichail, D.M., 2008, "Optimization model of an irrigation reservoir for water allocation and crop planning under various weather conditions", Irrig. Sci. 26: 487-504. [DOI:10.1007/s00271-008-0110-7]
27. [27] Ncube, S.P., Makurira, H., Kaseke, E., Mhizha, A., 2011, "Reservoir operation under variable climate: case of Rozva Dam, Zimbabwe". Phys. Chem. Earth 36; 1112- 1119. [DOI:10.1016/j.pce.2011.07.059]
28. [28] Afkhamifar, S. and Sarraf, A. P., 2020, "Prediction of groundwater level in Urmia Plain aquifer using hybrid model of wavelet Transform-Extreme Learning Machine based on quantum particle swarm optimization", Watershed Engineering and Management, 12(2): 351-364. doi: 10.22092/ijwmse.2019.126515.1669, [in Persian].
29. [29] Donyaii, A. R., Sarraf, A. P. and Ahmadi, H., 2020a, "Water Reservoir Multi-Objective Optimal Operation Using Grey Wolf Optimizer", Shock and vibration journal., 1-10 [DOI:10.1155/2020/8870464]
30. [30] Donyaii, A. R., Sarraf, A. P. and Ahmadi, H., 2020b, "Application of a New Approach in Optimizing the Operation of the Multi-Objective Reservoir", J. Hydraul. Struct., 6(3):1-20 DOI: 10.22055/jhs.2020.34556.1145. [DOI:10.1155/2020/8870464]
31. [31] Ahmed KF., Wang G., Silander J., Wilson AM., Allen JM., Horton R. and Anyah R. 2013, "Statistical downscaling and bias correction of climate model outputs for climate change impact assessment in the US northeast", Journal of Global and Planetary Change", 100: 320-332. [DOI:10.1016/j.gloplacha.2012.11.003]
32. [32] Huang GB., Liang NY. and Rong HJ., 2005, "On-line sequential extreme learning machine" In: The IASTED international conference on computational intelligence. Calgary
33. [33] Guo RF., Huang GB. and Lin QP., 2009, "Error minimized extreme learning machine with growth of hidden nodes and incremental learning", IEEE Trans Neural Netw 20(8):1352-1357 [DOI:10.1109/TNN.2009.2024147]
34. [34] Zhu QY., Qin AK. amdSuganthan, PN., 2005, "Evolutionary extreme learning machine", Pattern Recogn", 38(10):1759-1763 [DOI:10.1016/j.patcog.2005.03.028]
35. [35] Huang GB. and Siew CK., 2005, "Extreme learning machine with randomly assigned RBF Kernels", Int J Inf Technol 11(1):16-24
36. [36] Huang GB., Lei C. and Siew CK., 2006, "Universal approximation using incremental constructive feed forward networks with random hidden nodes", IEEE Trans Neural Netw 17(4):879-892 [DOI:10.1109/TNN.2006.875977]
37. [37] Ding S., Guo L. amd Hou Y., 2017, "Extreme learning machine with kernel model based on deep learning, Neural Computing and Applications", 28(8):1975-84. [DOI:10.1007/s00521-015-2170-y]
38. [38] Shokri A., Bozorg Haddad O., and Mari˜no M. A., 2013, "Algorithm for increasing the speed of evolutionary optimization and its accuracy in multi-objective problems", Water Resour. Manage. 27(7): 2231-2249. [DOI:10.1007/s11269-013-0285-4]
39. [39] Ashofteh P.S., Bozorg Haddad O. and Mari˜no, M.A., 2013, "Climate change impact on reservoir performance indices in agricultural water supply", Journal of Irrigation and Drainage Engineering, 139(2):19434774. [DOI:10.1061/(ASCE)IR.1943-4774.0000496]
40. [40] Karamouz M., Houck M. H. and Delleur, J.W., 1992, "Optimization and simulation of multiple reservoir systems", J. Water Resour. Plann. Manage. 71: 71-81. 10.1061/(ASCE)0733-9496(1992)118%3A1. [DOI:10.1061/(ASCE)0733-9496(1992)118:1(71)]
41. [41] Velavan TP, Meyer CG., 2020,"The COVID-19 epidemic",Trop Med Int Health, 25:278-280. [DOI:10.1111/tmi.13383]
42. [42] Giordano G., BlanchiniF. and Bruno R., 2020, "Modeling the COVID-19 epidemic and implementation of population-wide interventions in Italy", Nat Med. 26: 855-860. [DOI:10.1038/s41591-020-0883-7]
43. [43] Del Ser J., OsabaE. and Molina D., 2019, "Bio-inspired computation: Where we stand and what's next", SwarmEvolComput. 48: 220-250. [DOI:10.1016/j.swevo.2019.04.008]
44. [44].Boussaïd I., LepagnotJ. and Siarry P. ,2013, "A survey on optimization metaheuristics", Inf Sci.;237:82-117 [DOI:10.1016/j.ins.2013.02.041]
45. [45] World Health Organization. 2019, "Available online athttps://www.who.int/es/emergencies/diseases/novel-coronavirus-2019" (last accessed March20).
46. [46] Martínez-Álvarez, F., Asencio-Cortés, G., Torres, J. F. , Gutiérrez-Avilés, D., Melgar-García, L. and Pérez-Chacón, R., 2020, "Coronavirus Optimization Algorithm: A Bioinspired Metaheuristic Based on the COVID-19 Propagation Model", Big Data, 8(4) . [DOI:10.1089/big.2020.0051]

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