Irrigation Management Based on Reservoir Operation with an Improved Weed Algorithm

被引:9
|
作者
Ehteram, Mohammad [1 ]
Singh, Vijay P. [2 ]
Karami, Hojat [1 ]
Hosseini, Khosrow [1 ]
Dianatikhah, Mojgan [1 ]
Hossain, Md. Shabbir [3 ]
Fai, Chow Ming [3 ]
El-Shafie, Ahmed [4 ]
机构
[1] Semnan Univ, Fac Civil Engn, Dept Water Engn & Hydraul Struct, Semnan 3513119111, Iran
[2] Texas A&M Univ, Zachry Dept Civil Engn, Dept Biol & Agr Engn, 321 Scoates Hall, College Stn, TX 77843 USA
[3] Univ Tenaga Nas, Dept Civil Engn, Kajang 43000, Malaysia
[4] Univ Malaya, Fac Engn, Dept Civil Engn, Kuala Lumpur 50603, Malaysia
关键词
water resources management; Aswan High Dam; weed algorithm; irrigation demands; SWARM OPTIMIZATION ALGORITHM; REAL-TIME OPERATION; GENETIC ALGORITHM; ENERGY MANAGEMENT; CONVERSION; EXTENSION; SYSTEM; RULES; MODEL;
D O I
10.3390/w10091267
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Water scarcity is a serious problem throughout the world. One critical part of this problem is supplying sufficient water to meet irrigation demands for agricultural production. The present study introduced an improved weed algorithm for reservoir operation with the aim of decreasing irrigation deficits. The Aswan High Dam, one of the most important dams in Egypt, was selected for this study to supply irrigation demands. The improved weed algorithm (IWA) had developed local search ability so that the exploration ability for the IWA increased and it could escape from local optima. Three inflows (low, medium and high) to the reservoir were considered for the downstream demands. For example, the average solution for the IWA at high inflow was 0.985 while it was 1.037, 1.040, 1.115 and 1.121 for the weed algorithm (WA), bat algorithm (BA), improved particle swarm optimization algorithm (IPSOA) and genetic algorithm (GA). This meant that the IWA decreased the objective function for high inflow by 5.01%, 5.20%, 11.65% and 12% compared to the WA, BA, IPSOA and GA, respectively. The computational time for the IWA at high inflow was 22 s, which was 12%, 18%, 24% and 29% lower than the WA, BA, IPSOA and GA, respectively. Results indicated that the IWA could meet the demands at all three inflows. The reliability index for the IWA for the three inflows was greater than the WA, BA, IPSOA and GA, meaning that the released water based on IWA could well supply the downstream demands. Thus, the improved weed algorithm is suggested for solving complex problems in water resources management.
引用
收藏
页数:22
相关论文
共 50 条
  • [1] Weed Optimization Algorithm for Optimal Reservoir Operation
    Asgari, Hamid-Reza
    Bozorg Haddad, Omid
    Pazoki, Maryam
    Loaiciga, Hugo A.
    [J]. JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING, 2016, 142 (02)
  • [2] Improved Krill Algorithm for Reservoir Operation
    Karami, Hojat
    Mousavi, Sayed Farhad
    Farzin, Saeed
    Ehteram, Mohammad
    Singh, Vijay P.
    Kisi, Ozgur
    [J]. WATER RESOURCES MANAGEMENT, 2018, 32 (10) : 3353 - 3372
  • [3] Improved Krill Algorithm for Reservoir Operation
    Hojat Karami
    Sayed Farhad Mousavi
    Saeed Farzin
    Mohammad Ehteram
    Vijay P. Singh
    Ozgur Kisi
    [J]. Water Resources Management, 2018, 32 : 3353 - 3372
  • [4] Reservoir operation management using a new hybrid algorithm of Invasive Weed Optimization and Cuckoo Search Algorithm
    Trivedi, Mugdha
    Shrivastava, R. K.
    [J]. AQUA-WATER INFRASTRUCTURE ECOSYSTEMS AND SOCIETY, 2023, 72 (08) : 1607 - 1628
  • [5] Optimal Reservoir Operation Based on Improved Particle Swarm Optimization Algorithm
    Tian, Jiao
    Xie, Jiancang
    Xing, Xiaohong
    [J]. ADVANCES IN HYDROLOGY AND HYDRAULIC ENGINEERING, PTS 1 AND 2, 2012, 212-213 : 502 - 508
  • [6] Optimal Operation of Hydropower Reservoir Systems Using Weed Optimization Algorithm
    Azizipour, Mohammad
    Ghalenoei, Vahid
    Afshar, M. H.
    Solis, S. S.
    [J]. WATER RESOURCES MANAGEMENT, 2016, 30 (11) : 3995 - 4009
  • [7] Optimal Operation of Hydropower Reservoir Systems Using Weed Optimization Algorithm
    Mohammad Azizipour
    Vahid Ghalenoei
    M. H. Afshar
    S. S. Solis
    [J]. Water Resources Management, 2016, 30 : 3995 - 4009
  • [8] Research on multi-objective cascade reservoir operation based on improved NSDE algorithm
    Guo, Yi
    Huang, Sheng-Zhi
    Huang, Qiang
    [J]. Taiwan Water Conservancy, 2019, 67 (01): : 68 - 79
  • [9] Research and Application of Reservoir Flood Control Optimal Operation Based on Improved Genetic Algorithm
    Ren, Minglei
    Zhang, Qi
    Yang, Yuxia
    Wang, Gang
    Xu, Wei
    Zhao, Liping
    [J]. WATER, 2022, 14 (08)
  • [10] Optimization of Cascade Reservoir Operation for Power Generation, Based on an Improved Lightning Search Algorithm
    Tao, Yitao
    Mo, Li
    Yang, Yuqi
    Liu, Zixuan
    Liu, Yixuan
    Liu, Tong
    [J]. WATER, 2023, 15 (19)