Fuzzy rule-based control of multireservoir operation system for flood and drought mitigation in the Upper Mun River Basin

被引:1
作者
Phankamolsil, Yutthana [1 ]
Rittima, Areeya [2 ]
Sawangphol, Wudhichart [3 ]
Kraisangka, Jidapa [3 ]
Tabucanon, Allan Sriratana [4 ]
Talaluxmana, Yutthana [5 ]
Vudhivanich, Varawoot [6 ]
机构
[1] Mahidol Univ, Environm Engn & Disaster Management Program, Kanchanaburi Campus, Kanchanaburi, Thailand
[2] Mahidol Univ, Fac Engn, Nakhon Pathom 73170, Thailand
[3] Mahidol Univ, Fac Informat & Commun Technol, Nakhon Pathom 73170, Thailand
[4] Mahidol Univ, Fac Environm & Resource Studies, Nakhon Pathom 73170, Thailand
[5] Kasetsart Univ, Fac Engn, Dept Water Resources Engn, Bangkok, Thailand
[6] Kasetsart Univ, Fac Engn Kamphaeng Saen, Dept Irrigat Engn, Nakhon Pathom, Thailand
关键词
Fuzzy logic; Fuzzy rule-based model; Artificial intelligence; Upper Mun River Basin; RESERVOIR OPERATION; LOGIC;
D O I
10.1007/s40808-024-02081-5
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Strategic reservoir operation, a primary water management measures, plays a significant role in mitigating floods and droughts. Since the reservoir operation involves making complicated decisions on uncertain hydrological variables driven by climate variability, therefore, constructive tool for decision making like fuzzy logic is essential to optimize reservoir management and ensure water security. This study demonstrated fuzzy logic application to multiple reservoir operation in tropical region like Thailand. A Fuzzy Rule-Based Model (FRBM) exploiting FL approach was developed to control the upstream reservoir operation in the Upper Mun River Basin (UMRB) using the data from 2008 to 2021. Implementing FRBM for UMRB was conducted by identifying two key variables; available water storage and 7-day ahead predicted inflow, as fuzzy inputs. The fuzzy output of the system is the release fraction determined by three operational condition modules; flood, neutral, and drought. For flood module, fuzzy release is primarily determined by the predicted inflow. However, the determination of reservoir release for drought and neutral modules is influenced by the targeted water demand. The results of base case illustrate the capability of FRBM in increasing reservoir storages at the start of dry season by 123.56 MCM/yr in UMRB due to the new daily release schemes generated. This allows supplying water closer to the theoretical agricultural needs and gross irrigation water requirement potentially reducing the risk of water shortfall during consecutive dry years. Whereas, the maximum fuzzy release is constrained corresponding to safe channel capacity of tributaries and Upper Mun river, therefore, downstream flooding is accordingly prevented.
引用
收藏
页码:5605 / 5619
页数:15
相关论文
共 30 条
  • [21] Reservoir operation modelling with fuzzy logic
    Panigrahi, DP
    Mujumdar, PP
    [J]. WATER RESOURCES MANAGEMENT, 2000, 14 (02) : 89 - 109
  • [22] A fuzzy rule-based approach to drought assessment
    Pesti, G
    Shrestha, BP
    Duckstein, L
    Bogardi, I
    [J]. WATER RESOURCES RESEARCH, 1996, 32 (06) : 1741 - 1747
  • [23] Rajendra MSR., 2020, IRJMETS, V2, P726
  • [24] Shah MC., 2020, IJARET, V11, P294, DOI [10.17605/OSF.IO/MP8XN, DOI 10.17605/OSF.IO/MP8XN]
  • [25] Vulnerability and drought risk assessment in Iran based on fuzzy logic and hierarchical analysis
    Shiravand, Hengameh
    Bayat, Ali
    [J]. THEORETICAL AND APPLIED CLIMATOLOGY, 2023, 151 (3-4) : 1323 - 1335
  • [26] Fuzzy rule-based modeling of reservoir operation
    Shrestha, BP
    Duckstein, L
    Stakhiv, EZ
    [J]. JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT, 1996, 122 (04) : 262 - 269
  • [27] Fuzzy logic for reservoir operation with reduced rules
    Sivapragasam, C.
    Sugendran, P.
    Marimuthu, M.
    Seenivasakan, S.
    Vasudevan, G.
    [J]. ENVIRONMENTAL PROGRESS, 2008, 27 (01): : 98 - 103
  • [28] Performance evaluation of artificial intelligence paradigms-artificial neural networks, fuzzy logic, and adaptive neuro-fuzzy inference system for flood prediction
    Tabbussum, Ruhhee
    Dar, Abdul Qayoom
    [J]. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2021, 28 (20) : 25265 - 25282
  • [29] FUZZY SETS
    ZADEH, LA
    [J]. INFORMATION AND CONTROL, 1965, 8 (03): : 338 - &
  • [30] Zahran B., 2023, Int J Data Network Sci, V7, P97, DOI [10.5267/j.ijdns.2022.12.001, DOI 10.5267/J.IJDNS.2022.12.001]