State-of-the-Art for Modelling Reservoir Inflows and Management Optimization

Shi Mei Choong, A. El-Shafie

    Research output: Contribution to journalArticle

    17 Citations (Scopus)

    Abstract

    A multi-purpose reservoir operation requires a good decision from the operator in order to maximize the value of water. Therefore, a good inflows modelling will be very helpful in providing a better optimization solution. By then, perplexing in the selection of the most preferable solution might happen to the operator. A comprehensive review of different computational intelligent models which applied in reservoir inflows modelling and management optimization is presented in this paper. The aim of this study is to review, compare and summarize their attempts along with difficulties in dealing with the water management problem. The benefits derived from such comparison are used to improve the performance of the existing models for future work. Study showed that models based evolutionary algorithm revealing a great potential in the management of reservoir operation. However more research about the most recent self-optimization modelling application needs to be revised.

    Original languageEnglish
    Pages (from-to)1267-1282
    Number of pages16
    JournalWater Resources Management
    Volume29
    Issue number4
    DOIs
    Publication statusPublished - 2014

    Fingerprint

    inflow
    modeling
    Water management
    Evolutionary algorithms
    water management
    state of the art
    Water
    water
    decision
    comparison

    Keywords

    • Artificial bee colony
    • Inflow forecasting
    • Reservoir optimization

    ASJC Scopus subject areas

    • Water Science and Technology
    • Civil and Structural Engineering

    Cite this

    State-of-the-Art for Modelling Reservoir Inflows and Management Optimization. / Choong, Shi Mei; El-Shafie, A.

    In: Water Resources Management, Vol. 29, No. 4, 2014, p. 1267-1282.

    Research output: Contribution to journalArticle

    Choong, Shi Mei ; El-Shafie, A. / State-of-the-Art for Modelling Reservoir Inflows and Management Optimization. In: Water Resources Management. 2014 ; Vol. 29, No. 4. pp. 1267-1282.
    @article{8dc110dc78454ef081190e1fd3d2af8a,
    title = "State-of-the-Art for Modelling Reservoir Inflows and Management Optimization",
    abstract = "A multi-purpose reservoir operation requires a good decision from the operator in order to maximize the value of water. Therefore, a good inflows modelling will be very helpful in providing a better optimization solution. By then, perplexing in the selection of the most preferable solution might happen to the operator. A comprehensive review of different computational intelligent models which applied in reservoir inflows modelling and management optimization is presented in this paper. The aim of this study is to review, compare and summarize their attempts along with difficulties in dealing with the water management problem. The benefits derived from such comparison are used to improve the performance of the existing models for future work. Study showed that models based evolutionary algorithm revealing a great potential in the management of reservoir operation. However more research about the most recent self-optimization modelling application needs to be revised.",
    keywords = "Artificial bee colony, Inflow forecasting, Reservoir optimization",
    author = "Choong, {Shi Mei} and A. El-Shafie",
    year = "2014",
    doi = "10.1007/s11269-014-0872-z",
    language = "English",
    volume = "29",
    pages = "1267--1282",
    journal = "Water Resources Management",
    issn = "0920-4741",
    publisher = "Springer Netherlands",
    number = "4",

    }

    TY - JOUR

    T1 - State-of-the-Art for Modelling Reservoir Inflows and Management Optimization

    AU - Choong, Shi Mei

    AU - El-Shafie, A.

    PY - 2014

    Y1 - 2014

    N2 - A multi-purpose reservoir operation requires a good decision from the operator in order to maximize the value of water. Therefore, a good inflows modelling will be very helpful in providing a better optimization solution. By then, perplexing in the selection of the most preferable solution might happen to the operator. A comprehensive review of different computational intelligent models which applied in reservoir inflows modelling and management optimization is presented in this paper. The aim of this study is to review, compare and summarize their attempts along with difficulties in dealing with the water management problem. The benefits derived from such comparison are used to improve the performance of the existing models for future work. Study showed that models based evolutionary algorithm revealing a great potential in the management of reservoir operation. However more research about the most recent self-optimization modelling application needs to be revised.

    AB - A multi-purpose reservoir operation requires a good decision from the operator in order to maximize the value of water. Therefore, a good inflows modelling will be very helpful in providing a better optimization solution. By then, perplexing in the selection of the most preferable solution might happen to the operator. A comprehensive review of different computational intelligent models which applied in reservoir inflows modelling and management optimization is presented in this paper. The aim of this study is to review, compare and summarize their attempts along with difficulties in dealing with the water management problem. The benefits derived from such comparison are used to improve the performance of the existing models for future work. Study showed that models based evolutionary algorithm revealing a great potential in the management of reservoir operation. However more research about the most recent self-optimization modelling application needs to be revised.

    KW - Artificial bee colony

    KW - Inflow forecasting

    KW - Reservoir optimization

    UR - http://www.scopus.com/inward/record.url?scp=84925535179&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=84925535179&partnerID=8YFLogxK

    U2 - 10.1007/s11269-014-0872-z

    DO - 10.1007/s11269-014-0872-z

    M3 - Article

    AN - SCOPUS:84925535179

    VL - 29

    SP - 1267

    EP - 1282

    JO - Water Resources Management

    JF - Water Resources Management

    SN - 0920-4741

    IS - 4

    ER -