Forecasting the Level of Reservoirs Using Multiple Input Fuzzification in ANFIS

Nariman Valizadeh, Ahmed El-Shafie

    Research output: Contribution to journalArticle

    18 Citations (Scopus)

    Abstract

    Estimation the Level of water is one of the crucial subjects in reservoir management influencing on reservoir operation and decision making. One of the most accurate artificial intelligence model used broadly in water resource aspects is adaptive neuro-fuzzy interface system (ANFIS) taking in to account the membership functions (MF) on the basis of the smoothness characteristics and mathematical components each for set of input data. All researches in hydrological estimation used ANFIS, merely a type of MF has been noticed for all sets of inputs without considering the response of each of them. This study is applying a specified certain MFs for each type of input to improve the accuracy of ANFIS model in forecasting the water level in Klang Gates Dam in Malaysia. On the basis of the previous studies, two most popular MFs, Generalized Bell Shape MF and, Gaussian MF, are employed for examine the new pattern in two inputs ANFIS architecture resulted less stress in error performance, and higher accuracy in estimation, compare to the traditional ANFIS model. The aim is achieved by evaluating the performance in and fitness of the model in daily reservoir estimation.

    Original languageEnglish
    Pages (from-to)3319-3331
    Number of pages13
    JournalWater Resources Management
    Volume27
    Issue number9
    DOIs
    Publication statusPublished - Jul 2013

    Fingerprint

    Membership functions
    Reservoir management
    Petroleum reservoirs
    Water levels
    Water resources
    artificial intelligence
    Dams
    Artificial intelligence
    Decision making
    water level
    fitness
    dam
    water resource
    decision making
    Water
    water

    Keywords

    • Klang Dam
    • Level estimation
    • MFs
    • Neuro-fuzzy

    ASJC Scopus subject areas

    • Water Science and Technology
    • Civil and Structural Engineering

    Cite this

    Forecasting the Level of Reservoirs Using Multiple Input Fuzzification in ANFIS. / Valizadeh, Nariman; El-Shafie, Ahmed.

    In: Water Resources Management, Vol. 27, No. 9, 07.2013, p. 3319-3331.

    Research output: Contribution to journalArticle

    Valizadeh, Nariman ; El-Shafie, Ahmed. / Forecasting the Level of Reservoirs Using Multiple Input Fuzzification in ANFIS. In: Water Resources Management. 2013 ; Vol. 27, No. 9. pp. 3319-3331.
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