Fuzzy Neural Network Modeling of Reservoir Operation

被引:27
|
作者
Deka, Paresh Chandra [1 ]
Chandramouli, V. [2 ,3 ]
机构
[1] ArbaMinch Univ, Arbaminch, Ethiopia
[2] Univ Kentucky, Dept Civil Engn, Kentucky Water Resources Res Ctr, Lexington, KY 40506 USA
[3] Univ Kentucky, Dept Civil Engn, Adjunct Fac, Lexington, KY 40506 USA
关键词
INTELLIGENT CONTROL; PREDICTION; SYSTEM;
D O I
10.1061/(ASCE)0733-9496(2009)135:1(5)
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The present study aims at the application of the hybrid model, which consists of artificial neural network and fuzzy logic in the reservoir operating policy during critical periods. The proposed hybrid model [fuzzy neural network (FNN)] combines the learning ability of artificial neural networks and the transparent nature of fuzzy logic. The FNN model is found to be highly adaptive and efficient in investigating nonlinear relationships among different variables. The FNN model has been developed to study the behavior of optimal release operating policy on the proposed reservoir in Pagladiya River of the Assam State in India. Here, reservoir operation policies were formulated through dynamic programming. The optimal release was related to storage, inflow, and demand. The advantages of using the FNN model in reservoir release are discussed using the case study.
引用
收藏
页码:5 / 12
页数:8
相关论文
共 50 条
  • [41] Enhancing reservoir inflow forecasting precision through Bayesian Neural Network modeling and atmospheric teleconnection pattern analysis
    Farahani, Ehsan Vasheghani
    Bavani, Ali Reza Massah
    Roozbahani, Abbas
    STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, 2025, 39 (01) : 205 - 229
  • [42] Modeling of lime production process using artificial neural network
    Daeichian, Abolghasem
    Shahramfar, Rana
    Heidari, Elham
    CHEMICAL PRODUCT AND PROCESS MODELING, 2022, 17 (06): : 655 - 667
  • [43] Modeling of Thermal Cracking of Heavy Liquid Hydrocarbon: Application of Kinetic Modeling, Artificial Neural Network, and Neuro-Fuzzy Models
    Sedighi, Mehdi
    Keyvanloo, Kamyar
    Towfighi, Jafar
    INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2011, 50 (03) : 1536 - 1547
  • [44] Coordinated control system modeling of ultra-supercritical unit based on a new fuzzy neural network
    Hou, Guolian
    Xiong, Jian
    Zhou, Guiping
    Gong, Linjuan
    Huang, Congzhi
    Wang, Shunjiang
    ENERGY, 2021, 234
  • [45] Predictive modeling of mechanical properties of welded joints based on generalized dynamic fuzzy RBF neural network
    Zhang Y.
    Dong J.
    Hou J.
    Dong, Junhui (jhdong1009@163.com), 1600, Harbin Research Institute of Welding (38): : 37 - 40
  • [46] Modeling of nonlinear systems using the self-organizing fuzzy neural network with adaptive gradient algorithm
    Han, Hong-Gui
    Lin, Zheng-Lai
    Qiao, Jun-Fei
    NEUROCOMPUTING, 2017, 266 : 566 - 578
  • [47] RF power amplifiers behavioral modeling based on extended neural network with additional dynamic fuzzy weights
    Yuan, X. -H.
    Feng, Q. -Y.
    Zhou, L. -C.
    JOURNAL OF ELECTROMAGNETIC WAVES AND APPLICATIONS, 2013, 27 (13) : 1694 - 1701
  • [48] Modeling the air permeability of pile loop knit fabrics using fuzzy logic and artificial neural network
    Haroglu, Derya
    JOURNAL OF THE TEXTILE INSTITUTE, 2023, 114 (02) : 265 - 272
  • [49] A Fast and Compact Fuzzy Neural Network for Online Extraction of Fuzzy Rules
    Wang, Ning
    Meng, Xianyao
    Bai, Yiming
    CCDC 2009: 21ST CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-6, PROCEEDINGS, 2009, : 4249 - 4254
  • [50] PRSV equation of state parameter modeling through artificial neural network and adaptive network-based fuzzy inference system
    Hatami, Tahmaseb
    Rahimi, Masoud
    Daraei, Hiua
    Heidaryan, Ehsan
    Alsairafi, Ammar Abdulaziz
    KOREAN JOURNAL OF CHEMICAL ENGINEERING, 2012, 29 (05) : 657 - 667