Fuzzy rule-based modeling of reservoir operation

被引:129
|
作者
Shrestha, BP
Duckstein, L
Stakhiv, EZ
机构
[1] UNIV ARIZONA, DEPT SYST & IND ENGN, TUCSON, AZ 85721 USA
[2] USA, IWR, POLICY & SPEC STUDIES DIV, CORPS ENGINEERS, FT BELVOIR, VA 22060 USA
关键词
D O I
10.1061/(ASCE)0733-9496(1996)122:4(262)
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
A fuzzy rule-based model is constructed to derive operation rules for a multipurpose reservoir. The case study of the Tenkiller Lake in Oklahoma illustrates the methodology. Operation rules are generated on the basis of economic development criteria such as hydropower; municipal; industrial and irrigation demands; flood control and navigation; and environmental criteria such as water quality for fish and wildlife preservation, recreational needs, and downstream how regulation. The fuzzy rule-based model operates on an ''if-then'' principle, where the ''if'' is a vector of fuzzy explanatory variables or premises and ''then,'' of fuzzy consequences. The reservoir storage level, estimated inflows, and demands are used as the premises and release from the reservoir is taken as the consequence. Split sampling of historical data (mean daily time series of flow, lake level, demands, and releases) is used to train and then validate the rules. Different performance indices are calculated and two figures of merit, namely, engineering sustainability and engineering risk are developed for evaluating the rules generated by the model, which appears to be easy to construct, apply, and extend to a complex system of reservoirs.
引用
收藏
页码:262 / 269
页数:8
相关论文
共 50 条
  • [31] Chaining in fuzzy rule-based systems
    Hall, LO
    NINTH IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE 2000), VOLS 1 AND 2, 2000, : 906 - 910
  • [32] Fuzzy rule-based downscaling of precipitation
    Bardossy, A
    Bogardi, I
    Matyasovszky, I
    THEORETICAL AND APPLIED CLIMATOLOGY, 2005, 82 (1-2) : 119 - 129
  • [33] Fuzzy and rule-based image convolution
    Looney, CG
    MATHEMATICS AND COMPUTERS IN SIMULATION, 2000, 51 (3-4) : 209 - 219
  • [34] Clustering Based on Fuzzy Rule-Based Classifier
    Behera, D. K.
    Patra, P. K.
    COMPUTATIONAL INTELLIGENCE IN DATA MINING, VOL 1, 2015, 31 : 233 - 242
  • [35] Fuzzy Inference Decision Rule for Optimal Reservoir Operation
    Yang, Pan
    Ng, Tze Ling
    2015 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC 2015): BIG DATA ANALYTICS FOR HUMAN-CENTRIC SYSTEMS, 2015, : 2239 - 2243
  • [36] A NEW CLUSTERING-BASED APPROACH FOR MODELING FUZZY RULE-BASED CLASSIFICATION SYSTEMS
    Farahbod, F.
    Eftekhari, M.
    IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING, 2013, 37 (E1) : 67 - 77
  • [37] Fuzzy Rule-Based Classifier Design with Co-operation of Biology Related Algorithms
    Akhmedova, Shakhnaz
    Semenkin, Eugene
    Stanovov, Vladimir
    ADVANCES IN SWARM INTELLIGENCE, ICSI 2016, PT II, 2016, 9713 : 198 - 205
  • [38] On modeling of responses generated by travel 2.0 implementation: fuzzy rule-based systems
    Basaran, Murat Alper
    Dogan, Seden
    Kantarci, Kemal
    INTERNATIONAL JOURNAL OF CONTEMPORARY HOSPITALITY MANAGEMENT, 2020, 32 (04) : 1503 - 1522
  • [39] Modeling a High Concentrator Photovoltaic Module Using Fuzzy Rule-Based Systems
    Angel Gadeo-Martos, Manuel
    Jesus Yuste-Delgado, Antonio
    Almonacid Cruz, Florencia
    Fernandez-Prieto, Jose-Angel
    Canada-Bago, Joaquin
    ENERGIES, 2019, 12 (03)
  • [40] Support vector learning mechanism for fuzzy rule-based modeling: A new approach
    Chiang, JH
    Hao, PY
    IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2004, 12 (01) : 1 - 12