Application of Adaptive Neuro-fuzzy Inference System for Dam Inflow Prediction using Longrange Weather Forecast

被引：0
|
作者：
Awan, Jehangir Ashraf [1 ]
Bae, Deg-hyo [1 ]
机构：
[1] Sejong Univ, Dept Civil & Environm Engn, Seoul, South Korea
来源：
2013 EIGHTH INTERNATIONAL CONFERENCE ON DIGITAL INFORMATION MANAGEMENT (ICDIM) | 2013年
关键词：
ANFIS; Subtractive Clustering; Weather Forecast; Dam Inflow Forecast; NETWORKS; MODELS; RIVER;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Dam inflow forecast plays an important role for optimal reservoir operations. There are several techniques in use for dam inflow forecast; however, accurate long-range dam inflow forecast is still a challenging task. In this study, we developed a model based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for monthly dam inflow forecast. The subtractive clustering method is used to find optimum set of fuzzy rules. To obtain appropriate ANFIS structure the model is tuned with different values of cluster radius for subtractive clustering. The model is trained using dam inflow and weather data (i.e. temperature and rainfall) of preceding month and monthly normal rainfall of forecasting month as input for dam inflow forecast. To assess the significance of rainfall forecast for improvement of dam inflow prediction we attempted to incorporate Korea Meteorological Administration (KMA) monthly rainfall forecast as an input with other parameters. The use of monthly rainfall forecast showed significant improvement in the dam inflow forecast. The viability of the proposed model is demonstrated for 3 major dams of South Korea.
引用
收藏
页码：247 / 251
页数：5
相关论文
共 50 条
  • [41] Shear strength prediction of RC beams using adaptive neuro-fuzzy inference system
    Naderpour, H.
    Mirrashid, M.
    [J]. SCIENTIA IRANICA, 2020, 27 (02) : 657 - 670
  • [42] Noise cancellation by using Adaptive Neuro-Fuzzy Inference System
    Zhang, Bao-cheng
    Xu, Xie-xian
    [J]. Chuan Bo Li Xue/Journal of Ship Mechanics, 2000, 4 (04): : 62 - 67
  • [43] Geoacoustic inversion using adaptive neuro-fuzzy inference system
    Satyanarayana Yegireddi
    Arvind Kumar
    [J]. Computational Geosciences, 2008, 12 : 513 - 523
  • [44] PREDICTION OF BEARING FAULT SIZE BY USING MODEL OF ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM
    Kaplan, Kaplan
    Kuncan, Melih
    Ertunc, H. Metin
    [J]. 2015 23RD SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2015, : 1925 - 1928
  • [45] Prediction of the Performance of a Solar Thermal Energy System Using Adaptive Neuro-Fuzzy Inference System
    Yaici, Wahiba
    Entchev, Evgueniy
    [J]. 2014 INTERNATIONAL CONFERENCE ON RENEWABLE ENERGY RESEARCH AND APPLICATION (ICRERA), 2014, : 601 - 604
  • [46] Geoacoustic inversion using adaptive neuro-fuzzy inference system
    Yegireddi, Satyanarayana
    Kumar, Arvind
    [J]. COMPUTATIONAL GEOSCIENCES, 2008, 12 (04) : 513 - 523
  • [47] Illuminant Estimation Using Adaptive Neuro-Fuzzy Inference System
    Luo, Yunhui
    Wang, Xingguang
    Wang, Qing
    Chen, Yehong
    [J]. APPLIED SCIENCES-BASEL, 2021, 11 (21):
  • [48] Improved adaptive neuro-fuzzy inference system
    Benmiloud, Tarek
    [J]. NEURAL COMPUTING & APPLICATIONS, 2012, 21 (03): : 575 - 582
  • [49] Multioutput Adaptive Neuro-fuzzy Inference System
    Benmiloud, T.
    [J]. RECENT ADVANCES IN NEURAL NETWORKS, FUZZY SYSTEMS & EVOLUTIONARY COMPUTING, 2010, : 94 - 98
  • [50] Improved adaptive neuro-fuzzy inference system
    Tarek Benmiloud
    [J]. Neural Computing and Applications, 2012, 21 : 575 - 582
12345