Runoff forecasting using a Takagi-Sugeno neuro-fuzzy model with online learning

被引:44
作者
Talei, Amin [1 ,2 ,6 ]
Chua, Lloyd Hock Chye [3 ]
Quek, Chai [4 ]
Jansson, Per-Erik [5 ]
机构
[1] Nanyang Technol Univ, DHI NTU Water & Environm Res Ctr, Singapore 639798, Singapore
[2] Nanyang Technol Univ, Sch Civil & Environm Engn, Educ Hub, Singapore 639798, Singapore
[3] Nanyang Technol Univ, Sch Civil & Environm Engn, Singapore 639798, Singapore
[4] Nanyang Technol Univ, Sch Comp Engn, Ctr Computat Intelligence, Intelligent Syst Lab, Singapore 639798, Singapore
[5] KTH Royal Inst Technol, Dept Land & Water Resources Engn, S-10044 Stockholm, Sweden
[6] Monash Univ, Sch Engn, Selangor Darul Ehsan 46150, Malaysia
关键词
Rainfall-runoff modeling; Local learning; Global learning; Neuro-fuzzy systems; ANFIS; DENFIS; TIME-SERIES; PREDICTIVE CAPABILITIES; INFERENCE SYSTEM; NETWORKS; SIMULATION; HYDROLOGY; FLOW;
D O I
10.1016/j.jhydrol.2013.02.022
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
A study using local learning Neuro-Fuzzy System (NFS) was undertaken for a rainfall-runoff modeling application. The local learning model was first tested on three different catchments: an outdoor experimental catchment measuring 25 m(2) (Catchment 1), a small urban catchment 5.6 km(2) in size (Catchment 2), and a large rural watershed with area of 241.3 km(2) (Catchment 3). The results obtained from the local learning model were comparable or better than results obtained from physically-based, i.e. Kinematic Wave Model (KWM), Storm Water Management Model (SWMM), and Hydrologiska Byrans Vattenbalan-savdelning (HBV) model. The local learning algorithm also required a shorter training time compared to a global learning NFS model. The local learning model was next tested in real-time mode, where the model was continuously adapted when presented with current information in real time. The real-time implementation of the local learning model gave better results, without the need for retraining, when compared to a batch NFS model, where it was found that the batch model had to be retrained periodically in order to achieve similar results. (C) 2013 Elsevier B.V. All rights reserved.
引用
收藏
页码:17 / 32
页数:16
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