Long-Term prediction of discharges in manwan hydropower using adaptive-network-based fuzzy inference systems models

Authors:
Chun-Tian Cheng;Jian-Yi Lin;Ying-Guang Sun;Kwokwing Chau
Affiliations:
Institute of Hydroinformatics, Department of Civil Engineering, Dalian University of Technology, Dalian, P.R. China;Institute of Hydroinformatics, Department of Civil Engineering, Dalian University of Technology, Dalian, P.R. China;Institute of Hydroinformatics, Department of Civil Engineering, Dalian University of Technology, Dalian, P.R. China;Department of Civil and Structural Engineering, Hong Kong Polytechnic University, Kowloon, Hong Kong, People's Republic of China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Year:
2005

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Abstract

Forecasting reservoir inflow is important to hydropower reservoir management and scheduling. An Adaptive-Network-based Fuzzy Inference System (ANFIS) is successfully developed to forecast the long-term discharges in Manwan Hydropower. Using the long-term observations of discharges of monthly river flow discharges during 1953-2003, different types of membership functions and antecedent input flows associated with ANFIS model are tested. When compared to the ANN model, the ANFIS model has shown a significant forecast improvement. The training and validation results show that the ANFIS model is an effective algorithm to forecast the long-term discharges in Manwan Hydropower. The ANFIS model is finally employed in the advanced water resource project of Yunnan Power Group.