Long-Term Prediction of Discharges in Manwan Hydropower Using Adaptive-Network-Based Fuzzy Inference Systems Models

• Published in: Advances in Natural Computation pp. 1152 - 1161
• Main Authors: Cheng, Chun-Tian, Lin, Jian-Yi, Sun, Ying-Guang, Chau, Kwokwing
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: ANFIS Model; Applied sciences; Artificial intelligence; Artificial Neural Network Model; Computer science; control theory; systems; Exact sciences and technology; Fuzzy Inference System; Membership Function; Root Mean Square Error; Validation; Membership function; Inference; Scheduling; Rivers; Neural network; Long term; Modeling; Adaptive method; Forecasting; Fuzzy neural nets; Hydrology; Flow models
• DOI: 10.1007/11539902_145
• ISBN: 9783540283201; 354028320X; 3540283234; 9783540283232
• ISSN: 0302-9743

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.