Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
An Introduction to Fuzzy Logic Applications
An Introduction to Fuzzy Logic Applications
Fuzzy Modeling and Control: Selected Works of M. Sugeno
Fuzzy Modeling and Control: Selected Works of M. Sugeno
Fuzzy Neural Intelligent Systems: Mathematical Foundation and the Applications in Engineering
Fuzzy Neural Intelligent Systems: Mathematical Foundation and the Applications in Engineering
Fuzzy Model-Based Reinforcement Learning
Advances in Computational Intelligence and Learning: Methods and Applications
Fuzzy inference system learning by reinforcement methods
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Artificial Intelligence techniques: An introduction to their use for modelling environmental systems
Mathematics and Computers in Simulation
An expert system for predicting aeration performance of weirs by using ANFIS
Expert Systems with Applications: An International Journal
Advances in Engineering Software
Using intelligent methods to predict air-demand ratio in venturi weirs
Advances in Engineering Software
| Hi-index | 0.00 |
An accurate simulation model is a necessary tool for optimizing allocation of scarce water resources in large-scale river basins. Adaptive Neural Fuzzy Inference System (ANFIS) method is used to simulate seven interconnected sub-basins in a regional river system located in Iran. Simulated predictions of the method are compared with historical data measurements. ANFIS is a powerful tool for simulating water resources systems of all sub-basins. In this study, a new methodology, Adaptive Neural Fuzzy Reinforcement Learning (ANFRL) is presented for obtaining optimal values of the decision variables. By combining ANFIS with Fuzzy Reinforcement Learning within the content of historical data over a consecutive monthly management period, ANFRL method was derived. Based upon the results of this research, this methodology can be used to develop fuzzy rule systems that accurately simulate the behavior of complex river basin systems within the context of uncertainty. As previous researches have shown that, when simulation model accurately reproduces observed river basin behavior, the optimization model yields better results. Application of this approach in the present case study shows that the effects of uncertainty, imprecise and random factors are 21, 11 and 15% over water resources system, water demand estimated and hydrological regime, respectively. Finally, the results of this method showed that about 16% improvement in water allocation was attained when compared to the primary water resources management in this case study.