Modeling reservoir water release decision using adaptive neuro fuzzy inference system

• Main Authors: Abdul Mokhtar, Suriyati, Wan Ishak, Wan Hussain, Md Norwawi, Norita
• Format: Article
• Language: English
• Published: Universiti Utara Malaysia 2016
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://repo.uum.edu.my/20879/1/JICT%2015%202%202016%20141%20152.pdf; http://repo.uum.edu.my/20879/; http://jict.uum.edu.my/images/pdf3/vol15no2/7jictno22016.pdf

Reservoir water release decision is one of the critical actions in determining the quantity of water to be retained or released from the reservoir.Typically, the decision is influenced by the reservoir inflow that can be estimated based on the rainfall recorded at the reservoir’s upstream areas.Since the rainfall is recorded at several different locations, the use of temporal pattern alone may not be appropriate.Hence, in this study a spatial temporal pattern was used to retain the spatial information of the rainfall’s location.In addition, rainfall recorded at different locations may cause fuzziness in the data representation.Therefore, a hybrid computational intelligence approach, namely the Adaptive Neuro Fuzzy Inference System (ANFIS), was used to develop a reservoir water release decision model.ANFIS integrates both the neural network and fuzzy logic principles in order to deal with the fuzziness and complexity of the spatial temporal pattern of rainfall.In this study, the Timah Tasoh reservoir and rainfall from five upstream gauging stations were used as a case study.Two ANFIS models were developed and their performances were compared based on the lowest square error achieved from the simulation conducted.Both models utilized the spatial temporal pattern of the rainfall as input.The first model considered the current reservoir water level as an additional input, while the second model retained the existing input.The result indicated that the application of ANFIS could be used successfully for modeling reservoir water release decision. The first model with the additional input showed better performance with the lowest square error compared to the second model.