https://scholars.lib.ntu.edu.tw/handle/123456789/87306
標題: | Adaptive neuro-fuzzy inference system for prediction of water level in reservoir | 作者: | Chang, Fi-John Chang, Ya-Ting |
關鍵字: | Adaptive neuro-fuzzy inference system (ANFIS);Artificial neural networks;Water level forecasting;Reservoir management;Control and prediction | 公開日期: | 一月-2006 | 起(迄)頁: | 1-10 | 來源出版物: | Advances in Water Resources 29 (1) | 摘要: | Accurate prediction of the water level in a reservoir is crucial to optimizing the management of water resources. A neuro-fuzzy hybrid approach was used to construct a water level forecasting system during flood periods. In particular, we used the adaptive network-based fuzzy inference system (ANFIS) to build a prediction model for reservoir management. To illustrate the applicability and capability of the ANFIS, the Shihmen reservoir, Taiwan, was used as a case study. A large number (132) of typhoon and heavy rainfall events with 8640 hourly data sets collected in past 31 years were used. To investigate whether this neuro-fuzzy model can be cleverer (accurate) if human knowledge, i.e. current reservoir operation outflow, is provided, we developed two ANFIS models: one with human decision as input, another without. The results demonstrate that the ANFIS can be applied successfully and provide high accuracy and reliability for reservoir water level forecasting in the next three hours. Furthermore, the model with human decision as input variable has consistently superior performance with regard to all used indexes than the model without this input. |
URI: | http://ntur.lib.ntu.edu.tw//handle/246246/176291 | DOI: | 10.1016/j.advwatres.2005.04.015 |
顯示於: | 生物環境系統工程學系 |
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