耦合調適性網路模糊推論系統於颱風暴雨時期水庫濁度預報之研究

Reservoir Turbidity Forecasting Using a Coupled Adaptive Network-Based Fuzzy Inference System

徐年盛(Nien-Sheng Hsu) ； 魏志強(Chih-Chiang Wei) ； 黃建霖(Chien-Lin Huang) ； 姚俊豪(Chun-Hao Yao)

《中國土木水利工程學刊》 23卷3期 (2011/09) Pp. 257-267

https://doi.org/10.6652/JoCICHE.201109_23(3).0002

摘要

本研究之目的為以耦合與非耦合架構之調適性網路模糊推論系統（ANFIS）與倒傳遞類神經網路（BPNN）建構最佳短至長延時之水庫濁度預報模式，以供水庫操作人員決策參考之用。本研究以石門水庫為研究區域，預報水庫內龍珠灣與第二原水抽水站之原水濁度。研究結果顯示耦合架構於預報較穩定流況位置之水庫濁度時，相對於紊流流況位置有較佳之誤差容忍並有效提升長延時預報精確度。整體而言，BPNN之預報精確度與穩定性稍較ANFIS佳，但是ANFIS之建構時間較BPNN快速約六十倍。

關鍵字

調適性網路模糊推論系統 ； 濁度預報 ； 耦合 ； 倒傳遞類神經網路

並列摘要

The purpose of this study is to apply ANFIS (Adaptive Networkbased Fuzzy Inference System) and BPNN (Back-Propagation Neural Network) with a coupled and non-coupled structure to construct suitable reservoir turbidity forecast models for short and long lead times. The proposed models can be used by reservoir operators to predict reservoir turbidity while releasing water during typhoon periods. The study site was at Shih-Men reservoir. The Lung-Chu-Wan and the second pumping stations were selected as the prediction locations. Results showed that the coupled structures in the ANFIS and BPNN models demonstrated superior tolerance and ability to handle predictive errors in the stable flow regime compared to those in the turbulence flow regime when forecasting reservoir turbidity. Furthermore, the precision of predicting turbidity and stability was better with BPNN than with ANFIS. However, the training CPU time needed in constructing BPNN was sixty times greater than that of ANFIS.

並列關鍵字

adaptive network-based fuzzy inference system ； turbidity forecast ； coupled ； back-propagation neural network