類神經網路流量系集預報模式之應用

The Application of Ensemble Flood Forecasting Using Artificial Neural Network

林欣平(Hsin-Ping Lin) ； 陳昶憲(Chang-Shian Chen) ； 唐偉倫(Wei-Lun Tang) ； 鍾侑達(You-Da Jhong)

《農業工程學報》 64卷1期 (2018/03) Pp. 29-40

https://doi.org/10.29974/JTAE.201803_64(1).0003

摘要

近年來，人工智慧方法常應用於水文流量預測上，本研究是以倒傳遞類神經網路為洪水預測模式之主要架構，即以倒傳遞類神經網路建構集水區系統特性，然而建模時，類神經網路之權重與閥值透過隨機選取，可取得無限多組滿足某一模式評鑑指標精度的參數組。本研究嘗試以自組織映射圖網路，聚類出具代表性的參數權重組，進行流量預測、區間預測及機率預測，由結果可知在同類參數的類神經網路模式具有高準確性之流量預測。

關鍵字

倒傳遞類神經網路 ； 自組織映射圖網路 ； 系集洪流量預測

並列摘要

In recent years, artificial intelligence methods predicted the hydrologic flood forecasting universally. This study employed a Back-Propagation Network (BPN) as the main structure in flood forecasting to learn the characteristic of catchment system. When the modeling, it could obtain the infinite set of parameters to satisfy the efficiency coefficient with stochastic initiate parameters. This paper attempted to combine Self-Organizing Map (SOM) to cluster representative weights and biases. A SOM network with classification ability was applied to classify the BPN parameter rules and to obtain the winning parameters. Finally, it will provide the point prediction, interval prediction and probability prediction. It can see that the artificial neural network has a high accuracy of the flood forecasting.

並列關鍵字

Back-Propagation Network ； Self-Organizing Map ； Ensemble Flood Forecasting