類神經網路在水庫放流對河川水位增量之研究

A Study on Water Stage Increment of the River due to Reservoir Drainage by Artificial Neural Network

張大元(Da-Yuan Chang)

指導教授 ： 王安培

中原大學/工學院/土木工程研究所/碩士(2004年)

https://doi.org/10.6840/cycu200400002

摘要

中文摘要

台灣的年雨量約有78 %主要都是集中在夏、秋兩季，因此都是靠夏季洪汛期蓄水以供旱季之用。在颱洪期間，降雨時間短雨量卻很大，倘若水庫放水過少，擔心洪峰過大；放的過多，則怕在枯水季水庫無法正常供水。水庫因此具備多目標功能，需兼顧水庫壩體安全、下游河道不淹水及洪峰過後蓄水的目標，正確的水庫操作顯得非常重要。如果可以事先準確預測颱洪期間水庫入流量及模擬水庫洩洪對下游河道水位的影響，對於水庫操作者將是一大幫助。
本文以石門水庫集水區及大漢溪為演算實例，首先建立「雨量─逕流」模式，嘗試利用倒傳遞類神經網路模擬流域特性，以雨量為網路輸入，流量為網路輸出，建立即時流量預測系統，以推算未來六小時流量；其次模擬「水庫放流─水位」的關係，以雨量、水庫放流、河口潮汐為輸入，下游河道水位為輸出，模擬各種輸入值對水位增量之影響，所得之「水庫放流對河川水位增量三維立體圖」可推算在各種雨量及潮汐下，水庫放流與河川水位的關係。期望本文結果能提供石門水庫在即時操作的參考。

關鍵字

水位模擬 ； 流量預測 ； 類神經網路

並列摘要

Abstract

In Taiwan, roughly 78% of its yearly rainfall concentrates in the summer and autumn because of the particular climate and geographic characteristics. During Typhoon period, the reservoir operators often face the dilemma of maintaining more floodwater and taking the risk of failure of the dam and taking the risk of being drought if excess floodwater is released. The most difficult task of reservoir operation is to consider all the functions of the reservoir. To achieve this purpose, forecasting the inflows of reservoir and simulating downstream water-stage due to the drainage of reservoir are essential to operators.
Watershed of Shihmen reservoir and Da-han River are taken as demonstrations in this paper. The Artificial Neural Network (ANN) simulates the characteristics of river basin, and a “rainfall-runoff” model is established. While the model is built, we could predict the inflows of several hours later. In addition, the relationship between drainage and water-stage is found. Some important results and a three-dimension plot of drainage, water-stage and tide are present in this paper. The plot could figure out relationships between drainage and water-stage under different rainfall intensity or tide-level conditions. It is expected that this research be used for online reservoir operation in the future.

並列關鍵字

Discharge forecast ； Neural network ； Water stage simulate

參考文獻

38.陳昶憲、楊朝仲、王益文，1996，「類神經網路於烏溪流域洪流預報之應用」，中華水土保持學報，vol.27，no. 4：267-274。

41.陳昶憲、陳建宏，1999，「類神經模糊邏輯法應用於洪水位預報」，中國土木水利工程期刊，第十一卷第二期。

33.張斐章、梁晉銘、陳彥璋，2000，「複合演算類神經─模糊推論模式應用於洪水預測」，中華水土保持學報，31(3)。

1.Asaad, Y. S., M. C. Kieran and G. C. Liang, 1997.”Methods for combining the outputs of different rainfall-runoff models”, Journal of Hydrology, p.203-229.

2.Atiya, A. F., S. M. El-Shoura, S. I. Shaheen, & M. S. El-Sherif, 1999. “A Comparison between neural network forecasting techniques-case study: river flow forecasting”, IEEE TRANS. on NN, vol.10: 402-409.