Yilan River Flood Flow Persistent Analysis

碩士 === 國立臺灣大學 === 生物環境系統工程學研究所 === 105 === Flood forecasting is an essential issue in hydrological studies. In the literature, many flood forecasting models were shown to perform well. However, it has also been recognized that, due to flood flow persistence, even simple models could also achieve goo...

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Main Authors: Guang-Ying Shih, 石廣英
Other Authors: 鄭克聲
Format: Others
Language:zh-TW
Published: 2017
Online Access:http://ndltd.ncl.edu.tw/handle/cgmx54
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spelling ndltd-TW-105NTU054040192019-05-15T23:39:39Z http://ndltd.ncl.edu.tw/handle/cgmx54 Yilan River Flood Flow Persistent Analysis 宜蘭河洪水流量持續性分析 Guang-Ying Shih 石廣英 碩士 國立臺灣大學 生物環境系統工程學研究所 105 Flood forecasting is an essential issue in hydrological studies. In the literature, many flood forecasting models were shown to perform well. However, it has also been recognized that, due to flood flow persistence, even simple models could also achieve good performance. In this study, two model performance criteria, namely the coefficient of efficiency (CE) and coefficient of persistence (CP) were used to evaluate performance of flood forecasting models. Flood flow data at three stations in the Yilan River Basin were represented by autoregressive (AR) models. An asymptotic theoretical relationship between CE and CP, which is dependent on the lag-k autocorrelation coefficient, was derived and used to demonstrate why the simple naïve forecasting model could achieve good performance, in certain cases, even outperform more complicated models. 鄭克聲 2017 學位論文 ; thesis 44 zh-TW
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language zh-TW
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sources NDLTD
description 碩士 === 國立臺灣大學 === 生物環境系統工程學研究所 === 105 === Flood forecasting is an essential issue in hydrological studies. In the literature, many flood forecasting models were shown to perform well. However, it has also been recognized that, due to flood flow persistence, even simple models could also achieve good performance. In this study, two model performance criteria, namely the coefficient of efficiency (CE) and coefficient of persistence (CP) were used to evaluate performance of flood forecasting models. Flood flow data at three stations in the Yilan River Basin were represented by autoregressive (AR) models. An asymptotic theoretical relationship between CE and CP, which is dependent on the lag-k autocorrelation coefficient, was derived and used to demonstrate why the simple naïve forecasting model could achieve good performance, in certain cases, even outperform more complicated models.
author2 鄭克聲
author_facet 鄭克聲
Guang-Ying Shih
石廣英
author Guang-Ying Shih
石廣英
spellingShingle Guang-Ying Shih
石廣英
Yilan River Flood Flow Persistent Analysis
author_sort Guang-Ying Shih
title Yilan River Flood Flow Persistent Analysis
title_short Yilan River Flood Flow Persistent Analysis
title_full Yilan River Flood Flow Persistent Analysis
title_fullStr Yilan River Flood Flow Persistent Analysis
title_full_unstemmed Yilan River Flood Flow Persistent Analysis
title_sort yilan river flood flow persistent analysis
publishDate 2017
url http://ndltd.ncl.edu.tw/handle/cgmx54
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AT shíguǎngyīng yílánhéhóngshuǐliúliàngchíxùxìngfēnxī
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