Forecasting of gage height during high water using weather radar and neural network

碩士 === 國立臺北科技大學 === 土木工程系土木與防災碩士班 === 107 === The rainfall data which is measured from the rainfall station network in the catchment area from the QPESUMS of the Central Meteorological Administration is used to evaluate the quantitative precipitation patterns applicable to the catchment area of the...

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Main Authors:	KUO,YIN-YU, 郭盈妤
Other Authors:	CHEN,YEN-CHANG
Format:	Others
Language:	zh-TW
Published:	2019
Online Access:	http://ndltd.ncl.edu.tw/handle/jbk57s

id	ndltd-TW-107TIT00653087
record_format	oai_dc
spelling	ndltd-TW-107TIT006530872019-11-14T05:36:36Z http://ndltd.ncl.edu.tw/handle/jbk57s Forecasting of gage height during high water using weather radar and neural network 以氣象雷達及類神經網路預測高水位時期之河川水位 KUO,YIN-YU 郭盈妤碩士國立臺北科技大學土木工程系土木與防災碩士班 107 The rainfall data which is measured from the rainfall station network in the catchment area from the QPESUMS of the Central Meteorological Administration is used to evaluate the quantitative precipitation patterns applicable to the catchment area of the Feitsui Reservoir of the Beishi River area , and using this model to forecasting the water level for the next 3 hours in the future. This study will use the rainfall data from these observation grids to reqression of the forecasted rainfall for the next hour. Using the rainfall and branch flow data in the grid, the rainfall-runoff model is constructed with a neural network to estimate the main river water level and inflow of the future tributary to facilitate the operation of the reservoir and reduce the flood and shortage of the reservoir downstream. CHEN,YEN-CHANG 陳彥璋 2019 學位論文 ; thesis 77 zh-TW
collection	NDLTD
language	zh-TW
format	Others
sources	NDLTD
description	碩士 === 國立臺北科技大學 === 土木工程系土木與防災碩士班 === 107 === The rainfall data which is measured from the rainfall station network in the catchment area from the QPESUMS of the Central Meteorological Administration is used to evaluate the quantitative precipitation patterns applicable to the catchment area of the Feitsui Reservoir of the Beishi River area , and using this model to forecasting the water level for the next 3 hours in the future. This study will use the rainfall data from these observation grids to reqression of the forecasted rainfall for the next hour. Using the rainfall and branch flow data in the grid, the rainfall-runoff model is constructed with a neural network to estimate the main river water level and inflow of the future tributary to facilitate the operation of the reservoir and reduce the flood and shortage of the reservoir downstream.
author2	CHEN,YEN-CHANG
author_facet	CHEN,YEN-CHANG KUO,YIN-YU 郭盈妤
author	KUO,YIN-YU 郭盈妤
spellingShingle	KUO,YIN-YU 郭盈妤 Forecasting of gage height during high water using weather radar and neural network
author_sort	KUO,YIN-YU
title	Forecasting of gage height during high water using weather radar and neural network
title_short	Forecasting of gage height during high water using weather radar and neural network
title_full	Forecasting of gage height during high water using weather radar and neural network
title_fullStr	Forecasting of gage height during high water using weather radar and neural network
title_full_unstemmed	Forecasting of gage height during high water using weather radar and neural network
title_sort	forecasting of gage height during high water using weather radar and neural network
publishDate	2019
url	http://ndltd.ncl.edu.tw/handle/jbk57s
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Forecasting of gage height during high water using weather radar and neural network

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