以連續機率分佈熵及克利金建構雨量站網

Rainfall Network Design by Using Entropy with Probability Distribution Function and Kriging

陳茹蕙(Ru-Huei Chen)

指導教授 ： 陳彥璋

國立臺北科技大學/工程學院/土木與防災研究所/碩士(2007年)

https://doi.org/10.6841/NTUT.2007.00383

摘要

水文資訊為構成水資源規劃、水工結構物以及水利運轉之最基本要素。規劃良好的雨量站網可以提供代表該流域特性之精確且可靠的降雨資訊，影響雨量站網之設立通常包含地形、降雨型態、集水區特性、交通、人力及預算等因子，諸等因子將會影響到雨量站之位置與密度。過去雖投入大量人力、物力於集水區雨量站網設計之研究，但對於雨量站設站之位置以及所需設站的最小站數仍無法得到確切的答案。故有必要進一步研究集水區雨量設站之位置，以及可能反映該集水區之最少站數之問題。
本研究乃結合地理統計之克利金法以推估可能設站位置及其所能提供之降雨資訊，之後再藉由資訊熵以評估各測站之資訊量及不確定性，藉由此二方法之結合即可推估集水區飽和雨量站之數目及其位置。此法將可提供流域管理單位設置雨量站之依據，用以評估現有雨量站是否可提供所需之降雨資訊，若不足則需增站，若重複性太高則需減站，期望可用以評估或調整現有集水區之雨量站網。

關鍵字

連續機率分佈 ； 克利金法 ； 資訊熵 ； 雨量站網

並列摘要

Hydrological data are the basic ingredients for the planning, design and operation of water objects. A well designed hydrometeorological network can accurately represent and provide the information of rainfall in the catchment.
The proposed model is composed of kriging and entropy. The original kriging can be used to generate the rainfall data of the location that can be used to install rainfall station. Entropy based on probability can be used to measure uncertainty. In this study, the probability distribution functions will be introduced for fitting the statistical characteristics of the data of the raingage stations. By calculating the joint entropy and the transmitted information, the existing rain gage stations are prioritized. Therefore the maximum number of rainfall stations and the locations of the rainfall stations in the catchment can be obtained. In addition, the saturation of rainfall information can be used to establish or remove the rain gage stations.

並列關鍵字

Probability Distribution Function ； Original Kringing ； Entropy ； Rainfall network

參考文獻

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陳彥璋、衛強、葉惠中. (2006). "Rainfall network design using kringing and entropy." Hydrological Processes, 20.

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