【摘要】：河流的水位是掌握水文情況和推算其它水文要素的寶貴資料。對(duì)于水利部門來說精確地觀測(cè)水位對(duì)工程建設(shè)是可以減少很多損失,同時(shí)準(zhǔn)確地觀測(cè)水位對(duì)合理的利用水資源具有重大的意義。迄今為止,在測(cè)量河流水位中存在很多的問題,比如用水尺測(cè)量河流水位,水尺常年浸泡在河水中很容易被侵蝕,儀器的損壞會(huì)對(duì)測(cè)量結(jié)果產(chǎn)生很大的誤差,利用水尺測(cè)量水位工作量較大,并且人工測(cè)量也容易造成誤差;通過用精密設(shè)備測(cè)量河流水位,設(shè)備后期需要維護(hù)和保養(yǎng),成本較高等等。鑒于目前測(cè)量水位方法存在以上缺陷,同時(shí)在影響水位的眾多因素中,降雨量和流量的大小又是影響水位升降的重要因素。所以找到一個(gè)既能精確測(cè)量水位和又能使投入成本降低的方法很重要。因此本文首先基于一元線性回歸模型來研究降雨量與水位的之間的關(guān)系,以降雨量為自變量,水位為因變量,利用一元線性回歸模型通過降雨量預(yù)測(cè)出水位。其次基于多元線性回歸模型來研究降雨量、流量對(duì)水位的影響,以降雨量和流量作為自變量,水位作為因變量,利用二元線性回歸模型來預(yù)測(cè)出水位。本工作首先是從專業(yè)網(wǎng)站上如黃河網(wǎng)、中央氣象臺(tái)和河南雨量簡(jiǎn)明查詢系統(tǒng)等收集了2011年到2016年的歷史降雨量、流量與水位數(shù)據(jù)。并對(duì)數(shù)據(jù)進(jìn)行數(shù)據(jù)挖掘,逐年逐月逐日進(jìn)行分類整理,因?yàn)楸疚闹豢紤]正常降雨量和流量對(duì)水位的影響,所以在整理數(shù)據(jù)時(shí)將特殊狀況下的數(shù)據(jù)排除,比如在汛期發(fā)生特大暴雨時(shí),水壩對(duì)水位的調(diào)節(jié)排除;以及在干旱時(shí)期,降雨量偏少的情況排除。依據(jù)收集和整理的歷史數(shù)據(jù),對(duì)這些數(shù)據(jù)進(jìn)行數(shù)據(jù)挖掘分析,從其中找到潛在有用的信息和知識(shí)。將2011年到2016的降雨量和水位數(shù)據(jù)作為訓(xùn)練集,再將2014年到2016年的降雨量和水位數(shù)據(jù)作為測(cè)試集。利用訓(xùn)練集中的數(shù)據(jù)計(jì)算出線性回歸方程中的回歸參數(shù),并進(jìn)行回歸檢驗(yàn),對(duì)預(yù)測(cè)的結(jié)果進(jìn)行分析,將預(yù)測(cè)結(jié)果和實(shí)際觀測(cè)值進(jìn)行對(duì)比分析。再利用測(cè)試集中的數(shù)據(jù)檢驗(yàn)回歸方程的精確性。最終得出用線性回歸模型可以準(zhǔn)確地通過降雨量預(yù)測(cè)出水位的結(jié)論,而且該方法不僅可以降低測(cè)量水位的成本,而且還可以節(jié)省人力資源,提高測(cè)量水位的精確度。
[Abstract]:The water level of a river is a valuable data for mastering the hydrological situation and calculating other hydrological elements. For the water conservancy department, the accurate observation of water level can reduce a lot of losses to the construction of the project, and it is of great significance for the rational utilization of water resources to accurately observe the water level at the same time. Up to now, there are many problems in measuring river water level, such as measuring river water level with water meter, soaking in river water all year round is very easy to be eroded, the damage of instrument will cause great error to the result of measurement. It is difficult to measure water level by using water gauge, and it is easy to cause errors by manual measurement. Through the use of precision equipment to measure river water level, equipment later maintenance and maintenance, high cost and so on. In view of the above defects in the current method of measuring water level, among the many factors that affect the water level, the magnitude of rainfall and discharge are the important factors that affect the fluctuation of water level. So it is important to find a method that can measure water level accurately and reduce input cost. In this paper, the relationship between rainfall and water level is studied based on the univariate linear regression model. With rainfall as independent variable and water level as dependent variable, the water level is predicted by single linear regression model. Secondly, based on the multivariate linear regression model, the influence of rainfall and discharge on the water level is studied. The rainfall and discharge are taken as independent variables and the water level is taken as dependent variable, and the binary linear regression model is used to predict the water level. This work is to collect the historical rainfall, discharge and water level data from 2011 to 2016 from professional websites such as Yellow River net, Central Meteorological Station and Henan rainfall Concise query system. The data are mined and classified every month and day by year. Because the influence of normal rainfall and discharge on water level is only considered in this paper, the data under special conditions are excluded when sorting out the data. For example, in the flood season when the heavy rain occurred, the dam to adjust the water level exclusion; And in times of drought, less rainfall is excluded. According to the historical data collected and collated, the data are mined and analyzed to find the potentially useful information and knowledge. Rainfall and water level data from 2011 to 2016 are used as training set, and rainfall and water level data from 2014 to 2016 are used as test data. The regression parameters in the linear regression equation are calculated by using the data of the training set, and the regression test is carried out. The predicted results are analyzed, and the predicted results are compared with the observed values. The accuracy of the regression equation is verified by the data of the test set. Finally, it is concluded that the water level can be accurately predicted by rainfall with linear regression model, and this method can not only reduce the cost of measuring water level, but also save human resources and improve the accuracy of water level measurement.
【學(xué)位授予單位】：河南師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：P332

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